id	sid	tid	token	lemma	pos
cana-1019	1	1	communications	communication	NOUN
cana-1019	1	2	on	on	ADP
cana-1019	1	3	applied	apply	VERB
cana-1019	1	4	nonlinear	nonlinear	ADJ
cana-1019	1	5	analysis	analysis	NOUN
cana-1019	1	6	issn	issn	NOUN
cana-1019	1	7	:	:	PUNCT
cana-1019	1	8	1074	1074	NUM
cana-1019	1	9	-	-	PUNCT
cana-1019	1	10	133x	133x	NUM
cana-1019	1	11	vol	vol	NOUN
cana-1019	1	12	31	31	NUM
cana-1019	1	13	no	no	NOUN
cana-1019	1	14	.	.	PUNCT
cana-1019	2	1	5s	5s	NUM
cana-1019	2	2	(	(	PUNCT
cana-1019	2	3	2024	2024	NUM
cana-1019	2	4	)	)	PUNCT
cana-1019	2	5	234	234	NUM
cana-1019	2	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1019	2	7	enhancing	enhance	VERB
cana-1019	2	8	accuracy	accuracy	NOUN
cana-1019	2	9	and	and	CCONJ
cana-1019	2	10	performance	performance	NOUN
cana-1019	2	11	in	in	ADP
cana-1019	2	12	music	music	NOUN
cana-1019	2	13	mood	mood	NOUN
cana-1019	2	14	classification	classification	NOUN
cana-1019	2	15	through	through	ADP
cana-1019	2	16	fine	fine	ADV
cana-1019	2	17	-	-	PUNCT
cana-1019	2	18	tuned	tune	VERB
cana-1019	2	19	machine	machine	NOUN
cana-1019	2	20	learning	learning	NOUN
cana-1019	2	21	methods	method	NOUN
cana-1019	2	22	shital	shital	PROPN
cana-1019	2	23	shankar	shankar	PROPN
cana-1019	2	24	gujar1	gujar1	PROPN
cana-1019	2	25	,	,	PUNCT
cana-1019	2	26	dr	dr	PROPN
cana-1019	2	27	.	.	PROPN
cana-1019	2	28	ali	ali	PROPN
cana-1019	2	29	yawar	yawar	PROPN
cana-1019	2	30	reha2	reha2	PROPN
cana-1019	2	31	1computer	1computer	NUM
cana-1019	2	32	engineering	engineering	NOUN
cana-1019	2	33	,	,	PUNCT
cana-1019	2	34	pacific	pacific	PROPN
cana-1019	2	35	university	university	PROPN
cana-1019	2	36	,	,	PUNCT
cana-1019	2	37	udaipur	udaipur	NOUN
cana-1019	2	38	,	,	PUNCT
cana-1019	2	39	sgujar03@gmail.com	sgujar03@gmail.com	X
cana-1019	2	40	2management	2management	NUM
cana-1019	2	41	,	,	PUNCT
cana-1019	2	42	asst	asst	NOUN
cana-1019	2	43	.	.	PUNCT
cana-1019	3	1	prof	prof	PROPN
cana-1019	3	2	,	,	PUNCT
cana-1019	3	3	paher	paher	PROPN
cana-1019	3	4	university	university	NOUN
cana-1019	3	5	,	,	PUNCT
cana-1019	3	6	udaipur	udaipur	NOUN
cana-1019	3	7	,	,	PUNCT
cana-1019	3	8	ay_reha@yahoo.com	ay_reha@yahoo.com	X
cana-1019	3	9	article	article	NOUN
cana-1019	3	10	history	history	NOUN
cana-1019	3	11	:	:	PUNCT
cana-1019	3	12	received	receive	VERB
cana-1019	3	13	:	:	PUNCT
cana-1019	3	14	08	08	NUM
cana-1019	3	15	-	-	PUNCT
cana-1019	3	16	05	05	NUM
cana-1019	3	17	-	-	PUNCT
cana-1019	3	18	2024	2024	NUM
cana-1019	3	19	revised	revise	VERB
cana-1019	3	20	:	:	PUNCT
cana-1019	3	21	24	24	NUM
cana-1019	3	22	-	-	PUNCT
cana-1019	3	23	06	06	NUM
cana-1019	3	24	-	-	PUNCT
cana-1019	3	25	2024	2024	NUM
cana-1019	3	26	accepted	accept	VERB
cana-1019	3	27	:	:	PUNCT
cana-1019	3	28	06	06	NUM
cana-1019	3	29	-	-	SYM
cana-1019	3	30	07	07	NUM
cana-1019	3	31	-	-	PUNCT
cana-1019	3	32	2024	2024	NUM
cana-1019	3	33	abstract	abstract	NOUN
cana-1019	3	34	putting	put	VERB
cana-1019	3	35	emotional	emotional	ADJ
cana-1019	3	36	labels	label	NOUN
cana-1019	3	37	on	on	ADP
cana-1019	3	38	music	music	NOUN
cana-1019	3	39	,	,	PUNCT
cana-1019	3	40	or	or	CCONJ
cana-1019	3	41	"	"	PUNCT
cana-1019	3	42	music	music	NOUN
cana-1019	3	43	mood	mood	NOUN
cana-1019	3	44	classification	classification	NOUN
cana-1019	3	45	,	,	PUNCT
cana-1019	3	46	"	"	PUNCT
cana-1019	3	47	is	be	AUX
cana-1019	3	48	important	important	ADJ
cana-1019	3	49	for	for	ADP
cana-1019	3	50	use	use	NOUN
cana-1019	3	51	in	in	ADP
cana-1019	3	52	recommendation	recommendation	NOUN
cana-1019	3	53	systems	system	NOUN
cana-1019	3	54	and	and	CCONJ
cana-1019	3	55	music	music	NOUN
cana-1019	3	56	therapy	therapy	NOUN
cana-1019	3	57	.	.	PUNCT
cana-1019	4	1	using	use	VERB
cana-1019	4	2	fine	fine	ADV
cana-1019	4	3	-	-	PUNCT
cana-1019	4	4	tuned	tune	VERB
cana-1019	4	5	machine	machine	NOUN
cana-1019	4	6	learning	learning	NOUN
cana-1019	4	7	methods	method	NOUN
cana-1019	4	8	,	,	PUNCT
cana-1019	4	9	this	this	DET
cana-1019	4	10	study	study	NOUN
cana-1019	4	11	aims	aim	VERB
cana-1019	4	12	to	to	PART
cana-1019	4	13	improve	improve	VERB
cana-1019	4	14	the	the	DET
cana-1019	4	15	accuracy	accuracy	NOUN
cana-1019	4	16	and	and	CCONJ
cana-1019	4	17	performance	performance	NOUN
cana-1019	4	18	of	of	ADP
cana-1019	4	19	classification	classification	NOUN
cana-1019	4	20	.	.	PUNCT
cana-1019	5	1	we	we	PRON
cana-1019	5	2	used	use	VERB
cana-1019	5	3	a	a	DET
cana-1019	5	4	large	large	ADJ
cana-1019	5	5	dataset	dataset	NOUN
cana-1019	5	6	with	with	ADP
cana-1019	5	7	names	name	NOUN
cana-1019	5	8	for	for	ADP
cana-1019	5	9	different	different	ADJ
cana-1019	5	10	types	type	NOUN
cana-1019	5	11	of	of	ADP
cana-1019	5	12	music	music	NOUN
cana-1019	5	13	and	and	CCONJ
cana-1019	5	14	moods	mood	NOUN
cana-1019	5	15	to	to	PART
cana-1019	5	16	make	make	VERB
cana-1019	5	17	sure	sure	ADJ
cana-1019	5	18	that	that	SCONJ
cana-1019	5	19	the	the	DET
cana-1019	5	20	model	model	NOUN
cana-1019	5	21	training	training	NOUN
cana-1019	5	22	was	be	AUX
cana-1019	5	23	strong	strong	ADJ
cana-1019	5	24	.	.	PUNCT
cana-1019	6	1	advanced	advanced	ADJ
cana-1019	6	2	feature	feature	NOUN
cana-1019	6	3	extraction	extraction	NOUN
cana-1019	6	4	methods	method	NOUN
cana-1019	6	5	picked	pick	VERB
cana-1019	6	6	up	up	ADP
cana-1019	6	7	both	both	CCONJ
cana-1019	6	8	the	the	DET
cana-1019	6	9	traits	trait	NOUN
cana-1019	6	10	of	of	ADP
cana-1019	6	11	the	the	DET
cana-1019	6	12	audio	audio	ADJ
cana-1019	6	13	stream	stream	NOUN
cana-1019	6	14	and	and	CCONJ
cana-1019	6	15	the	the	DET
cana-1019	6	16	lyrics	lyric	NOUN
cana-1019	6	17	.	.	PUNCT
cana-1019	7	1	for	for	ADP
cana-1019	7	2	audio	audio	ADJ
cana-1019	7	3	features	feature	NOUN
cana-1019	7	4	,	,	PUNCT
cana-1019	7	5	color	color	NOUN
cana-1019	7	6	features	feature	NOUN
cana-1019	7	7	,	,	PUNCT
cana-1019	7	8	spectral	spectral	ADJ
cana-1019	7	9	contrast	contrast	NOUN
cana-1019	7	10	,	,	PUNCT
cana-1019	7	11	and	and	CCONJ
cana-1019	7	12	mel	mel	PROPN
cana-1019	7	13	-	-	PUNCT
cana-1019	7	14	frequency	frequency	ADJ
cana-1019	7	15	cepstral	cepstral	ADJ
cana-1019	7	16	coefficients	coefficient	NOUN
cana-1019	7	17	(	(	PUNCT
cana-1019	7	18	mfccs	mfccs	PROPN
cana-1019	7	19	)	)	PUNCT
cana-1019	7	20	were	be	AUX
cana-1019	7	21	recovered	recover	VERB
cana-1019	7	22	.	.	PUNCT
cana-1019	8	1	for	for	ADP
cana-1019	8	2	poetry	poetry	NOUN
cana-1019	8	3	analysis	analysis	NOUN
cana-1019	8	4	,	,	PUNCT
cana-1019	8	5	tf	tf	PROPN
cana-1019	8	6	-	-	PUNCT
cana-1019	8	7	idf	idf	NOUN
cana-1019	8	8	and	and	CCONJ
cana-1019	8	9	word	word	NOUN
cana-1019	8	10	embeddings	embedding	NOUN
cana-1019	8	11	were	be	AUX
cana-1019	8	12	used	use	VERB
cana-1019	8	13	,	,	PUNCT
cana-1019	8	14	along	along	ADP
cana-1019	8	15	with	with	ADP
cana-1019	8	16	natural	natural	ADJ
cana-1019	8	17	language	language	NOUN
cana-1019	8	18	processing	processing	NOUN
cana-1019	8	19	(	(	PUNCT
cana-1019	8	20	nlp	nlp	NOUN
cana-1019	8	21	)	)	PUNCT
cana-1019	8	22	methods	method	NOUN
cana-1019	8	23	.	.	PUNCT
cana-1019	9	1	logistic	logistic	ADJ
cana-1019	9	2	regression	regression	NOUN
cana-1019	9	3	,	,	PUNCT
cana-1019	9	4	sgd	sgd	PROPN
cana-1019	9	5	classifier	classifier	PROPN
cana-1019	9	6	,	,	PUNCT
cana-1019	9	7	gaussian	gaussian	ADJ
cana-1019	9	8	naive	naive	ADJ
cana-1019	9	9	bayes	bayes	NOUN
cana-1019	9	10	,	,	PUNCT
cana-1019	9	11	decision	decision	NOUN
cana-1019	9	12	tree	tree	NOUN
cana-1019	9	13	,	,	PUNCT
cana-1019	9	14	random	random	ADJ
cana-1019	9	15	forest	forest	NOUN
cana-1019	9	16	,	,	PUNCT
cana-1019	9	17	xgb	xgb	PROPN
cana-1019	9	18	classifier	classifier	NOUN
cana-1019	9	19	,	,	PUNCT
cana-1019	9	20	svm	svm	PROPN
cana-1019	9	21	linear	linear	NOUN
cana-1019	9	22	,	,	PUNCT
cana-1019	9	23	and	and	CCONJ
cana-1019	9	24	k	k	X
cana-1019	9	25	-	-	PUNCT
cana-1019	9	26	nearest	near	ADJ
cana-1019	9	27	neighbors	neighbor	NOUN
cana-1019	9	28	(	(	PUNCT
cana-1019	9	29	knn	knn	PROPN
cana-1019	9	30	)	)	PUNCT
cana-1019	9	31	were	be	AUX
cana-1019	9	32	some	some	PRON
cana-1019	9	33	of	of	ADP
cana-1019	9	34	the	the	DET
cana-1019	9	35	machine	machine	NOUN
cana-1019	9	36	learning	learn	VERB
cana-1019	9	37	classification	classification	NOUN
cana-1019	9	38	methods	method	NOUN
cana-1019	9	39	we	we	PRON
cana-1019	9	40	used	use	VERB
cana-1019	9	41	.	.	PUNCT
cana-1019	10	1	random	random	ADJ
cana-1019	10	2	forest	forest	NOUN
cana-1019	10	3	,	,	PUNCT
cana-1019	10	4	xgb	xgb	PROPN
cana-1019	10	5	classifier	classifier	NOUN
cana-1019	10	6	,	,	PUNCT
cana-1019	10	7	and	and	CCONJ
cana-1019	10	8	svm	svm	ADJ
cana-1019	10	9	linear	linear	PROPN
cana-1019	10	10	all	all	PRON
cana-1019	10	11	did	do	VERB
cana-1019	10	12	better	well	ADJ
cana-1019	10	13	than	than	ADP
cana-1019	10	14	the	the	DET
cana-1019	10	15	others	other	NOUN
cana-1019	10	16	.	.	PUNCT
cana-1019	11	1	we	we	PRON
cana-1019	11	2	used	use	VERB
cana-1019	11	3	grid	grid	NOUN
cana-1019	11	4	search	search	NOUN
cana-1019	11	5	and	and	CCONJ
cana-1019	11	6	random	random	ADJ
cana-1019	11	7	search	search	NOUN
cana-1019	11	8	to	to	PART
cana-1019	11	9	finetune	finetune	VERB
cana-1019	11	10	the	the	DET
cana-1019	11	11	hyperparameters	hyperparameter	NOUN
cana-1019	11	12	of	of	ADP
cana-1019	11	13	these	these	DET
cana-1019	11	14	top	top	ADV
cana-1019	11	15	-	-	PUNCT
cana-1019	11	16	performing	perform	VERB
cana-1019	11	17	models	model	NOUN
cana-1019	11	18	in	in	ADP
cana-1019	11	19	order	order	NOUN
cana-1019	11	20	to	to	PART
cana-1019	11	21	make	make	VERB
cana-1019	11	22	them	they	PRON
cana-1019	11	23	even	even	ADV
cana-1019	11	24	better	well	ADV
cana-1019	11	25	.	.	PUNCT
cana-1019	12	1	cross	cross	ADJ
cana-1019	12	2	-	-	ADJ
cana-1019	12	3	validation	validation	ADJ
cana-1019	12	4	made	make	VERB
cana-1019	12	5	sure	sure	ADJ
cana-1019	12	6	that	that	SCONJ
cana-1019	12	7	the	the	DET
cana-1019	12	8	models	model	NOUN
cana-1019	12	9	were	be	AUX
cana-1019	12	10	stable	stable	ADJ
cana-1019	12	11	and	and	CCONJ
cana-1019	12	12	could	could	AUX
cana-1019	12	13	be	be	AUX
cana-1019	12	14	used	use	VERB
cana-1019	12	15	in	in	ADP
cana-1019	12	16	other	other	ADJ
cana-1019	12	17	situations	situation	NOUN
cana-1019	12	18	.	.	PUNCT
cana-1019	13	1	our	our	PRON
cana-1019	13	2	results	result	NOUN
cana-1019	13	3	show	show	VERB
cana-1019	13	4	that	that	SCONJ
cana-1019	13	5	the	the	DET
cana-1019	13	6	highly	highly	ADV
cana-1019	13	7	tuned	tune	VERB
cana-1019	13	8	random	random	ADJ
cana-1019	13	9	forest	forest	NOUN
cana-1019	13	10	,	,	PUNCT
cana-1019	13	11	xgb	xgb	PROPN
cana-1019	13	12	,	,	PUNCT
cana-1019	13	13	and	and	CCONJ
cana-1019	13	14	svm	svm	ADJ
cana-1019	13	15	models	model	NOUN
cana-1019	13	16	greatly	greatly	ADV
cana-1019	13	17	improved	improve	VERB
cana-1019	13	18	the	the	DET
cana-1019	13	19	accuracy	accuracy	NOUN
cana-1019	13	20	of	of	ADP
cana-1019	13	21	classification	classification	NOUN
cana-1019	13	22	,	,	PUNCT
cana-1019	13	23	with	with	ADP
cana-1019	13	24	the	the	DET
cana-1019	13	25	xgb	xgb	NOUN
cana-1019	13	26	classifier	classifier	NOUN
cana-1019	13	27	performing	perform	VERB
cana-1019	13	28	the	the	DET
cana-1019	13	29	best	good	ADJ
cana-1019	13	30	.	.	PUNCT
cana-1019	14	1	this	this	DET
cana-1019	14	2	study	study	NOUN
cana-1019	14	3	adds	add	VERB
cana-1019	14	4	to	to	ADP
cana-1019	14	5	music	music	NOUN
cana-1019	14	6	information	information	NOUN
cana-1019	14	7	retrieval	retrieval	NOUN
cana-1019	14	8	by	by	ADP
cana-1019	14	9	creating	create	VERB
cana-1019	14	10	a	a	DET
cana-1019	14	11	useful	useful	ADJ
cana-1019	14	12	method	method	NOUN
cana-1019	14	13	for	for	ADP
cana-1019	14	14	mood	mood	NOUN
cana-1019	14	15	classification	classification	NOUN
cana-1019	14	16	that	that	PRON
cana-1019	14	17	can	can	AUX
cana-1019	14	18	be	be	AUX
cana-1019	14	19	used	use	VERB
cana-1019	14	20	in	in	ADP
cana-1019	14	21	real	real	ADJ
cana-1019	14	22	-	-	PUNCT
cana-1019	14	23	life	life	NOUN
cana-1019	14	24	situations	situation	NOUN
cana-1019	14	25	to	to	PART
cana-1019	14	26	improve	improve	VERB
cana-1019	14	27	user	user	NOUN
cana-1019	14	28	experiences	experience	NOUN
cana-1019	14	29	and	and	CCONJ
cana-1019	14	30	create	create	VERB
cana-1019	14	31	more	more	ADJ
cana-1019	14	32	personalized	personalized	ADJ
cana-1019	14	33	music	music	NOUN
cana-1019	14	34	services	service	NOUN
cana-1019	14	35	.	.	PUNCT
cana-1019	15	1	keywords	keyword	NOUN
cana-1019	15	2	:	:	PUNCT
cana-1019	15	3	music	music	NOUN
cana-1019	15	4	mood	mood	NOUN
cana-1019	15	5	classification	classification	NOUN
cana-1019	15	6	,	,	PUNCT
cana-1019	15	7	machine	machine	NOUN
cana-1019	15	8	learning	learning	NOUN
cana-1019	15	9	,	,	PUNCT
cana-1019	15	10	feature	feature	NOUN
cana-1019	15	11	extraction	extraction	NOUN
cana-1019	15	12	,	,	PUNCT
cana-1019	15	13	hyperparameter	hyperparameter	NOUN
cana-1019	15	14	optimization	optimization	NOUN
cana-1019	15	15	,	,	PUNCT
cana-1019	15	16	random	random	ADJ
cana-1019	15	17	forest	forest	NOUN
cana-1019	15	18	,	,	PUNCT
cana-1019	15	19	xgb	xgb	PROPN
cana-1019	15	20	classifier	classifier	NOUN
cana-1019	15	21	,	,	PUNCT
cana-1019	15	22	svm	svm	ADJ
cana-1019	15	23	,	,	PUNCT
cana-1019	15	24	music	music	NOUN
cana-1019	15	25	information	information	NOUN
cana-1019	15	26	retrieval	retrieval	NOUN
cana-1019	15	27	.	.	PUNCT
cana-1019	16	1	1	1	X
cana-1019	16	2	.	.	X
cana-1019	16	3	introduction	introduction	NOUN
cana-1019	16	4	the	the	DET
cana-1019	16	5	study	study	NOUN
cana-1019	16	6	of	of	ADP
cana-1019	16	7	music	music	NOUN
cana-1019	16	8	mood	mood	NOUN
cana-1019	16	9	classification	classification	NOUN
cana-1019	16	10	is	be	AUX
cana-1019	16	11	both	both	CCONJ
cana-1019	16	12	complicated	complicated	ADJ
cana-1019	16	13	and	and	CCONJ
cana-1019	16	14	interesting	interesting	ADJ
cana-1019	16	15	.	.	PUNCT
cana-1019	17	1	it	it	PRON
cana-1019	17	2	combines	combine	VERB
cana-1019	17	3	aspects	aspect	NOUN
cana-1019	17	4	of	of	ADP
cana-1019	17	5	musicology	musicology	NOUN
cana-1019	17	6	,	,	PUNCT
cana-1019	17	7	psychology	psychology	NOUN
cana-1019	17	8	,	,	PUNCT
cana-1019	17	9	and	and	CCONJ
cana-1019	17	10	computer	computer	NOUN
cana-1019	17	11	science	science	NOUN
cana-1019	17	12	.	.	PUNCT
cana-1019	18	1	the	the	DET
cana-1019	18	2	main	main	ADJ
cana-1019	18	3	goal	goal	NOUN
cana-1019	18	4	is	be	AUX
cana-1019	18	5	to	to	PART
cana-1019	18	6	correctly	correctly	ADV
cana-1019	18	7	give	give	VERB
cana-1019	18	8	emotional	emotional	ADJ
cana-1019	18	9	tags	tag	NOUN
cana-1019	18	10	to	to	ADP
cana-1019	18	11	songs	song	NOUN
cana-1019	18	12	so	so	SCONJ
cana-1019	18	13	that	that	SCONJ
cana-1019	18	14	users	user	NOUN
cana-1019	18	15	can	can	AUX
cana-1019	18	16	have	have	VERB
cana-1019	18	17	more	more	ADV
cana-1019	18	18	unique	unique	ADJ
cana-1019	18	19	and	and	CCONJ
cana-1019	18	20	relevant	relevant	ADJ
cana-1019	18	21	music	music	NOUN
cana-1019	18	22	experiences	experience	NOUN
cana-1019	18	23	.	.	PUNCT
cana-1019	19	1	this	this	DET
cana-1019	19	2	feature	feature	NOUN
cana-1019	19	3	is	be	AUX
cana-1019	19	4	very	very	ADV
cana-1019	19	5	useful	useful	ADJ
cana-1019	19	6	for	for	ADP
cana-1019	19	7	many	many	ADJ
cana-1019	19	8	things	thing	NOUN
cana-1019	19	9	,	,	PUNCT
cana-1019	19	10	like	like	ADP
cana-1019	19	11	song	song	NOUN
cana-1019	19	12	suggestion	suggestion	NOUN
cana-1019	19	13	systems	system	NOUN
cana-1019	19	14	,	,	PUNCT
cana-1019	19	15	mood	mood	NOUN
cana-1019	19	16	-	-	PUNCT
cana-1019	19	17	based	base	VERB
cana-1019	19	18	tracks	track	NOUN
cana-1019	19	19	,	,	PUNCT
cana-1019	19	20	and	and	CCONJ
cana-1019	19	21	healing	healing	NOUN
cana-1019	19	22	settings	setting	NOUN
cana-1019	19	23	.	.	PUNCT
cana-1019	20	1	the	the	DET
cana-1019	20	2	need	need	NOUN
cana-1019	20	3	for	for	ADP
cana-1019	20	4	automatic	automatic	ADJ
cana-1019	20	5	and	and	CCONJ
cana-1019	20	6	accurate	accurate	ADJ
cana-1019	20	7	mood	mood	NOUN
cana-1019	20	8	classification	classification	NOUN
cana-1019	20	9	systems	system	NOUN
cana-1019	20	10	grows	grow	VERB
cana-1019	20	11	as	as	ADP
cana-1019	20	12	the	the	DET
cana-1019	20	13	amount	amount	NOUN
cana-1019	20	14	of	of	ADP
cana-1019	20	15	digital	digital	ADJ
cana-1019	20	16	music	music	NOUN
cana-1019	20	17	keeps	keep	VERB
cana-1019	20	18	growing	grow	VERB
cana-1019	20	19	at	at	ADP
cana-1019	20	20	an	an	DET
cana-1019	20	21	exponential	exponential	ADJ
cana-1019	20	22	rate	rate	NOUN
cana-1019	20	23	.	.	PUNCT
cana-1019	21	1	music	music	NOUN
cana-1019	21	2	mood	mood	NOUN
cana-1019	21	3	classification	classification	NOUN
cana-1019	21	4	has	have	AUX
cana-1019	21	5	relied	rely	VERB
cana-1019	21	6	on	on	ADP
cana-1019	21	7	simple	simple	ADJ
cana-1019	21	8	heuristics	heuristic	NOUN
cana-1019	21	9	and	and	CCONJ
cana-1019	21	10	hand	hand	NOUN
cana-1019	21	11	tagging	tagging	NOUN
cana-1019	21	12	,	,	PUNCT
cana-1019	21	13	which	which	PRON
cana-1019	21	14	take	take	VERB
cana-1019	21	15	a	a	DET
cana-1019	21	16	lot	lot	NOUN
cana-1019	21	17	of	of	ADP
cana-1019	21	18	time	time	NOUN
cana-1019	21	19	and	and	CCONJ
cana-1019	21	20	are	be	AUX
cana-1019	21	21	open	open	ADJ
cana-1019	21	22	to	to	ADP
cana-1019	21	23	personal	personal	ADJ
cana-1019	21	24	bias	bias	NOUN
cana-1019	21	25	.	.	PUNCT
cana-1019	22	1	machine	machine	NOUN
cana-1019	22	2	learning	learning	NOUN
cana-1019	22	3	has	have	AUX
cana-1019	22	4	completely	completely	ADV
cana-1019	22	5	changed	change	VERB
cana-1019	22	6	this	this	DET
cana-1019	22	7	area	area	NOUN
cana-1019	22	8	by	by	ADP
cana-1019	22	9	providing	provide	VERB
cana-1019	22	10	strong	strong	ADJ
cana-1019	22	11	tools	tool	NOUN
cana-1019	22	12	and	and	CCONJ
cana-1019	22	13	methods	method	NOUN
cana-1019	22	14	that	that	PRON
cana-1019	22	15	can	can	AUX
cana-1019	22	16	deal	deal	VERB
cana-1019	22	17	with	with	ADP
cana-1019	22	18	the	the	DET
cana-1019	22	19	complexity	complexity	NOUN
cana-1019	22	20	and	and	CCONJ
cana-1019	22	21	variability	variability	NOUN
cana-1019	22	22	of	of	ADP
cana-1019	22	23	sound	sound	ADJ
cana-1019	22	24	data	datum	NOUN
cana-1019	22	25	.	.	PUNCT
cana-1019	23	1	big	big	ADJ
cana-1019	23	2	datasets	dataset	NOUN
cana-1019	23	3	can	can	AUX
cana-1019	23	4	teach	teach	VERB
cana-1019	23	5	machine	machine	NOUN
cana-1019	23	6	learning	learning	NOUN
cana-1019	23	7	models	model	NOUN
cana-1019	23	8	new	new	ADJ
cana-1019	23	9	things	thing	NOUN
cana-1019	23	10	,	,	PUNCT
cana-1019	23	11	and	and	CCONJ
cana-1019	23	12	these	these	DET
cana-1019	23	13	models	model	NOUN
cana-1019	23	14	can	can	AUX
cana-1019	23	15	find	find	VERB
cana-1019	23	16	connections	connection	NOUN
cana-1019	23	17	and	and	CCONJ
cana-1019	23	18	trends	trend	NOUN
cana-1019	23	19	that	that	PRON
cana-1019	23	20	humans	human	NOUN
cana-1019	23	21	often	often	ADV
cana-1019	23	22	miss	miss	VERB
cana-1019	23	23	[	[	X
cana-1019	23	24	1	1	NUM
cana-1019	23	25	]	]	PUNCT
cana-1019	23	26	.	.	PUNCT
cana-1019	24	1	the	the	DET
cana-1019	24	2	move	move	NOUN
cana-1019	24	3	toward	toward	ADP
cana-1019	24	4	data	data	NOUN
cana-1019	24	5	-	-	PUNCT
cana-1019	24	6	driven	drive	VERB
cana-1019	24	7	communications	communication	NOUN
cana-1019	24	8	on	on	ADP
cana-1019	24	9	applied	apply	VERB
cana-1019	24	10	nonlinear	nonlinear	ADJ
cana-1019	24	11	analysis	analysis	NOUN
cana-1019	24	12	issn	issn	NOUN
cana-1019	24	13	:	:	PUNCT
cana-1019	24	14	1074	1074	NUM
cana-1019	24	15	-	-	PUNCT
cana-1019	24	16	133x	133x	NUM
cana-1019	24	17	vol	vol	NOUN
cana-1019	24	18	31	31	NUM
cana-1019	24	19	no	no	NOUN
cana-1019	24	20	.	.	PUNCT
cana-1019	25	1	5s	5s	NUM
cana-1019	25	2	(	(	PUNCT
cana-1019	25	3	2024	2024	NUM
cana-1019	25	4	)	)	PUNCT
cana-1019	25	5	235	235	NUM
cana-1019	25	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1019	25	7	methods	method	NOUN
cana-1019	25	8	has	have	AUX
cana-1019	25	9	made	make	VERB
cana-1019	25	10	music	music	NOUN
cana-1019	25	11	mood	mood	NOUN
cana-1019	25	12	labeling	labeling	NOUN
cana-1019	25	13	tools	tool	NOUN
cana-1019	25	14	much	much	ADV
cana-1019	25	15	more	more	ADV
cana-1019	25	16	accurate	accurate	ADJ
cana-1019	25	17	and	and	CCONJ
cana-1019	25	18	scalable	scalable	ADJ
cana-1019	25	19	.	.	PUNCT
cana-1019	26	1	one	one	NUM
cana-1019	26	2	important	important	ADJ
cana-1019	26	3	part	part	NOUN
cana-1019	26	4	of	of	ADP
cana-1019	26	5	accurately	accurately	ADV
cana-1019	26	6	classifying	classify	VERB
cana-1019	26	7	music	music	NOUN
cana-1019	26	8	mood	mood	NOUN
cana-1019	26	9	is	be	AUX
cana-1019	26	10	extracting	extract	VERB
cana-1019	26	11	features	feature	NOUN
cana-1019	26	12	.	.	PUNCT
cana-1019	27	1	as	as	ADP
cana-1019	27	2	a	a	DET
cana-1019	27	3	tool	tool	NOUN
cana-1019	27	4	,	,	PUNCT
cana-1019	27	5	music	music	NOUN
cana-1019	27	6	has	have	VERB
cana-1019	27	7	many	many	ADJ
cana-1019	27	8	uses	use	NOUN
cana-1019	27	9	.	.	PUNCT
cana-1019	28	1	it	it	PRON
cana-1019	28	2	includes	include	VERB
cana-1019	28	3	both	both	DET
cana-1019	28	4	sound	sound	ADJ
cana-1019	28	5	messages	message	NOUN
cana-1019	28	6	and	and	CCONJ
cana-1019	28	7	lyrics	lyric	NOUN
cana-1019	28	8	.	.	PUNCT
cana-1019	29	1	things	thing	NOUN
cana-1019	29	2	in	in	ADP
cana-1019	29	3	sound	sound	NOUN
cana-1019	29	4	like	like	ADP
cana-1019	29	5	mel	mel	NOUN
cana-1019	29	6	-	-	PUNCT
cana-1019	29	7	frequency	frequency	ADJ
cana-1019	29	8	cepstral	cepstral	ADJ
cana-1019	29	9	coefficients	coefficient	NOUN
cana-1019	29	10	(	(	PUNCT
cana-1019	29	11	mfccs	mfccs	NOUN
cana-1019	29	12	)	)	PUNCT
cana-1019	29	13	,	,	PUNCT
cana-1019	29	14	color	color	NOUN
cana-1019	29	15	features	feature	NOUN
cana-1019	29	16	,	,	PUNCT
cana-1019	29	17	and	and	CCONJ
cana-1019	29	18	spectral	spectral	ADJ
cana-1019	29	19	contrast	contrast	NOUN
cana-1019	29	20	tell	tell	VERB
cana-1019	29	21	us	we	PRON
cana-1019	29	22	a	a	DET
cana-1019	29	23	lot	lot	NOUN
cana-1019	29	24	about	about	ADP
cana-1019	29	25	the	the	DET
cana-1019	29	26	music	music	NOUN
cana-1019	29	27	's	's	PART
cana-1019	29	28	harmonic	harmonic	ADJ
cana-1019	29	29	and	and	CCONJ
cana-1019	29	30	rhythmic	rhythmic	ADJ
cana-1019	29	31	qualities	quality	NOUN
cana-1019	29	32	[	[	X
cana-1019	29	33	3	3	NUM
cana-1019	29	34	]	]	PUNCT
cana-1019	29	35	.	.	PUNCT
cana-1019	30	1	on	on	ADP
cana-1019	30	2	the	the	DET
cana-1019	30	3	other	other	ADJ
cana-1019	30	4	hand	hand	NOUN
cana-1019	30	5	,	,	PUNCT
cana-1019	30	6	the	the	DET
cana-1019	30	7	lyrics	lyric	NOUN
cana-1019	30	8	can	can	AUX
cana-1019	30	9	help	help	VERB
cana-1019	30	10	you	you	PRON
cana-1019	30	11	understand	understand	VERB
cana-1019	30	12	the	the	DET
cana-1019	30	13	emotional	emotional	ADJ
cana-1019	30	14	and	and	CCONJ
cana-1019	30	15	thematic	thematic	ADJ
cana-1019	30	16	themes	theme	NOUN
cana-1019	30	17	of	of	ADP
cana-1019	30	18	a	a	DET
cana-1019	30	19	song	song	NOUN
cana-1019	30	20	.	.	PUNCT
cana-1019	31	1	many	many	ADJ
cana-1019	31	2	natural	natural	ADJ
cana-1019	31	3	language	language	NOUN
cana-1019	31	4	processing	processing	NOUN
cana-1019	31	5	(	(	PUNCT
cana-1019	31	6	nlp	nlp	ADJ
cana-1019	31	7	)	)	PUNCT
cana-1019	31	8	methods	method	NOUN
cana-1019	31	9	,	,	PUNCT
cana-1019	31	10	like	like	ADP
cana-1019	31	11	term	term	NOUN
cana-1019	31	12	frequency	frequency	NOUN
cana-1019	31	13	-	-	PUNCT
cana-1019	31	14	inverse	inverse	NOUN
cana-1019	31	15	document	document	NOUN
cana-1019	31	16	frequency	frequency	NOUN
cana-1019	31	17	(	(	PUNCT
cana-1019	31	18	tf	tf	PROPN
cana-1019	31	19	-	-	PUNCT
cana-1019	31	20	idf	idf	NOUN
cana-1019	31	21	)	)	PUNCT
cana-1019	31	22	and	and	CCONJ
cana-1019	31	23	word	word	NOUN
cana-1019	31	24	embeddings	embedding	NOUN
cana-1019	31	25	,	,	PUNCT
cana-1019	31	26	have	have	AUX
cana-1019	31	27	been	be	AUX
cana-1019	31	28	shown	show	VERB
cana-1019	31	29	to	to	PART
cana-1019	31	30	be	be	AUX
cana-1019	31	31	useful	useful	ADJ
cana-1019	31	32	for	for	ADP
cana-1019	31	33	reading	read	VERB
cana-1019	31	34	songs	song	NOUN
cana-1019	31	35	and	and	CCONJ
cana-1019	31	36	pulling	pull	VERB
cana-1019	31	37	out	out	ADP
cana-1019	31	38	useful	useful	ADJ
cana-1019	31	39	information	information	NOUN
cana-1019	31	40	.	.	PUNCT
cana-1019	32	1	putting	put	VERB
cana-1019	32	2	these	these	DET
cana-1019	32	3	audio	audio	ADJ
cana-1019	32	4	and	and	CCONJ
cana-1019	32	5	written	write	VERB
cana-1019	32	6	traits	trait	NOUN
cana-1019	32	7	together	together	ADV
cana-1019	32	8	will	will	AUX
cana-1019	32	9	help	help	VERB
cana-1019	32	10	us	we	PRON
cana-1019	32	11	make	make	VERB
cana-1019	32	12	more	more	ADJ
cana-1019	32	13	complete	complete	ADJ
cana-1019	32	14	models	model	NOUN
cana-1019	32	15	that	that	PRON
cana-1019	32	16	can	can	AUX
cana-1019	32	17	understand	understand	VERB
cana-1019	32	18	all	all	DET
cana-1019	32	19	the	the	DET
cana-1019	32	20	different	different	ADJ
cana-1019	32	21	kinds	kind	NOUN
cana-1019	32	22	of	of	ADP
cana-1019	32	23	information	information	NOUN
cana-1019	32	24	that	that	PRON
cana-1019	32	25	are	be	AUX
cana-1019	32	26	in	in	ADP
cana-1019	32	27	musical	musical	ADJ
cana-1019	32	28	pieces	piece	NOUN
cana-1019	32	29	[	[	X
cana-1019	32	30	2	2	NUM
cana-1019	32	31	]	]	PUNCT
cana-1019	32	32	.	.	PUNCT
cana-1019	33	1	it	it	PRON
cana-1019	33	2	look	look	VERB
cana-1019	33	3	into	into	ADP
cana-1019	33	4	how	how	SCONJ
cana-1019	33	5	well	well	ADV
cana-1019	33	6	different	different	ADJ
cana-1019	33	7	machine	machine	NOUN
cana-1019	33	8	learning	learn	VERB
cana-1019	33	9	classification	classification	NOUN
cana-1019	33	10	methods	method	NOUN
cana-1019	33	11	work	work	VERB
cana-1019	33	12	for	for	ADP
cana-1019	33	13	figuring	figure	VERB
cana-1019	33	14	out	out	ADP
cana-1019	33	15	the	the	DET
cana-1019	33	16	mood	mood	NOUN
cana-1019	33	17	of	of	ADP
cana-1019	33	18	music	music	NOUN
cana-1019	33	19	.	.	PUNCT
cana-1019	34	1	logistic	logistic	ADJ
cana-1019	34	2	regression	regression	NOUN
cana-1019	34	3	,	,	PUNCT
cana-1019	34	4	sgd	sgd	PROPN
cana-1019	34	5	classifier	classifier	PROPN
cana-1019	34	6	,	,	PUNCT
cana-1019	34	7	gaussian	gaussian	ADJ
cana-1019	34	8	naive	naive	ADJ
cana-1019	34	9	bayes	bayes	NOUN
cana-1019	34	10	,	,	PUNCT
cana-1019	34	11	decision	decision	NOUN
cana-1019	34	12	tree	tree	NOUN
cana-1019	34	13	,	,	PUNCT
cana-1019	34	14	random	random	ADJ
cana-1019	34	15	forest	forest	NOUN
cana-1019	34	16	,	,	PUNCT
cana-1019	34	17	xgb	xgb	PROPN
cana-1019	34	18	classifier	classifier	NOUN
cana-1019	34	19	,	,	PUNCT
cana-1019	34	20	svm	svm	PROPN
cana-1019	34	21	linear	linear	NOUN
cana-1019	34	22	,	,	PUNCT
cana-1019	34	23	and	and	CCONJ
cana-1019	34	24	k	k	X
cana-1019	34	25	-	-	PUNCT
cana-1019	34	26	nearest	near	ADJ
cana-1019	34	27	neighbors	neighbor	NOUN
cana-1019	34	28	(	(	PUNCT
cana-1019	34	29	knn	knn	PROPN
cana-1019	34	30	)	)	PUNCT
cana-1019	34	31	are	be	AUX
cana-1019	34	32	some	some	PRON
cana-1019	34	33	of	of	ADP
cana-1019	34	34	the	the	DET
cana-1019	34	35	algorithms	algorithm	NOUN
cana-1019	34	36	we	we	PRON
cana-1019	34	37	look	look	VERB
cana-1019	34	38	into	into	ADP
cana-1019	34	39	.	.	PUNCT
cana-1019	35	1	because	because	SCONJ
cana-1019	35	2	they	they	PRON
cana-1019	35	3	are	be	AUX
cana-1019	35	4	all	all	ADV
cana-1019	35	5	different	different	ADJ
cana-1019	35	6	,	,	PUNCT
cana-1019	35	7	these	these	DET
cana-1019	35	8	algorithms	algorithm	NOUN
cana-1019	35	9	can	can	AUX
cana-1019	35	10	be	be	AUX
cana-1019	35	11	used	use	VERB
cana-1019	35	12	for	for	ADP
cana-1019	35	13	different	different	ADJ
cana-1019	35	14	kinds	kind	NOUN
cana-1019	35	15	of	of	ADP
cana-1019	35	16	data	datum	NOUN
cana-1019	35	17	and	and	CCONJ
cana-1019	35	18	sorting	sort	VERB
cana-1019	35	19	jobs	job	NOUN
cana-1019	35	20	.	.	PUNCT
cana-1019	36	1	we	we	PRON
cana-1019	36	2	want	want	VERB
cana-1019	36	3	to	to	PART
cana-1019	36	4	find	find	VERB
cana-1019	36	5	the	the	DET
cana-1019	36	6	best	good	ADJ
cana-1019	36	7	method	method	NOUN
cana-1019	36	8	for	for	ADP
cana-1019	36	9	our	our	PRON
cana-1019	36	10	classification	classification	NOUN
cana-1019	36	11	problem	problem	NOUN
cana-1019	36	12	by	by	ADP
cana-1019	36	13	carefully	carefully	ADV
cana-1019	36	14	comparing	compare	VERB
cana-1019	36	15	these	these	DET
cana-1019	36	16	models	model	NOUN
cana-1019	36	17	.	.	PUNCT
cana-1019	37	1	fine	fine	ADJ
cana-1019	37	2	-	-	PUNCT
cana-1019	37	3	tuning	tune	VERB
cana-1019	37	4	the	the	DET
cana-1019	37	5	hyperparameters	hyperparameter	NOUN
cana-1019	37	6	is	be	AUX
cana-1019	37	7	the	the	DET
cana-1019	37	8	next	next	ADJ
cana-1019	37	9	important	important	ADJ
cana-1019	37	10	step	step	NOUN
cana-1019	37	11	after	after	ADP
cana-1019	37	12	finding	find	VERB
cana-1019	37	13	the	the	DET
cana-1019	37	14	models	model	NOUN
cana-1019	37	15	that	that	PRON
cana-1019	37	16	work	work	VERB
cana-1019	37	17	best	well	ADV
cana-1019	37	18	.	.	PUNCT
cana-1019	38	1	this	this	PRON
cana-1019	38	2	is	be	AUX
cana-1019	38	3	different	different	ADJ
cana-1019	38	4	from	from	ADP
cana-1019	38	5	the	the	DET
cana-1019	38	6	model	model	NOUN
cana-1019	38	7	's	's	PART
cana-1019	38	8	parameters	parameter	NOUN
cana-1019	38	9	:	:	PUNCT
cana-1019	38	10	hyperparameters	hyperparameter	NOUN
cana-1019	38	11	are	be	AUX
cana-1019	38	12	the	the	DET
cana-1019	38	13	parameters	parameter	NOUN
cana-1019	38	14	of	of	ADP
cana-1019	38	15	the	the	DET
cana-1019	38	16	learning	learning	NOUN
cana-1019	38	17	method	method	NOUN
cana-1019	38	18	itself	itself	PRON
cana-1019	38	19	.	.	PUNCT
cana-1019	39	1	to	to	PART
cana-1019	39	2	get	get	VERB
cana-1019	39	3	the	the	DET
cana-1019	39	4	most	most	ADJ
cana-1019	39	5	out	out	ADP
cana-1019	39	6	of	of	ADP
cana-1019	39	7	the	the	DET
cana-1019	39	8	model	model	NOUN
cana-1019	39	9	,	,	PUNCT
cana-1019	39	10	these	these	DET
cana-1019	39	11	hyperparameters	hyperparameter	NOUN
cana-1019	39	12	must	must	AUX
cana-1019	39	13	be	be	AUX
cana-1019	39	14	tuned	tune	VERB
cana-1019	39	15	correctly	correctly	ADV
cana-1019	39	16	.	.	PUNCT
cana-1019	40	1	we	we	PRON
cana-1019	40	2	use	use	VERB
cana-1019	40	3	methods	method	NOUN
cana-1019	40	4	like	like	ADP
cana-1019	40	5	grid	grid	NOUN
cana-1019	40	6	search	search	NOUN
cana-1019	40	7	and	and	CCONJ
cana-1019	40	8	random	random	ADJ
cana-1019	40	9	search	search	NOUN
cana-1019	40	10	to	to	PART
cana-1019	40	11	carefully	carefully	ADV
cana-1019	40	12	look	look	VERB
cana-1019	40	13	through	through	ADP
cana-1019	40	14	the	the	DET
cana-1019	40	15	hyperparameter	hyperparameter	NOUN
cana-1019	40	16	space	space	NOUN
cana-1019	40	17	of	of	ADP
cana-1019	40	18	random	random	ADJ
cana-1019	40	19	forest	forest	NOUN
cana-1019	40	20	,	,	PUNCT
cana-1019	40	21	xgb	xgb	PROPN
cana-1019	40	22	classifier	classifier	NOUN
cana-1019	40	23	,	,	PUNCT
cana-1019	40	24	and	and	CCONJ
cana-1019	40	25	svm	svm	PROPN
cana-1019	40	26	,	,	PUNCT
cana-1019	40	27	which	which	PRON
cana-1019	40	28	are	be	AUX
cana-1019	40	29	our	our	PRON
cana-1019	40	30	best	good	ADJ
cana-1019	40	31	models	model	NOUN
cana-1019	40	32	.	.	PUNCT
cana-1019	41	1	cross	cross	ADJ
cana-1019	41	2	-	-	ADJ
cana-1019	41	3	validation	validation	NOUN
cana-1019	41	4	is	be	AUX
cana-1019	41	5	used	use	VERB
cana-1019	41	6	to	to	PART
cana-1019	41	7	make	make	VERB
cana-1019	41	8	sure	sure	ADJ
cana-1019	41	9	the	the	DET
cana-1019	41	10	results	result	NOUN
cana-1019	41	11	are	be	AUX
cana-1019	41	12	reliable	reliable	ADJ
cana-1019	41	13	and	and	CCONJ
cana-1019	41	14	can	can	AUX
cana-1019	41	15	be	be	AUX
cana-1019	41	16	used	use	VERB
cana-1019	41	17	in	in	ADP
cana-1019	41	18	other	other	ADJ
cana-1019	41	19	situations	situation	NOUN
cana-1019	41	20	.	.	PUNCT
cana-1019	42	1	our	our	PRON
cana-1019	42	2	study	study	NOUN
cana-1019	42	3	shows	show	VERB
cana-1019	42	4	that	that	SCONJ
cana-1019	42	5	fine	fine	ADJ
cana-1019	42	6	-	-	PUNCT
cana-1019	42	7	tuning	tuning	NOUN
cana-1019	42	8	makes	make	VERB
cana-1019	42	9	music	music	NOUN
cana-1019	42	10	mood	mood	NOUN
cana-1019	42	11	classification	classification	NOUN
cana-1019	42	12	models	model	NOUN
cana-1019	42	13	much	much	ADV
cana-1019	42	14	more	more	ADV
cana-1019	42	15	accurate	accurate	ADJ
cana-1019	42	16	and	and	CCONJ
cana-1019	42	17	reliable	reliable	ADJ
cana-1019	42	18	.	.	PUNCT
cana-1019	43	1	it	it	PRON
cana-1019	43	2	was	be	AUX
cana-1019	43	3	especially	especially	ADV
cana-1019	43	4	the	the	DET
cana-1019	43	5	fine	fine	ADV
cana-1019	43	6	-	-	PUNCT
cana-1019	43	7	tuned	tune	VERB
cana-1019	43	8	xgb	xgb	PROPN
cana-1019	43	9	classifier	classifier	NOUN
cana-1019	43	10	that	that	PRON
cana-1019	43	11	showed	show	VERB
cana-1019	43	12	huge	huge	ADJ
cana-1019	43	13	performance	performance	NOUN
cana-1019	43	14	gains	gain	NOUN
cana-1019	43	15	[	[	X
cana-1019	43	16	3	3	NUM
cana-1019	43	17	]	]	PUNCT
cana-1019	43	18	.	.	PUNCT
cana-1019	44	1	this	this	PRON
cana-1019	44	2	shows	show	VERB
cana-1019	44	3	how	how	SCONJ
cana-1019	44	4	important	important	ADJ
cana-1019	44	5	it	it	PRON
cana-1019	44	6	is	be	AUX
cana-1019	44	7	to	to	PART
cana-1019	44	8	choose	choose	VERB
cana-1019	44	9	the	the	DET
cana-1019	44	10	right	right	ADJ
cana-1019	44	11	model	model	NOUN
cana-1019	44	12	and	and	CCONJ
cana-1019	44	13	optimize	optimize	VERB
cana-1019	44	14	hyperparameters	hyperparameter	NOUN
cana-1019	44	15	to	to	PART
cana-1019	44	16	get	get	VERB
cana-1019	44	17	the	the	DET
cana-1019	44	18	best	good	ADJ
cana-1019	44	19	results	result	NOUN
cana-1019	44	20	.	.	PUNCT
cana-1019	45	1	this	this	DET
cana-1019	45	2	study	study	NOUN
cana-1019	45	3	not	not	PART
cana-1019	45	4	only	only	ADV
cana-1019	45	5	adds	add	VERB
cana-1019	45	6	to	to	ADP
cana-1019	45	7	our	our	PRON
cana-1019	45	8	technical	technical	ADJ
cana-1019	45	9	knowledge	knowledge	NOUN
cana-1019	45	10	of	of	ADP
cana-1019	45	11	how	how	SCONJ
cana-1019	45	12	to	to	PART
cana-1019	45	13	classify	classify	VERB
cana-1019	45	14	music	music	NOUN
cana-1019	45	15	based	base	VERB
cana-1019	45	16	on	on	ADP
cana-1019	45	17	mood	mood	NOUN
cana-1019	45	18	,	,	PUNCT
cana-1019	45	19	but	but	CCONJ
cana-1019	45	20	it	it	PRON
cana-1019	45	21	also	also	ADV
cana-1019	45	22	shows	show	VERB
cana-1019	45	23	us	we	PRON
cana-1019	45	24	how	how	SCONJ
cana-1019	45	25	to	to	PART
cana-1019	45	26	use	use	VERB
cana-1019	45	27	these	these	DET
cana-1019	45	28	models	model	NOUN
cana-1019	45	29	in	in	ADP
cana-1019	45	30	real	real	ADJ
cana-1019	45	31	life	life	NOUN
cana-1019	45	32	.	.	PUNCT
cana-1019	46	1	this	this	DET
cana-1019	46	2	study	study	NOUN
cana-1019	46	3	shows	show	VERB
cana-1019	46	4	a	a	DET
cana-1019	46	5	complete	complete	ADJ
cana-1019	46	6	method	method	NOUN
cana-1019	46	7	for	for	ADP
cana-1019	46	8	classifying	classify	VERB
cana-1019	46	9	music	music	NOUN
cana-1019	46	10	moods	mood	NOUN
cana-1019	46	11	using	use	VERB
cana-1019	46	12	cutting	cutting	NOUN
cana-1019	46	13	-	-	PUNCT
cana-1019	46	14	edge	edge	NOUN
cana-1019	46	15	feature	feature	NOUN
cana-1019	46	16	extraction	extraction	NOUN
cana-1019	46	17	methods	method	NOUN
cana-1019	46	18	and	and	CCONJ
cana-1019	46	19	cutting	cutting	NOUN
cana-1019	46	20	-	-	PUNCT
cana-1019	46	21	edge	edge	NOUN
cana-1019	46	22	machine	machine	NOUN
cana-1019	46	23	learning	learn	VERB
cana-1019	46	24	algorithms	algorithm	NOUN
cana-1019	46	25	.	.	PUNCT
cana-1019	47	1	we	we	PRON
cana-1019	47	2	improve	improve	VERB
cana-1019	47	3	the	the	DET
cana-1019	47	4	performance	performance	NOUN
cana-1019	47	5	of	of	ADP
cana-1019	47	6	the	the	DET
cana-1019	47	7	most	most	ADV
cana-1019	47	8	promising	promising	ADJ
cana-1019	47	9	models	model	NOUN
cana-1019	47	10	by	by	ADP
cana-1019	47	11	fine	fine	ADV
cana-1019	47	12	-	-	PUNCT
cana-1019	47	13	tuning	tune	VERB
cana-1019	47	14	their	their	PRON
cana-1019	47	15	hyperparameters	hyperparameter	NOUN
cana-1019	47	16	[	[	X
cana-1019	47	17	4	4	NUM
cana-1019	47	18	]	]	PUNCT
cana-1019	47	19	.	.	PUNCT
cana-1019	48	1	this	this	PRON
cana-1019	48	2	paves	pave	VERB
cana-1019	48	3	the	the	DET
cana-1019	48	4	way	way	NOUN
cana-1019	48	5	for	for	ADP
cana-1019	48	6	more	more	ADV
cana-1019	48	7	accurate	accurate	ADJ
cana-1019	48	8	and	and	CCONJ
cana-1019	48	9	userfriendly	userfriendly	ADV
cana-1019	48	10	systems	system	NOUN
cana-1019	48	11	that	that	PRON
cana-1019	48	12	suggest	suggest	VERB
cana-1019	48	13	music	music	NOUN
cana-1019	48	14	.	.	PUNCT
cana-1019	49	1	this	this	DET
cana-1019	49	2	study	study	NOUN
cana-1019	49	3	adds	add	VERB
cana-1019	49	4	to	to	ADP
cana-1019	49	5	the	the	DET
cana-1019	49	6	field	field	NOUN
cana-1019	49	7	of	of	ADP
cana-1019	49	8	retrieving	retrieve	VERB
cana-1019	49	9	music	music	NOUN
cana-1019	49	10	information	information	NOUN
cana-1019	49	11	and	and	CCONJ
cana-1019	49	12	shows	show	VERB
cana-1019	49	13	how	how	SCONJ
cana-1019	49	14	machine	machine	NOUN
cana-1019	49	15	learning	learning	NOUN
cana-1019	49	16	can	can	AUX
cana-1019	49	17	help	help	VERB
cana-1019	49	18	us	we	PRON
cana-1019	49	19	understand	understand	VERB
cana-1019	49	20	and	and	CCONJ
cana-1019	49	21	connect	connect	VERB
cana-1019	49	22	with	with	ADP
cana-1019	49	23	music	music	NOUN
cana-1019	49	24	better	well	ADV
cana-1019	49	25	.	.	PUNCT
cana-1019	50	1	2	2	X
cana-1019	50	2	.	.	X
cana-1019	50	3	related	relate	VERB
cana-1019	50	4	work	work	NOUN
cana-1019	50	5	music	music	NOUN
cana-1019	50	6	temperament	temperament	NOUN
cana-1019	50	7	classification	classification	NOUN
cana-1019	50	8	has	have	AUX
cana-1019	50	9	experienced	experience	VERB
cana-1019	50	10	noteworthy	noteworthy	ADJ
cana-1019	50	11	advancement	advancement	NOUN
cana-1019	50	12	with	with	ADP
cana-1019	50	13	the	the	DET
cana-1019	50	14	rise	rise	NOUN
cana-1019	50	15	of	of	ADP
cana-1019	50	16	machine	machine	NOUN
cana-1019	50	17	learning	learning	NOUN
cana-1019	50	18	methods	method	NOUN
cana-1019	50	19	,	,	PUNCT
cana-1019	50	20	profound	profound	ADJ
cana-1019	50	21	learning	learning	NOUN
cana-1019	50	22	structures	structure	NOUN
cana-1019	50	23	,	,	PUNCT
cana-1019	50	24	and	and	CCONJ
cana-1019	50	25	multimodal	multimodal	NOUN
cana-1019	50	26	approaches	approach	NOUN
cana-1019	50	27	.	.	PUNCT
cana-1019	51	1	early	early	ADJ
cana-1019	51	2	approaches	approach	NOUN
cana-1019	51	3	depended	depend	VERB
cana-1019	51	4	intensely	intensely	ADV
cana-1019	51	5	on	on	ADP
cana-1019	51	6	manual	manual	ADJ
cana-1019	51	7	explanation	explanation	NOUN
cana-1019	51	8	and	and	CCONJ
cana-1019	51	9	heuristic	heuristic	NOUN
cana-1019	51	10	-	-	PUNCT
cana-1019	51	11	based	base	VERB
cana-1019	51	12	strategies	strategy	NOUN
cana-1019	51	13	,	,	PUNCT
cana-1019	51	14	which	which	PRON
cana-1019	51	15	were	be	AUX
cana-1019	51	16	constrained	constrain	VERB
cana-1019	51	17	by	by	ADP
cana-1019	51	18	subjectivity	subjectivity	NOUN
cana-1019	51	19	and	and	CCONJ
cana-1019	51	20	needed	need	VERB
cana-1019	51	21	versatility	versatility	NOUN
cana-1019	51	22	[	[	X
cana-1019	51	23	5	5	NUM
cana-1019	51	24	]	]	PUNCT
cana-1019	51	25	.	.	PUNCT
cana-1019	52	1	the	the	DET
cana-1019	52	2	coming	coming	NOUN
cana-1019	52	3	of	of	ADP
cana-1019	52	4	machine	machine	NOUN
cana-1019	52	5	learning	learn	VERB
cana-1019	52	6	empowered	empower	VERB
cana-1019	52	7	analysts	analyst	NOUN
cana-1019	52	8	to	to	PART
cana-1019	52	9	robotize	robotize	VERB
cana-1019	52	10	temperament	temperament	NOUN
cana-1019	52	11	classification	classification	NOUN
cana-1019	52	12	forms	form	NOUN
cana-1019	52	13	by	by	ADP
cana-1019	52	14	learning	learn	VERB
cana-1019	52	15	designs	design	NOUN
cana-1019	52	16	from	from	ADP
cana-1019	52	17	information	information	NOUN
cana-1019	52	18	[	[	X
cana-1019	52	19	6	6	NUM
cana-1019	52	20	]	]	PUNCT
cana-1019	52	21	.	.	PUNCT
cana-1019	53	1	back	back	ADJ
cana-1019	53	2	vector	vector	NOUN
cana-1019	53	3	machines	machine	NOUN
cana-1019	53	4	(	(	PUNCT
cana-1019	53	5	svm	svm	PROPN
cana-1019	53	6	)	)	PUNCT
cana-1019	53	7	developed	develop	VERB
cana-1019	53	8	as	as	ADP
cana-1019	53	9	a	a	DET
cana-1019	53	10	significant	significant	ADJ
cana-1019	53	11	device	device	NOUN
cana-1019	53	12	in	in	ADP
cana-1019	53	13	early	early	ADJ
cana-1019	53	14	considers	consider	NOUN
cana-1019	53	15	,	,	PUNCT
cana-1019	53	16	illustrating	illustrate	VERB
cana-1019	53	17	their	their	PRON
cana-1019	53	18	adequacy	adequacy	NOUN
cana-1019	53	19	in	in	ADP
cana-1019	53	20	capturing	capture	VERB
cana-1019	53	21	nonlinear	nonlinear	ADJ
cana-1019	53	22	connections	connection	NOUN
cana-1019	53	23	between	between	ADP
cana-1019	53	24	sound	sound	ADJ
cana-1019	53	25	highlights	highlight	NOUN
cana-1019	53	26	extricated	extricate	VERB
cana-1019	53	27	from	from	ADP
cana-1019	53	28	music	music	NOUN
cana-1019	53	29	signals	signal	NOUN
cana-1019	53	30	and	and	CCONJ
cana-1019	53	31	disposition	disposition	NOUN
cana-1019	53	32	names	name	NOUN
cana-1019	53	33	[	[	X
cana-1019	53	34	7	7	NUM
cana-1019	53	35	]	]	PUNCT
cana-1019	53	36	.	.	PUNCT
cana-1019	54	1	svms	svms	NOUN
cana-1019	54	2	given	give	VERB
cana-1019	54	3	a	a	DET
cana-1019	54	4	vigorous	vigorous	ADJ
cana-1019	54	5	system	system	NOUN
cana-1019	54	6	for	for	ADP
cana-1019	54	7	progressing	progress	VERB
cana-1019	54	8	classification	classification	NOUN
cana-1019	54	9	precision	precision	NOUN
cana-1019	54	10	and	and	CCONJ
cana-1019	54	11	decreasing	decrease	VERB
cana-1019	54	12	human	human	ADJ
cana-1019	54	13	intercession	intercession	NOUN
cana-1019	54	14	in	in	ADP
cana-1019	54	15	temperament	temperament	NOUN
cana-1019	54	16	labeling	labeling	NOUN
cana-1019	54	17	.	.	PUNCT
cana-1019	55	1	profound	profound	ADJ
cana-1019	55	2	learning	learn	VERB
cana-1019	55	3	structures	structure	NOUN
cana-1019	55	4	,	,	PUNCT
cana-1019	55	5	especially	especially	ADV
cana-1019	55	6	communications	communication	NOUN
cana-1019	55	7	on	on	ADP
cana-1019	55	8	applied	apply	VERB
cana-1019	55	9	nonlinear	nonlinear	ADJ
cana-1019	55	10	analysis	analysis	NOUN
cana-1019	55	11	issn	issn	NOUN
cana-1019	55	12	:	:	PUNCT
cana-1019	55	13	1074	1074	NUM
cana-1019	55	14	-	-	PUNCT
cana-1019	55	15	133x	133x	NUM
cana-1019	55	16	vol	vol	NOUN
cana-1019	55	17	31	31	NUM
cana-1019	55	18	no	no	NOUN
cana-1019	55	19	.	.	PUNCT
cana-1019	56	1	5s	5s	NUM
cana-1019	56	2	(	(	PUNCT
cana-1019	56	3	2024	2024	NUM
cana-1019	56	4	)	)	PUNCT
cana-1019	56	5	236	236	NUM
cana-1019	56	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1019	56	7	convolutional	convolutional	ADJ
cana-1019	56	8	neural	neural	ADJ
cana-1019	56	9	systems	system	NOUN
cana-1019	56	10	(	(	PUNCT
cana-1019	56	11	cnns	cnns	PROPN
cana-1019	56	12	)	)	PUNCT
cana-1019	56	13	and	and	CCONJ
cana-1019	56	14	repetitive	repetitive	ADJ
cana-1019	56	15	neural	neural	ADJ
cana-1019	56	16	systems	system	NOUN
cana-1019	56	17	(	(	PUNCT
cana-1019	56	18	rnns	rnns	PROPN
cana-1019	56	19	)	)	PUNCT
cana-1019	56	20	,	,	PUNCT
cana-1019	56	21	revolutionized	revolutionize	VERB
cana-1019	56	22	the	the	DET
cana-1019	56	23	field	field	NOUN
cana-1019	56	24	by	by	ADP
cana-1019	56	25	specifically	specifically	ADV
cana-1019	56	26	handling	handle	VERB
cana-1019	56	27	spectrograms	spectrogram	NOUN
cana-1019	56	28	and	and	CCONJ
cana-1019	56	29	capturing	capture	VERB
cana-1019	56	30	worldly	worldly	ADJ
cana-1019	56	31	conditions	condition	NOUN
cana-1019	56	32	in	in	ADP
cana-1019	56	33	music	music	NOUN
cana-1019	56	34	information	information	NOUN
cana-1019	56	35	[	[	X
cana-1019	56	36	8	8	NUM
cana-1019	56	37	]	]	PUNCT
cana-1019	56	38	.	.	PUNCT
cana-1019	57	1	choi	choi	PROPN
cana-1019	57	2	et	et	PROPN
cana-1019	57	3	al	al	PROPN
cana-1019	57	4	.	.	PUNCT
cana-1019	58	1	[	[	X
cana-1019	58	2	8	8	NUM
cana-1019	58	3	]	]	PUNCT
cana-1019	58	4	showcased	showcase	VERB
cana-1019	58	5	the	the	DET
cana-1019	58	6	adequacy	adequacy	NOUN
cana-1019	58	7	of	of	ADP
cana-1019	58	8	cnns	cnn	NOUN
cana-1019	58	9	in	in	ADP
cana-1019	58	10	extricating	extricate	VERB
cana-1019	58	11	various	various	ADJ
cana-1019	58	12	leveled	level	VERB
cana-1019	58	13	highlights	highlight	NOUN
cana-1019	58	14	from	from	ADP
cana-1019	58	15	sound	sound	ADJ
cana-1019	58	16	signals	signal	NOUN
cana-1019	58	17	,	,	PUNCT
cana-1019	58	18	whereas	whereas	SCONJ
cana-1019	58	19	rnns	rnn	NOUN
cana-1019	58	20	exceeded	exceed	VERB
cana-1019	58	21	expectations	expectation	NOUN
cana-1019	58	22	in	in	ADP
cana-1019	58	23	modeling	model	VERB
cana-1019	58	24	consecutive	consecutive	ADJ
cana-1019	58	25	designs	design	NOUN
cana-1019	58	26	in	in	ADP
cana-1019	58	27	music	music	NOUN
cana-1019	58	28	.	.	PUNCT
cana-1019	59	1	in	in	ADP
cana-1019	59	2	parallel	parallel	NOUN
cana-1019	59	3	,	,	PUNCT
cana-1019	59	4	analysts	analyst	NOUN
cana-1019	59	5	investigated	investigate	VERB
cana-1019	59	6	the	the	DET
cana-1019	59	7	integration	integration	NOUN
cana-1019	59	8	of	of	ADP
cana-1019	59	9	literary	literary	ADJ
cana-1019	59	10	data	datum	NOUN
cana-1019	59	11	,	,	PUNCT
cana-1019	59	12	such	such	ADJ
cana-1019	59	13	as	as	ADP
cana-1019	59	14	tune	tune	NOUN
cana-1019	59	15	verses	verse	NOUN
cana-1019	59	16	,	,	PUNCT
cana-1019	59	17	into	into	ADP
cana-1019	59	18	disposition	disposition	NOUN
cana-1019	59	19	classification	classification	NOUN
cana-1019	59	20	frameworks	framework	NOUN
cana-1019	59	21	.	.	PUNCT
cana-1019	60	1	common	common	ADJ
cana-1019	60	2	dialect	dialect	NOUN
cana-1019	60	3	preparing	prepare	VERB
cana-1019	60	4	(	(	PUNCT
cana-1019	60	5	nlp	nlp	ADJ
cana-1019	60	6	)	)	PUNCT
cana-1019	60	7	methods	method	NOUN
cana-1019	60	8	,	,	PUNCT
cana-1019	60	9	counting	count	VERB
cana-1019	60	10	opinion	opinion	NOUN
cana-1019	60	11	investigation	investigation	NOUN
cana-1019	60	12	and	and	CCONJ
cana-1019	60	13	topical	topical	ADJ
cana-1019	60	14	extraction	extraction	NOUN
cana-1019	60	15	,	,	PUNCT
cana-1019	60	16	improved	improve	VERB
cana-1019	60	17	the	the	DET
cana-1019	60	18	relevant	relevant	ADJ
cana-1019	60	19	understanding	understanding	NOUN
cana-1019	60	20	of	of	ADP
cana-1019	60	21	music	music	NOUN
cana-1019	60	22	substance	substance	NOUN
cana-1019	61	1	[	[	X
cana-1019	61	2	9	9	NUM
cana-1019	61	3	]	]	PUNCT
cana-1019	61	4	.	.	PUNCT
cana-1019	62	1	hu	hu	PROPN
cana-1019	62	2	and	and	CCONJ
cana-1019	62	3	downie	downie	PROPN
cana-1019	63	1	[	[	X
cana-1019	63	2	9	9	NUM
cana-1019	63	3	]	]	PUNCT
cana-1019	63	4	illustrated	illustrate	VERB
cana-1019	63	5	that	that	SCONJ
cana-1019	63	6	combining	combine	VERB
cana-1019	63	7	literary	literary	ADJ
cana-1019	63	8	examination	examination	NOUN
cana-1019	63	9	with	with	ADP
cana-1019	63	10	sound	sound	ADJ
cana-1019	63	11	highlights	highlight	NOUN
cana-1019	63	12	altogether	altogether	ADV
cana-1019	63	13	progressed	progress	VERB
cana-1019	63	14	classification	classification	NOUN
cana-1019	63	15	precision	precision	NOUN
cana-1019	63	16	,	,	PUNCT
cana-1019	63	17	highlighting	highlight	VERB
cana-1019	63	18	the	the	DET
cana-1019	63	19	complementary	complementary	ADJ
cana-1019	63	20	nature	nature	NOUN
cana-1019	63	21	of	of	ADP
cana-1019	63	22	acoustic	acoustic	ADJ
cana-1019	63	23	and	and	CCONJ
cana-1019	63	24	semantic	semantic	ADJ
cana-1019	63	25	prompts	prompt	NOUN
cana-1019	63	26	in	in	ADP
cana-1019	63	27	temperament	temperament	NOUN
cana-1019	63	28	forecast	forecast	NOUN
cana-1019	63	29	.	.	PUNCT
cana-1019	64	1	outfit	outfit	ADJ
cana-1019	64	2	strategies	strategy	NOUN
cana-1019	64	3	have	have	AUX
cana-1019	64	4	moreover	moreover	ADV
cana-1019	64	5	played	play	VERB
cana-1019	64	6	a	a	DET
cana-1019	64	7	significant	significant	ADJ
cana-1019	64	8	part	part	NOUN
cana-1019	64	9	in	in	ADP
cana-1019	64	10	upgrading	upgrade	VERB
cana-1019	64	11	prescient	prescient	ADJ
cana-1019	64	12	execution	execution	NOUN
cana-1019	64	13	by	by	ADP
cana-1019	64	14	combining	combine	VERB
cana-1019	64	15	numerous	numerous	ADJ
cana-1019	64	16	classifiers	classifier	NOUN
cana-1019	64	17	.	.	PUNCT
cana-1019	65	1	zhang	zhang	PROPN
cana-1019	65	2	et	et	PROPN
cana-1019	65	3	al	al	PROPN
cana-1019	65	4	.	.	PUNCT
cana-1019	66	1	[	[	X
cana-1019	66	2	10	10	NUM
cana-1019	66	3	]	]	PUNCT
cana-1019	66	4	proposed	propose	VERB
cana-1019	66	5	a	a	DET
cana-1019	66	6	half	half	NOUN
cana-1019	66	7	breed	breed	NOUN
cana-1019	66	8	demonstrate	demonstrate	NOUN
cana-1019	66	9	that	that	SCONJ
cana-1019	66	10	coordinates	coordinate	NOUN
cana-1019	66	11	svm	svm	VERB
cana-1019	66	12	with	with	ADP
cana-1019	66	13	choice	choice	NOUN
cana-1019	66	14	trees	tree	NOUN
cana-1019	66	15	,	,	PUNCT
cana-1019	66	16	leveraging	leverage	VERB
cana-1019	66	17	the	the	DET
cana-1019	66	18	qualities	quality	NOUN
cana-1019	66	19	of	of	ADP
cana-1019	66	20	both	both	DET
cana-1019	66	21	calculations	calculation	NOUN
cana-1019	66	22	to	to	PART
cana-1019	66	23	realize	realize	VERB
cana-1019	66	24	prevalent	prevalent	NOUN
cana-1019	66	25	comes	come	VERB
cana-1019	66	26	about	about	ADP
cana-1019	66	27	compared	compare	VERB
cana-1019	66	28	to	to	ADP
cana-1019	66	29	person	person	NOUN
cana-1019	66	30	classifiers	classifier	NOUN
cana-1019	66	31	.	.	PUNCT
cana-1019	67	1	gathering	gather	VERB
cana-1019	67	2	learning	learn	VERB
cana-1019	67	3	approaches	approach	NOUN
cana-1019	67	4	have	have	AUX
cana-1019	67	5	demonstrated	demonstrate	VERB
cana-1019	67	6	successful	successful	ADJ
cana-1019	67	7	in	in	ADP
cana-1019	67	8	dealing	deal	VERB
cana-1019	67	9	with	with	ADP
cana-1019	67	10	the	the	DET
cana-1019	67	11	differing	differ	VERB
cana-1019	67	12	qualities	quality	NOUN
cana-1019	67	13	and	and	CCONJ
cana-1019	67	14	complexity	complexity	NOUN
cana-1019	67	15	of	of	ADP
cana-1019	67	16	music	music	NOUN
cana-1019	67	17	information	information	NOUN
cana-1019	67	18	.	.	PUNCT
cana-1019	68	1	later	later	ADJ
cana-1019	68	2	progressions	progression	NOUN
cana-1019	68	3	in	in	ADP
cana-1019	68	4	hyperparameter	hyperparameter	NOUN
cana-1019	68	5	optimization	optimization	NOUN
cana-1019	68	6	have	have	AUX
cana-1019	68	7	assist	assist	VERB
cana-1019	68	8	refined	refine	VERB
cana-1019	68	9	the	the	DET
cana-1019	68	10	execution	execution	NOUN
cana-1019	68	11	of	of	ADP
cana-1019	68	12	machine	machine	NOUN
cana-1019	68	13	learning	learning	NOUN
cana-1019	68	14	models	model	NOUN
cana-1019	68	15	for	for	ADP
cana-1019	68	16	music	music	NOUN
cana-1019	68	17	temperament	temperament	NOUN
cana-1019	68	18	classification	classification	NOUN
cana-1019	68	19	.	.	PUNCT
cana-1019	69	1	strategies	strategy	NOUN
cana-1019	69	2	such	such	ADJ
cana-1019	69	3	as	as	ADP
cana-1019	69	4	framework	framework	NOUN
cana-1019	69	5	look	look	VERB
cana-1019	69	6	and	and	CCONJ
cana-1019	69	7	bayesian	bayesian	NOUN
cana-1019	69	8	optimization	optimization	NOUN
cana-1019	69	9	empower	empower	NOUN
cana-1019	69	10	analysts	analyst	NOUN
cana-1019	69	11	to	to	PART
cana-1019	69	12	efficiently	efficiently	ADV
cana-1019	69	13	investigate	investigate	VERB
cana-1019	69	14	the	the	DET
cana-1019	69	15	parameter	parameter	NOUN
cana-1019	69	16	space	space	NOUN
cana-1019	69	17	and	and	CCONJ
cana-1019	69	18	recognize	recognize	VERB
cana-1019	69	19	ideal	ideal	ADJ
cana-1019	69	20	arrangements	arrangement	NOUN
cana-1019	69	21	[	[	X
cana-1019	69	22	11	11	NUM
cana-1019	69	23	]	]	PUNCT
cana-1019	69	24	.	.	PUNCT
cana-1019	70	1	fine	fine	ADJ
cana-1019	70	2	-	-	PUNCT
cana-1019	70	3	tuning	tune	VERB
cana-1019	70	4	hyperparameters	hyperparameter	NOUN
cana-1019	70	5	upgrades	upgrade	NOUN
cana-1019	70	6	demonstrate	demonstrate	VERB
cana-1019	70	7	generalization	generalization	NOUN
cana-1019	70	8	and	and	CCONJ
cana-1019	70	9	strength	strength	NOUN
cana-1019	70	10	,	,	PUNCT
cana-1019	70	11	driving	drive	VERB
cana-1019	70	12	to	to	PART
cana-1019	70	13	moved	move	VERB
cana-1019	70	14	forward	forward	ADV
cana-1019	70	15	precision	precision	NOUN
cana-1019	70	16	in	in	ADP
cana-1019	70	17	temperament	temperament	NOUN
cana-1019	70	18	forecast	forecast	NOUN
cana-1019	70	19	assignments	assignment	NOUN
cana-1019	70	20	.	.	PUNCT
cana-1019	71	1	assessment	assessment	NOUN
cana-1019	71	2	measurements	measurement	NOUN
cana-1019	71	3	in	in	ADP
cana-1019	71	4	music	music	NOUN
cana-1019	71	5	temperament	temperament	NOUN
cana-1019	71	6	classification	classification	NOUN
cana-1019	71	7	include	include	VERB
cana-1019	71	8	precision	precision	NOUN
cana-1019	71	9	,	,	PUNCT
cana-1019	71	10	exactness	exactness	NOUN
cana-1019	71	11	,	,	PUNCT
cana-1019	71	12	review	review	NOUN
cana-1019	71	13	,	,	PUNCT
cana-1019	71	14	and	and	CCONJ
cana-1019	71	15	f1	f1	NOUN
cana-1019	71	16	-	-	PUNCT
cana-1019	71	17	score	score	NOUN
cana-1019	71	18	,	,	PUNCT
cana-1019	71	19	giving	give	VERB
cana-1019	71	20	comprehensive	comprehensive	ADJ
cana-1019	71	21	experiences	experience	NOUN
cana-1019	71	22	into	into	ADP
cana-1019	71	23	demonstrate	demonstrate	NOUN
cana-1019	71	24	execution	execution	NOUN
cana-1019	71	25	over	over	ADP
cana-1019	71	26	distinctive	distinctive	ADJ
cana-1019	71	27	temperament	temperament	NOUN
cana-1019	71	28	categories	category	NOUN
cana-1019	72	1	[	[	X
cana-1019	72	2	12	12	NUM
cana-1019	72	3	]	]	PUNCT
cana-1019	72	4	.	.	PUNCT
cana-1019	73	1	cross	cross	ADJ
cana-1019	73	2	-	-	ADJ
cana-1019	73	3	validation	validation	ADJ
cana-1019	73	4	procedures	procedure	NOUN
cana-1019	73	5	such	such	ADJ
cana-1019	73	6	as	as	ADP
cana-1019	73	7	k	k	ADJ
cana-1019	73	8	-	-	ADJ
cana-1019	73	9	fold	fold	ADJ
cana-1019	73	10	cross	cross	ADJ
cana-1019	73	11	-	-	ADJ
cana-1019	73	12	validation	validation	ADJ
cana-1019	73	13	guarantee	guarantee	NOUN
cana-1019	73	14	thorough	thorough	ADJ
cana-1019	73	15	assessment	assessment	NOUN
cana-1019	73	16	and	and	CCONJ
cana-1019	73	17	approval	approval	NOUN
cana-1019	73	18	of	of	ADP
cana-1019	73	19	show	show	NOUN
cana-1019	73	20	execution	execution	NOUN
cana-1019	73	21	on	on	ADP
cana-1019	73	22	assorted	assorted	ADJ
cana-1019	73	23	subsets	subset	NOUN
cana-1019	73	24	of	of	ADP
cana-1019	73	25	information	information	NOUN
cana-1019	73	26	,	,	PUNCT
cana-1019	73	27	guaranteeing	guarantee	VERB
cana-1019	73	28	unwavering	unwavering	ADJ
cana-1019	73	29	quality	quality	NOUN
cana-1019	73	30	and	and	CCONJ
cana-1019	73	31	generalizability	generalizability	NOUN
cana-1019	73	32	[	[	X
cana-1019	73	33	13	13	NUM
cana-1019	73	34	]	]	PUNCT
cana-1019	73	35	.	.	PUNCT
cana-1019	74	1	profound	profound	ADJ
cana-1019	74	2	learning	learn	VERB
cana-1019	74	3	designs	design	NOUN
cana-1019	74	4	proceed	proceed	VERB
cana-1019	74	5	to	to	PART
cana-1019	74	6	thrust	thrust	VERB
cana-1019	74	7	the	the	DET
cana-1019	74	8	boundaries	boundary	NOUN
cana-1019	74	9	of	of	ADP
cana-1019	74	10	music	music	NOUN
cana-1019	74	11	disposition	disposition	NOUN
cana-1019	74	12	classification	classification	NOUN
cana-1019	74	13	,	,	PUNCT
cana-1019	74	14	with	with	ADP
cana-1019	74	15	repetitive	repetitive	ADJ
cana-1019	74	16	neural	neural	ADJ
cana-1019	74	17	systems	system	NOUN
cana-1019	74	18	(	(	PUNCT
cana-1019	74	19	rnns	rnns	PROPN
cana-1019	74	20	)	)	PUNCT
cana-1019	74	21	and	and	CCONJ
cana-1019	74	22	long	long	ADJ
cana-1019	74	23	short	short	ADJ
cana-1019	74	24	-	-	PUNCT
cana-1019	74	25	term	term	NOUN
cana-1019	74	26	memory	memory	NOUN
cana-1019	74	27	(	(	PUNCT
cana-1019	74	28	lstm	lstm	NOUN
cana-1019	74	29	)	)	PUNCT
cana-1019	74	30	systems	system	NOUN
cana-1019	74	31	proficient	proficient	ADJ
cana-1019	74	32	at	at	ADP
cana-1019	74	33	capturing	capture	VERB
cana-1019	74	34	transient	transient	ADJ
cana-1019	74	35	conditions	condition	NOUN
cana-1019	74	36	and	and	CCONJ
cana-1019	74	37	consecutive	consecutive	ADJ
cana-1019	74	38	designs	design	NOUN
cana-1019	74	39	in	in	ADP
cana-1019	74	40	music	music	NOUN
cana-1019	74	41	information	information	NOUN
cana-1019	74	42	[	[	X
cana-1019	74	43	14	14	NUM
cana-1019	74	44	]	]	PUNCT
cana-1019	74	45	.	.	PUNCT
cana-1019	75	1	these	these	DET
cana-1019	75	2	models	model	NOUN
cana-1019	75	3	exceed	exceed	VERB
cana-1019	75	4	expectations	expectation	NOUN
cana-1019	75	5	in	in	ADP
cana-1019	75	6	handling	handle	VERB
cana-1019	75	7	consecutive	consecutive	ADJ
cana-1019	75	8	information	information	NOUN
cana-1019	75	9	such	such	ADJ
cana-1019	75	10	as	as	ADP
cana-1019	75	11	music	music	NOUN
cana-1019	75	12	sound	sound	ADJ
cana-1019	75	13	streams	stream	NOUN
cana-1019	75	14	and	and	CCONJ
cana-1019	75	15	expressive	expressive	ADJ
cana-1019	75	16	groupings	grouping	NOUN
cana-1019	75	17	,	,	PUNCT
cana-1019	75	18	advertising	advertising	NOUN
cana-1019	75	19	upgraded	upgrade	VERB
cana-1019	75	20	precision	precision	NOUN
cana-1019	75	21	and	and	CCONJ
cana-1019	75	22	expressive	expressive	ADJ
cana-1019	75	23	control	control	NOUN
cana-1019	75	24	in	in	ADP
cana-1019	75	25	temperament	temperament	NOUN
cana-1019	75	26	classification	classification	NOUN
cana-1019	75	27	frameworks	framework	NOUN
cana-1019	75	28	.	.	PUNCT
cana-1019	76	1	moreover	moreover	ADV
cana-1019	76	2	,	,	PUNCT
cana-1019	76	3	multimodal	multimodal	ADJ
cana-1019	76	4	learning	learning	NOUN
cana-1019	76	5	approaches	approach	NOUN
cana-1019	76	6	have	have	AUX
cana-1019	76	7	gained	gain	VERB
cana-1019	76	8	footing	foot	VERB
cana-1019	76	9	by	by	ADP
cana-1019	76	10	joining	join	VERB
cana-1019	76	11	data	datum	NOUN
cana-1019	76	12	from	from	ADP
cana-1019	76	13	numerous	numerous	ADJ
cana-1019	76	14	modalities	modality	NOUN
cana-1019	76	15	,	,	PUNCT
cana-1019	76	16	counting	count	VERB
cana-1019	76	17	sound	sound	NOUN
cana-1019	76	18	,	,	PUNCT
cana-1019	76	19	verses	verse	NOUN
cana-1019	76	20	,	,	PUNCT
cana-1019	76	21	and	and	CCONJ
cana-1019	76	22	metadata	metadata	NOUN
cana-1019	76	23	.	.	PUNCT
cana-1019	77	1	by	by	ADP
cana-1019	77	2	leveraging	leverage	VERB
cana-1019	77	3	complementary	complementary	ADJ
cana-1019	77	4	prompts	prompt	NOUN
cana-1019	77	5	from	from	ADP
cana-1019	77	6	distinctive	distinctive	ADJ
cana-1019	77	7	modalities	modality	NOUN
cana-1019	77	8	,	,	PUNCT
cana-1019	77	9	multimodal	multimodal	NOUN
cana-1019	77	10	models	model	NOUN
cana-1019	77	11	improve	improve	VERB
cana-1019	77	12	the	the	DET
cana-1019	77	13	strength	strength	NOUN
cana-1019	77	14	and	and	CCONJ
cana-1019	77	15	precision	precision	NOUN
cana-1019	77	16	of	of	ADP
cana-1019	77	17	disposition	disposition	NOUN
cana-1019	77	18	classification	classification	NOUN
cana-1019	77	19	expectations	expectation	NOUN
cana-1019	77	20	[	[	X
cana-1019	77	21	15	15	NUM
cana-1019	77	22	]	]	PUNCT
cana-1019	77	23	.	.	PUNCT
cana-1019	78	1	these	these	DET
cana-1019	78	2	approaches	approach	NOUN
cana-1019	78	3	emphasize	emphasize	VERB
cana-1019	78	4	the	the	DET
cana-1019	78	5	significance	significance	NOUN
cana-1019	78	6	of	of	ADP
cana-1019	78	7	coordination	coordination	NOUN
cana-1019	78	8	differing	differ	VERB
cana-1019	78	9	sources	source	NOUN
cana-1019	78	10	of	of	ADP
cana-1019	78	11	data	datum	NOUN
cana-1019	78	12	to	to	PART
cana-1019	78	13	attain	attain	VERB
cana-1019	78	14	a	a	DET
cana-1019	78	15	all	all	DET
cana-1019	78	16	encompassing	encompass	VERB
cana-1019	78	17	understanding	understanding	NOUN
cana-1019	78	18	of	of	ADP
cana-1019	78	19	music	music	NOUN
cana-1019	78	20	substance	substance	NOUN
cana-1019	78	21	and	and	CCONJ
cana-1019	78	22	setting	setting	NOUN
cana-1019	78	23	.	.	PUNCT
cana-1019	79	1	in	in	ADP
cana-1019	79	2	rundown	rundown	NOUN
cana-1019	79	3	,	,	PUNCT
cana-1019	79	4	the	the	DET
cana-1019	79	5	field	field	NOUN
cana-1019	79	6	of	of	ADP
cana-1019	79	7	music	music	NOUN
cana-1019	79	8	temperament	temperament	NOUN
cana-1019	79	9	classification	classification	NOUN
cana-1019	79	10	has	have	AUX
cana-1019	79	11	advanced	advance	VERB
cana-1019	79	12	altogether	altogether	ADV
cana-1019	79	13	through	through	ADP
cana-1019	79	14	the	the	DET
cana-1019	79	15	selection	selection	NOUN
cana-1019	79	16	of	of	ADP
cana-1019	79	17	machine	machine	NOUN
cana-1019	79	18	learning	learning	NOUN
cana-1019	79	19	,	,	PUNCT
cana-1019	79	20	profound	profound	ADJ
cana-1019	79	21	learning	learning	NOUN
cana-1019	79	22	,	,	PUNCT
cana-1019	79	23	and	and	CCONJ
cana-1019	79	24	multimodal	multimodal	NOUN
cana-1019	79	25	approaches	approach	NOUN
cana-1019	79	26	.	.	PUNCT
cana-1019	80	1	analysts	analyst	NOUN
cana-1019	80	2	proceed	proceed	VERB
cana-1019	80	3	to	to	PART
cana-1019	80	4	investigate	investigate	VERB
cana-1019	80	5	novel	novel	ADJ
cana-1019	80	6	techniques	technique	NOUN
cana-1019	80	7	and	and	CCONJ
cana-1019	80	8	refine	refine	VERB
cana-1019	80	9	existing	exist	VERB
cana-1019	80	10	systems	system	NOUN
cana-1019	80	11	to	to	PART
cana-1019	80	12	address	address	VERB
cana-1019	80	13	the	the	DET
cana-1019	80	14	complexities	complexity	NOUN
cana-1019	80	15	characteristic	characteristic	ADJ
cana-1019	80	16	in	in	ADP
cana-1019	80	17	music	music	NOUN
cana-1019	80	18	information	information	NOUN
cana-1019	80	19	investigation	investigation	NOUN
cana-1019	80	20	.	.	PUNCT
cana-1019	81	1	the	the	DET
cana-1019	81	2	integration	integration	NOUN
cana-1019	81	3	of	of	ADP
cana-1019	81	4	progressed	progress	VERB
cana-1019	81	5	include	include	VERB
cana-1019	81	6	extraction	extraction	NOUN
cana-1019	81	7	,	,	PUNCT
cana-1019	81	8	outfit	outfit	ADJ
cana-1019	81	9	learning	learning	NOUN
cana-1019	81	10	,	,	PUNCT
cana-1019	81	11	and	and	CCONJ
cana-1019	81	12	hyperparameter	hyperparameter	NOUN
cana-1019	81	13	optimization	optimization	NOUN
cana-1019	81	14	has	have	AUX
cana-1019	81	15	cleared	clear	VERB
cana-1019	81	16	the	the	DET
cana-1019	81	17	way	way	NOUN
cana-1019	81	18	for	for	ADP
cana-1019	81	19	more	more	ADV
cana-1019	81	20	precise	precise	ADJ
cana-1019	81	21	and	and	CCONJ
cana-1019	81	22	personalized	personalized	ADJ
cana-1019	81	23	music	music	NOUN
cana-1019	81	24	disposition	disposition	NOUN
cana-1019	81	25	classification	classification	NOUN
cana-1019	81	26	frameworks	framework	NOUN
cana-1019	81	27	,	,	PUNCT
cana-1019	81	28	upgrading	upgrade	VERB
cana-1019	81	29	client	client	NOUN
cana-1019	81	30	encounters	encounter	NOUN
cana-1019	81	31	and	and	CCONJ
cana-1019	81	32	applications	application	NOUN
cana-1019	81	33	in	in	ADP
cana-1019	81	34	music	music	NOUN
cana-1019	81	35	suggestion	suggestion	NOUN
cana-1019	81	36	and	and	CCONJ
cana-1019	81	37	treatment	treatment	NOUN
cana-1019	82	1	[	[	X
cana-1019	82	2	16	16	NUM
cana-1019	82	3	]	]	PUNCT
cana-1019	82	4	.	.	PUNCT
cana-1019	83	1	communications	communication	NOUN
cana-1019	83	2	on	on	ADP
cana-1019	83	3	applied	apply	VERB
cana-1019	83	4	nonlinear	nonlinear	ADJ
cana-1019	83	5	analysis	analysis	NOUN
cana-1019	83	6	issn	issn	NOUN
cana-1019	83	7	:	:	PUNCT
cana-1019	83	8	1074	1074	NUM
cana-1019	83	9	-	-	PUNCT
cana-1019	83	10	133x	133x	NUM
cana-1019	83	11	vol	vol	NOUN
cana-1019	83	12	31	31	NUM
cana-1019	83	13	no	no	NOUN
cana-1019	83	14	.	.	PUNCT
cana-1019	84	1	5s	5s	NUM
cana-1019	84	2	(	(	PUNCT
cana-1019	84	3	2024	2024	NUM
cana-1019	84	4	)	)	PUNCT
cana-1019	84	5	237	237	NUM
cana-1019	84	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1019	84	7	table	table	NOUN
cana-1019	84	8	1	1	NUM
cana-1019	84	9	:	:	PUNCT
cana-1019	84	10	summary	summary	NOUN
cana-1019	84	11	of	of	ADP
cana-1019	84	12	related	related	ADJ
cana-1019	84	13	work	work	NOUN
cana-1019	84	14	approach	approach	NOUN
cana-1019	84	15	algorithm	algorithm	NOUN
cana-1019	84	16	methodology	methodology	NOUN
cana-1019	84	17	key	key	ADJ
cana-1019	84	18	finding	finding	NOUN
cana-1019	84	19	application	application	NOUN
cana-1019	84	20	traditional	traditional	ADJ
cana-1019	84	21	heuristic	heuristic	ADJ
cana-1019	84	22	methods	method	NOUN
cana-1019	84	23	[	[	X
cana-1019	84	24	17	17	NUM
cana-1019	84	25	]	]	PUNCT
cana-1019	84	26	manual	manual	ADJ
cana-1019	84	27	annotation	annotation	NOUN
cana-1019	84	28	,	,	PUNCT
cana-1019	84	29	rule	rule	NOUN
cana-1019	84	30	-	-	PUNCT
cana-1019	84	31	based	base	VERB
cana-1019	84	32	classification	classification	NOUN
cana-1019	84	33	subjectivity	subjectivity	NOUN
cana-1019	84	34	and	and	CCONJ
cana-1019	84	35	scalability	scalability	NOUN
cana-1019	84	36	limitations	limitation	NOUN
cana-1019	84	37	;	;	PUNCT
cana-1019	84	38	basic	basic	ADJ
cana-1019	84	39	mood	mood	NOUN
cana-1019	84	40	tagging	tag	VERB
cana-1019	84	41	initial	initial	ADJ
cana-1019	84	42	music	music	NOUN
cana-1019	84	43	categorization	categorization	NOUN
cana-1019	84	44	machine	machine	NOUN
cana-1019	84	45	learning	learn	VERB
cana-1019	84	46	svm	svm	PROPN
cana-1019	84	47	[	[	X
cana-1019	84	48	18	18	NUM
cana-1019	84	49	]	]	PUNCT
cana-1019	84	50	feature	feature	NOUN
cana-1019	84	51	extraction	extraction	NOUN
cana-1019	84	52	(	(	PUNCT
cana-1019	84	53	e.g.	e.g.	ADV
cana-1019	84	54	,	,	PUNCT
cana-1019	84	55	mfccs	mfccs	ADJ
cana-1019	84	56	)	)	PUNCT
cana-1019	84	57	,	,	PUNCT
cana-1019	84	58	supervised	supervise	VERB
cana-1019	84	59	learning	learning	NOUN
cana-1019	84	60	captures	capture	VERB
cana-1019	84	61	nonlinear	nonlinear	ADJ
cana-1019	84	62	relationships	relationship	NOUN
cana-1019	84	63	in	in	ADP
cana-1019	84	64	audio	audio	ADJ
cana-1019	84	65	features	feature	NOUN
cana-1019	84	66	;	;	PUNCT
cana-1019	84	67	improves	improve	VERB
cana-1019	84	68	classification	classification	NOUN
cana-1019	84	69	accuracy	accuracy	NOUN
cana-1019	84	70	automated	automate	VERB
cana-1019	84	71	mood	mood	NOUN
cana-1019	84	72	tagging	tagging	NOUN
cana-1019	84	73	,	,	PUNCT
cana-1019	84	74	recommendation	recommendation	NOUN
cana-1019	84	75	systems	system	NOUN
cana-1019	84	76	deep	deep	ADV
cana-1019	84	77	learning	learn	VERB
cana-1019	84	78	cnn	cnn	PROPN
cana-1019	85	1	[	[	X
cana-1019	85	2	19	19	NUM
cana-1019	85	3	]	]	X
cana-1019	85	4	spectrogram	spectrogram	NOUN
cana-1019	85	5	processing	processing	NOUN
cana-1019	85	6	,	,	PUNCT
cana-1019	85	7	hierarchical	hierarchical	ADJ
cana-1019	85	8	feature	feature	NOUN
cana-1019	85	9	extraction	extraction	NOUN
cana-1019	85	10	efficient	efficient	ADJ
cana-1019	85	11	feature	feature	NOUN
cana-1019	85	12	learning	learn	VERB
cana-1019	85	13	from	from	ADP
cana-1019	85	14	audio	audio	ADJ
cana-1019	85	15	signals	signal	NOUN
cana-1019	85	16	;	;	PUNCT
cana-1019	85	17	competitive	competitive	ADJ
cana-1019	85	18	performance	performance	NOUN
cana-1019	85	19	in	in	ADP
cana-1019	85	20	mood	mood	NOUN
cana-1019	85	21	prediction	prediction	NOUN
cana-1019	85	22	high	high	ADV
cana-1019	85	23	-	-	PUNCT
cana-1019	85	24	dimensional	dimensional	ADJ
cana-1019	85	25	audio	audio	ADJ
cana-1019	85	26	data	datum	NOUN
cana-1019	85	27	analysis	analysis	NOUN
cana-1019	85	28	,	,	PUNCT
cana-1019	85	29	real	real	ADJ
cana-1019	85	30	-	-	PUNCT
cana-1019	85	31	time	time	NOUN
cana-1019	85	32	mood	mood	NOUN
cana-1019	85	33	classification	classification	NOUN
cana-1019	85	34	textual	textual	ADJ
cana-1019	85	35	analysis	analysis	NOUN
cana-1019	85	36	nlp	nlp	ADJ
cana-1019	85	37	[	[	X
cana-1019	85	38	20	20	NUM
cana-1019	85	39	]	]	PUNCT
cana-1019	85	40	sentiment	sentiment	NOUN
cana-1019	85	41	analysis	analysis	NOUN
cana-1019	85	42	,	,	PUNCT
cana-1019	85	43	thematic	thematic	ADJ
cana-1019	85	44	extraction	extraction	NOUN
cana-1019	85	45	enhances	enhance	VERB
cana-1019	85	46	contextual	contextual	ADJ
cana-1019	85	47	understanding	understanding	NOUN
cana-1019	85	48	of	of	ADP
cana-1019	85	49	lyrics	lyric	NOUN
cana-1019	85	50	;	;	PUNCT
cana-1019	85	51	improves	improve	VERB
cana-1019	85	52	accuracy	accuracy	NOUN
cana-1019	85	53	when	when	SCONJ
cana-1019	85	54	combined	combine	VERB
cana-1019	85	55	with	with	ADP
cana-1019	85	56	audio	audio	ADJ
cana-1019	85	57	features	feature	NOUN
cana-1019	85	58	lyrics	lyric	NOUN
cana-1019	85	59	-	-	PUNCT
cana-1019	85	60	based	base	VERB
cana-1019	85	61	mood	mood	NOUN
cana-1019	85	62	classification	classification	NOUN
cana-1019	85	63	,	,	PUNCT
cana-1019	85	64	integration	integration	NOUN
cana-1019	85	65	with	with	ADP
cana-1019	85	66	audiobased	audiobase	VERB
cana-1019	85	67	models	model	NOUN
cana-1019	85	68	ensemble	ensemble	ADJ
cana-1019	85	69	methods	method	NOUN
cana-1019	85	70	[	[	X
cana-1019	85	71	21	21	NUM
cana-1019	85	72	]	]	X
cana-1019	85	73	svm	svm	PROPN
cana-1019	85	74	+	+	CCONJ
cana-1019	85	75	decision	decision	NOUN
cana-1019	85	76	trees	tree	NOUN
cana-1019	85	77	hybridization	hybridization	NOUN
cana-1019	85	78	,	,	PUNCT
cana-1019	85	79	combination	combination	NOUN
cana-1019	85	80	of	of	ADP
cana-1019	85	81	classifiers	classifier	NOUN
cana-1019	85	82	improves	improve	VERB
cana-1019	85	83	predictive	predictive	ADJ
cana-1019	85	84	performance	performance	NOUN
cana-1019	85	85	and	and	CCONJ
cana-1019	85	86	robustness	robustness	NOUN
cana-1019	85	87	;	;	PUNCT
cana-1019	86	1	synergistic	synergistic	ADJ
cana-1019	86	2	effects	effect	NOUN
cana-1019	86	3	of	of	ADP
cana-1019	86	4	different	different	ADJ
cana-1019	86	5	algorithms	algorithm	NOUN
cana-1019	86	6	robust	robust	ADJ
cana-1019	86	7	mood	mood	NOUN
cana-1019	86	8	prediction	prediction	NOUN
cana-1019	86	9	models	model	NOUN
cana-1019	86	10	,	,	PUNCT
cana-1019	86	11	handling	handle	VERB
cana-1019	86	12	diverse	diverse	ADJ
cana-1019	86	13	music	music	NOUN
cana-1019	86	14	datasets	dataset	NOUN
cana-1019	86	15	user	user	NOUN
cana-1019	86	16	-	-	PUNCT
cana-1019	86	17	centric	centric	ADJ
cana-1019	86	18	collaborative	collaborative	ADJ
cana-1019	86	19	filtering	filtering	NOUN
cana-1019	87	1	[	[	X
cana-1019	87	2	22	22	NUM
cana-1019	87	3	]	]	PUNCT
cana-1019	87	4	user	user	NOUN
cana-1019	87	5	interaction	interaction	NOUN
cana-1019	87	6	data	datum	NOUN
cana-1019	87	7	,	,	PUNCT
cana-1019	87	8	preference	preference	NOUN
cana-1019	87	9	modeling	modeling	NOUN
cana-1019	87	10	personalizes	personalize	VERB
cana-1019	87	11	mood	mood	NOUN
cana-1019	87	12	recommendations	recommendation	NOUN
cana-1019	87	13	based	base	VERB
cana-1019	87	14	on	on	ADP
cana-1019	87	15	user	user	NOUN
cana-1019	87	16	preferences	preference	NOUN
cana-1019	87	17	;	;	PUNCT
cana-1019	87	18	enhances	enhance	VERB
cana-1019	87	19	user	user	NOUN
cana-1019	87	20	engagement	engagement	NOUN
cana-1019	87	21	personalized	personalize	VERB
cana-1019	87	22	music	music	NOUN
cana-1019	87	23	recommendation	recommendation	NOUN
cana-1019	87	24	systems	system	NOUN
cana-1019	87	25	,	,	PUNCT
cana-1019	87	26	user	user	NOUN
cana-1019	87	27	-	-	PUNCT
cana-1019	87	28	driven	drive	VERB
cana-1019	87	29	music	music	NOUN
cana-1019	87	30	therapy	therapy	NOUN
cana-1019	87	31	hyperparameter	hyperparameter	NOUN
cana-1019	87	32	grid	grid	NOUN
cana-1019	87	33	search	search	NOUN
cana-1019	87	34	,	,	PUNCT
cana-1019	87	35	bayesian	bayesian	NOUN
cana-1019	87	36	opt	opt	NOUN
cana-1019	87	37	.	.	PUNCT
cana-1019	88	1	[	[	X
cana-1019	88	2	23	23	NUM
cana-1019	88	3	]	]	X
cana-1019	88	4	systematic	systematic	ADJ
cana-1019	88	5	exploration	exploration	NOUN
cana-1019	88	6	of	of	ADP
cana-1019	88	7	parameter	parameter	NOUN
cana-1019	88	8	space	space	NOUN
cana-1019	88	9	optimizes	optimize	VERB
cana-1019	88	10	model	model	NOUN
cana-1019	88	11	performance	performance	NOUN
cana-1019	88	12	and	and	CCONJ
cana-1019	88	13	generalization	generalization	NOUN
cana-1019	88	14	;	;	PUNCT
cana-1019	88	15	fine	fine	ADJ
cana-1019	88	16	-	-	PUNCT
cana-1019	88	17	tuning	tuning	NOUN
cana-1019	88	18	enhances	enhance	NOUN
cana-1019	88	19	accuracy	accuracy	NOUN
cana-1019	88	20	enhancing	enhance	VERB
cana-1019	88	21	model	model	NOUN
cana-1019	88	22	robustness	robustness	NOUN
cana-1019	88	23	and	and	CCONJ
cana-1019	88	24	accuracy	accuracy	NOUN
cana-1019	88	25	in	in	ADP
cana-1019	88	26	mood	mood	NOUN
cana-1019	88	27	classification	classification	NOUN
cana-1019	88	28	sequential	sequential	ADJ
cana-1019	88	29	data	datum	NOUN
cana-1019	88	30	rnn	rnn	NOUN
cana-1019	88	31	,	,	PUNCT
cana-1019	88	32	lstm	lstm	ADJ
cana-1019	88	33	[	[	X
cana-1019	88	34	24	24	NUM
cana-1019	88	35	]	]	PUNCT
cana-1019	88	36	temporal	temporal	ADJ
cana-1019	88	37	dependencies	dependency	NOUN
cana-1019	88	38	,	,	PUNCT
cana-1019	88	39	sequential	sequential	ADJ
cana-1019	88	40	pattern	pattern	NOUN
cana-1019	88	41	learning	learn	VERB
cana-1019	88	42	captures	capture	VERB
cana-1019	88	43	temporal	temporal	ADJ
cana-1019	88	44	aspects	aspect	NOUN
cana-1019	88	45	in	in	ADP
cana-1019	88	46	music	music	NOUN
cana-1019	88	47	data	datum	NOUN
cana-1019	88	48	;	;	PUNCT
cana-1019	88	49	enhances	enhance	VERB
cana-1019	88	50	predictive	predictive	ADJ
cana-1019	88	51	power	power	NOUN
cana-1019	88	52	in	in	ADP
cana-1019	88	53	mood	mood	NOUN
cana-1019	88	54	classification	classification	NOUN
cana-1019	88	55	sequential	sequential	ADJ
cana-1019	88	56	music	music	NOUN
cana-1019	88	57	analysis	analysis	NOUN
cana-1019	88	58	,	,	PUNCT
cana-1019	89	1	dynamic	dynamic	ADJ
cana-1019	89	2	mood	mood	NOUN
cana-1019	89	3	tracking	track	VERB
cana-1019	89	4	multimodal	multimodal	ADJ
cana-1019	89	5	fusion	fusion	NOUN
cana-1019	89	6	of	of	ADP
cana-1019	89	7	audio	audio	NOUN
cana-1019	89	8	and	and	CCONJ
cana-1019	89	9	text	text	NOUN
cana-1019	90	1	[	[	X
cana-1019	90	2	25	25	NUM
cana-1019	90	3	]	]	PUNCT
cana-1019	90	4	integration	integration	NOUN
cana-1019	90	5	of	of	ADP
cana-1019	90	6	multiple	multiple	ADJ
cana-1019	90	7	modalities	modality	NOUN
cana-1019	90	8	,	,	PUNCT
cana-1019	90	9	complementary	complementary	ADJ
cana-1019	90	10	cues	cue	NOUN
cana-1019	90	11	improves	improve	VERB
cana-1019	90	12	robustness	robustness	NOUN
cana-1019	90	13	and	and	CCONJ
cana-1019	90	14	accuracy	accuracy	NOUN
cana-1019	90	15	;	;	PUNCT
cana-1019	90	16	holistic	holistic	ADJ
cana-1019	90	17	understanding	understanding	NOUN
cana-1019	90	18	of	of	ADP
cana-1019	90	19	music	music	NOUN
cana-1019	90	20	content	content	NOUN
cana-1019	90	21	and	and	CCONJ
cana-1019	90	22	context	context	NOUN
cana-1019	90	23	enhanced	enhanced	ADJ
cana-1019	90	24	music	music	NOUN
cana-1019	90	25	mood	mood	NOUN
cana-1019	90	26	prediction	prediction	NOUN
cana-1019	90	27	,	,	PUNCT
cana-1019	90	28	comprehensive	comprehensive	ADJ
cana-1019	90	29	music	music	NOUN
cana-1019	90	30	information	information	NOUN
cana-1019	90	31	retrieval	retrieval	NOUN
cana-1019	90	32	real	real	ADJ
cana-1019	90	33	-	-	PUNCT
cana-1019	90	34	world	world	NOUN
cana-1019	90	35	apps	app	NOUN
cana-1019	90	36	music	music	NOUN
cana-1019	90	37	recommendation	recommendation	NOUN
cana-1019	90	38	systems	system	NOUN
cana-1019	90	39	[	[	X
cana-1019	90	40	26	26	NUM
cana-1019	90	41	]	]	PUNCT
cana-1019	90	42	integration	integration	NOUN
cana-1019	90	43	into	into	ADP
cana-1019	90	44	practical	practical	ADJ
cana-1019	90	45	applications	application	NOUN
cana-1019	90	46	enhances	enhance	VERB
cana-1019	90	47	user	user	NOUN
cana-1019	90	48	experiences	experience	NOUN
cana-1019	90	49	;	;	PUNCT
cana-1019	90	50	supports	support	VERB
cana-1019	90	51	diverse	diverse	ADJ
cana-1019	90	52	musicrelated	musicrelate	VERB
cana-1019	90	53	services	service	NOUN
cana-1019	90	54	and	and	CCONJ
cana-1019	90	55	applications	application	NOUN
cana-1019	90	56	personalized	personalize	VERB
cana-1019	90	57	music	music	NOUN
cana-1019	90	58	services	service	NOUN
cana-1019	90	59	,	,	PUNCT
cana-1019	90	60	therapeutic	therapeutic	ADJ
cana-1019	90	61	music	music	NOUN
cana-1019	90	62	applications	application	NOUN
cana-1019	90	63	3	3	NUM
cana-1019	90	64	.	.	PUNCT
cana-1019	91	1	dataset	dataset	ADJ
cana-1019	91	2	description	description	NOUN
cana-1019	91	3	the	the	DET
cana-1019	91	4	presentation	presentation	NOUN
cana-1019	91	5	of	of	ADP
cana-1019	91	6	a	a	DET
cana-1019	91	7	unused	unused	ADJ
cana-1019	91	8	multi	multi	ADJ
cana-1019	91	9	-	-	ADJ
cana-1019	91	10	modal	modal	ADJ
cana-1019	91	11	feeling	feeling	NOUN
cana-1019	91	12	dataset	dataset	VERB
cana-1019	91	13	for	for	ADP
cana-1019	91	14	music	music	NOUN
cana-1019	91	15	,	,	PUNCT
cana-1019	91	16	associated	associate	VERB
cana-1019	91	17	to	to	ADP
cana-1019	91	18	mirex	mirex	PROPN
cana-1019	91	19	benchmarks	benchmark	NOUN
cana-1019	91	20	,	,	PUNCT
cana-1019	91	21	marks	mark	VERB
cana-1019	91	22	a	a	DET
cana-1019	91	23	noteworthy	noteworthy	ADJ
cana-1019	91	24	headway	headway	NOUN
cana-1019	91	25	in	in	ADP
cana-1019	91	26	music	music	NOUN
cana-1019	91	27	feeling	feel	VERB
cana-1019	91	28	classification	classification	NOUN
cana-1019	91	29	inquire	inquire	VERB
cana-1019	91	30	about	about	ADP
cana-1019	91	31	.	.	PUNCT
cana-1019	92	1	this	this	DET
cana-1019	92	2	dataset	dataset	NOUN
cana-1019	92	3	comprises	comprise	VERB
cana-1019	92	4	903	903	NUM
cana-1019	92	5	sound	sound	ADJ
cana-1019	92	6	clips	clip	NOUN
cana-1019	92	7	,	,	PUNCT
cana-1019	92	8	each	each	DET
cana-1019	92	9	30	30	NUM
cana-1019	92	10	seconds	second	NOUN
cana-1019	92	11	long	long	ADV
cana-1019	92	12	,	,	PUNCT
cana-1019	92	13	organized	organize	VERB
cana-1019	92	14	into	into	ADP
cana-1019	92	15	clusters	cluster	NOUN
cana-1019	92	16	and	and	CCONJ
cana-1019	92	17	subfolders	subfolder	NOUN
cana-1019	92	18	based	base	VERB
cana-1019	92	19	on	on	ADP
cana-1019	92	20	communications	communication	NOUN
cana-1019	92	21	on	on	ADP
cana-1019	92	22	applied	apply	VERB
cana-1019	92	23	nonlinear	nonlinear	ADJ
cana-1019	92	24	analysis	analysis	NOUN
cana-1019	92	25	issn	issn	NOUN
cana-1019	92	26	:	:	PUNCT
cana-1019	92	27	1074	1074	NUM
cana-1019	92	28	-	-	PUNCT
cana-1019	92	29	133x	133x	NUM
cana-1019	92	30	vol	vol	NOUN
cana-1019	92	31	31	31	NUM
cana-1019	92	32	no	no	NOUN
cana-1019	92	33	.	.	PUNCT
cana-1019	93	1	5s	5s	NUM
cana-1019	93	2	(	(	PUNCT
cana-1019	93	3	2024	2024	NUM
cana-1019	93	4	)	)	PUNCT
cana-1019	93	5	238	238	NUM
cana-1019	93	6	https://internationalpubls.com	https://internationalpubls.com	NOUN
cana-1019	93	7	passionate	passionate	ADJ
cana-1019	93	8	names	name	NOUN
cana-1019	93	9	.	.	PUNCT
cana-1019	94	1	moreover	moreover	ADV
cana-1019	94	2	,	,	PUNCT
cana-1019	94	3	it	it	PRON
cana-1019	94	4	incorporates	incorporate	VERB
cana-1019	94	5	764	764	NUM
cana-1019	94	6	verse	verse	NOUN
cana-1019	94	7	records	record	NOUN
cana-1019	94	8	in	in	ADP
cana-1019	94	9	content	content	NOUN
cana-1019	94	10	arrange	arrange	NOUN
cana-1019	94	11	and	and	CCONJ
cana-1019	94	12	196	196	NUM
cana-1019	94	13	midi	midi	NOUN
cana-1019	94	14	records	record	NOUN
cana-1019	94	15	,	,	PUNCT
cana-1019	94	16	making	make	VERB
cana-1019	94	17	it	it	PRON
cana-1019	94	18	the	the	DET
cana-1019	94	19	primary	primary	NOUN
cana-1019	94	20	of	of	ADP
cana-1019	94	21	its	its	PRON
cana-1019	94	22	kind	kind	NOUN
cana-1019	94	23	to	to	PART
cana-1019	94	24	consolidate	consolidate	VERB
cana-1019	94	25	these	these	DET
cana-1019	94	26	three	three	NUM
cana-1019	94	27	unmistakable	unmistakable	ADJ
cana-1019	94	28	sources	source	NOUN
cana-1019	94	29	—	—	PUNCT
cana-1019	94	30	audio	audio	NOUN
cana-1019	94	31	,	,	PUNCT
cana-1019	94	32	verses	verse	NOUN
cana-1019	94	33	,	,	PUNCT
cana-1019	94	34	and	and	CCONJ
cana-1019	94	35	midi	midi	PROPN
cana-1019	94	36	.	.	PUNCT
cana-1019	95	1	the	the	DET
cana-1019	95	2	overview	overview	NOUN
cana-1019	95	3	of	of	ADP
cana-1019	95	4	dataset	dataset	NOUN
cana-1019	95	5	and	and	CCONJ
cana-1019	95	6	its	its	PRON
cana-1019	95	7	feature	feature	NOUN
cana-1019	95	8	value	value	NOUN
cana-1019	95	9	illustrate	illustrate	VERB
cana-1019	95	10	in	in	ADP
cana-1019	95	11	figure	figure	NOUN
cana-1019	95	12	2	2	NUM
cana-1019	95	13	.	.	PUNCT
cana-1019	95	14	figure	figure	NOUN
cana-1019	95	15	2	2	NUM
cana-1019	95	16	:	:	PUNCT
cana-1019	95	17	dataset	dataset	VERB
cana-1019	95	18	features	feature	NOUN
cana-1019	95	19	and	and	CCONJ
cana-1019	95	20	values	value	NOUN
cana-1019	95	21	this	this	DET
cana-1019	95	22	comprehensive	comprehensive	ADJ
cana-1019	95	23	dataset	dataset	NOUN
cana-1019	95	24	addresses	address	NOUN
cana-1019	95	25	the	the	DET
cana-1019	95	26	developing	develop	VERB
cana-1019	95	27	require	require	NOUN
cana-1019	95	28	for	for	ADP
cana-1019	95	29	multimodal	multimodal	ADJ
cana-1019	95	30	information	information	NOUN
cana-1019	95	31	in	in	ADP
cana-1019	95	32	music	music	NOUN
cana-1019	95	33	feeling	feel	VERB
cana-1019	95	34	classification	classification	NOUN
cana-1019	95	35	thinks	think	VERB
cana-1019	95	36	about	about	ADP
cana-1019	95	37	.	.	PUNCT
cana-1019	96	1	by	by	ADP
cana-1019	96	2	coordination	coordination	NOUN
cana-1019	96	3	sound	sound	NOUN
cana-1019	96	4	signals	signal	NOUN
cana-1019	96	5	,	,	PUNCT
cana-1019	96	6	expressive	expressive	ADJ
cana-1019	96	7	substance	substance	NOUN
cana-1019	96	8	,	,	PUNCT
cana-1019	96	9	and	and	CCONJ
cana-1019	96	10	melodic	melodic	ADJ
cana-1019	96	11	structure	structure	NOUN
cana-1019	96	12	(	(	PUNCT
cana-1019	96	13	spoken	speak	VERB
cana-1019	96	14	to	to	ADP
cana-1019	96	15	by	by	ADP
cana-1019	96	16	midi	midi	NOUN
cana-1019	96	17	records	record	NOUN
cana-1019	96	18	)	)	PUNCT
cana-1019	96	19	,	,	PUNCT
cana-1019	96	20	analysts	analyst	NOUN
cana-1019	96	21	pick	pick	VERB
cana-1019	96	22	up	up	ADP
cana-1019	96	23	a	a	DET
cana-1019	96	24	more	more	ADV
cana-1019	96	25	nuanced	nuanced	ADJ
cana-1019	96	26	understanding	understanding	NOUN
cana-1019	96	27	of	of	ADP
cana-1019	96	28	how	how	SCONJ
cana-1019	96	29	distinctive	distinctive	ADJ
cana-1019	96	30	modalities	modality	NOUN
cana-1019	96	31	contribute	contribute	VERB
cana-1019	96	32	to	to	ADP
cana-1019	96	33	enthusiastic	enthusiastic	ADJ
cana-1019	96	34	expression	expression	NOUN
cana-1019	96	35	in	in	ADP
cana-1019	96	36	music	music	NOUN
cana-1019	96	37	,	,	PUNCT
cana-1019	96	38	the	the	DET
cana-1019	96	39	different	different	ADJ
cana-1019	96	40	class	class	NOUN
cana-1019	96	41	distribution	distribution	NOUN
cana-1019	96	42	in	in	ADP
cana-1019	96	43	figure	figure	NOUN
cana-1019	96	44	3	3	NUM
cana-1019	96	45	.	.	PUNCT
cana-1019	97	1	such	such	ADJ
cana-1019	97	2	datasets	dataset	NOUN
cana-1019	97	3	are	be	AUX
cana-1019	97	4	pivotal	pivotal	ADJ
cana-1019	97	5	for	for	ADP
cana-1019	97	6	creating	create	VERB
cana-1019	97	7	and	and	CCONJ
cana-1019	97	8	approving	approve	VERB
cana-1019	97	9	machine	machine	NOUN
cana-1019	97	10	learning	learning	NOUN
cana-1019	97	11	models	model	NOUN
cana-1019	97	12	that	that	PRON
cana-1019	97	13	can	can	AUX
cana-1019	97	14	viably	viably	ADV
cana-1019	97	15	analyze	analyze	VERB
cana-1019	97	16	and	and	CCONJ
cana-1019	97	17	classify	classify	VERB
cana-1019	97	18	feelings	feeling	NOUN
cana-1019	97	19	in	in	ADP
cana-1019	97	20	music	music	NOUN
cana-1019	97	21	,	,	PUNCT
cana-1019	97	22	in	in	ADP
cana-1019	97	23	this	this	DET
cana-1019	97	24	manner	manner	NOUN
cana-1019	97	25	improving	improve	VERB
cana-1019	97	26	applications	application	NOUN
cana-1019	97	27	like	like	ADP
cana-1019	97	28	personalized	personalize	VERB
cana-1019	97	29	music	music	NOUN
cana-1019	97	30	suggestion	suggestion	NOUN
cana-1019	97	31	frameworks	framework	NOUN
cana-1019	97	32	and	and	CCONJ
cana-1019	97	33	music	music	NOUN
cana-1019	97	34	treatment	treatment	NOUN
cana-1019	97	35	intercessions	intercession	NOUN
cana-1019	97	36	.	.	PUNCT
cana-1019	98	1	figure	figure	VERB
cana-1019	98	2	3	3	NUM
cana-1019	98	3	:	:	PUNCT
cana-1019	98	4	distribution	distribution	NOUN
cana-1019	98	5	of	of	ADP
cana-1019	98	6	class	class	NOUN
cana-1019	98	7	communications	communication	NOUN
cana-1019	98	8	on	on	ADP
cana-1019	98	9	applied	apply	VERB
cana-1019	98	10	nonlinear	nonlinear	ADJ
cana-1019	98	11	analysis	analysis	NOUN
cana-1019	98	12	issn	issn	NOUN
cana-1019	98	13	:	:	PUNCT
cana-1019	98	14	1074	1074	NUM
cana-1019	98	15	-	-	PUNCT
cana-1019	98	16	133x	133x	NUM
cana-1019	98	17	vol	vol	NOUN
cana-1019	98	18	31	31	NUM
cana-1019	98	19	no	no	NOUN
cana-1019	98	20	.	.	PUNCT
cana-1019	99	1	5s	5s	NUM
cana-1019	99	2	(	(	PUNCT
cana-1019	99	3	2024	2024	NUM
cana-1019	99	4	)	)	PUNCT
cana-1019	99	5	239	239	NUM
cana-1019	99	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1019	99	7	4	4	NUM
cana-1019	99	8	.	.	X
cana-1019	99	9	methodology	methodology	NOUN
cana-1019	99	10	a.	a.	NOUN
cana-1019	99	11	data	data	PROPN
cana-1019	99	12	input	input	NOUN
cana-1019	99	13	and	and	CCONJ
cana-1019	99	14	pre	pre	NOUN
cana-1019	99	15	-	-	NOUN
cana-1019	99	16	process	process	NOUN
cana-1019	99	17	the	the	DET
cana-1019	99	18	data	datum	NOUN
cana-1019	99	19	:	:	PUNCT
cana-1019	99	20	dataset	dataset	ADJ
cana-1019	99	21	preparation	preparation	NOUN
cana-1019	99	22	is	be	AUX
cana-1019	99	23	an	an	DET
cana-1019	99	24	important	important	ADJ
cana-1019	99	25	step	step	NOUN
cana-1019	99	26	in	in	ADP
cana-1019	99	27	getting	get	VERB
cana-1019	99	28	data	datum	NOUN
cana-1019	99	29	ready	ready	ADJ
cana-1019	99	30	for	for	ADP
cana-1019	99	31	machine	machine	NOUN
cana-1019	99	32	learning	learn	VERB
cana-1019	99	33	jobs	job	NOUN
cana-1019	99	34	like	like	ADP
cana-1019	99	35	figuring	figure	VERB
cana-1019	99	36	out	out	ADP
cana-1019	99	37	how	how	SCONJ
cana-1019	99	38	music	music	NOUN
cana-1019	99	39	makes	make	VERB
cana-1019	99	40	people	people	NOUN
cana-1019	99	41	feel	feel	VERB
cana-1019	99	42	.	.	PUNCT
cana-1019	100	1	at	at	ADP
cana-1019	100	2	first	first	ADV
cana-1019	100	3	,	,	PUNCT
cana-1019	100	4	it	it	PRON
cana-1019	100	5	includes	include	VERB
cana-1019	100	6	checking	check	VERB
cana-1019	100	7	all	all	DET
cana-1019	100	8	features	feature	NOUN
cana-1019	100	9	for	for	ADP
cana-1019	100	10	missing	miss	VERB
cana-1019	100	11	numbers	number	NOUN
cana-1019	100	12	to	to	PART
cana-1019	100	13	make	make	VERB
cana-1019	100	14	sure	sure	ADJ
cana-1019	100	15	the	the	DET
cana-1019	100	16	data	data	NOUN
cana-1019	100	17	is	be	AUX
cana-1019	100	18	full	full	ADJ
cana-1019	100	19	and	and	CCONJ
cana-1019	100	20	accurate	accurate	ADJ
cana-1019	100	21	.	.	PUNCT
cana-1019	101	1	depending	depend	VERB
cana-1019	101	2	on	on	ADP
cana-1019	101	3	the	the	DET
cana-1019	101	4	size	size	NOUN
cana-1019	101	5	of	of	ADP
cana-1019	101	6	the	the	DET
cana-1019	101	7	collection	collection	NOUN
cana-1019	101	8	and	and	CCONJ
cana-1019	101	9	the	the	DET
cana-1019	101	10	type	type	NOUN
cana-1019	101	11	of	of	ADP
cana-1019	101	12	messiness	messiness	NOUN
cana-1019	101	13	,	,	PUNCT
cana-1019	101	14	any	any	DET
cana-1019	101	15	numbers	number	NOUN
cana-1019	101	16	that	that	PRON
cana-1019	101	17	are	be	AUX
cana-1019	101	18	not	not	PART
cana-1019	101	19	present	present	ADJ
cana-1019	101	20	are	be	AUX
cana-1019	101	21	either	either	ADV
cana-1019	101	22	filled	fill	VERB
cana-1019	101	23	in	in	ADP
cana-1019	101	24	using	use	VERB
cana-1019	101	25	statistical	statistical	ADJ
cana-1019	101	26	methods	method	NOUN
cana-1019	101	27	or	or	CCONJ
cana-1019	101	28	taken	take	VERB
cana-1019	101	29	out	out	ADP
cana-1019	101	30	.	.	PUNCT
cana-1019	102	1	next	next	ADV
cana-1019	102	2	,	,	PUNCT
cana-1019	102	3	columns	column	NOUN
cana-1019	102	4	that	that	PRON
cana-1019	102	5	do	do	AUX
cana-1019	102	6	n't	not	PART
cana-1019	102	7	help	help	VERB
cana-1019	102	8	with	with	ADP
cana-1019	102	9	the	the	DET
cana-1019	102	10	classification	classification	NOUN
cana-1019	102	11	job	job	NOUN
cana-1019	102	12	or	or	CCONJ
cana-1019	102	13	have	have	VERB
cana-1019	102	14	information	information	NOUN
cana-1019	102	15	that	that	PRON
cana-1019	102	16	is	be	AUX
cana-1019	102	17	already	already	ADV
cana-1019	102	18	known	know	VERB
cana-1019	102	19	are	be	AUX
cana-1019	102	20	removed	remove	VERB
cana-1019	102	21	to	to	PART
cana-1019	102	22	make	make	VERB
cana-1019	102	23	the	the	DET
cana-1019	102	24	dataset	dataset	NOUN
cana-1019	102	25	smaller	small	ADJ
cana-1019	102	26	and	and	CCONJ
cana-1019	102	27	lower	lower	VERB
cana-1019	102	28	the	the	DET
cana-1019	102	29	noise	noise	NOUN
cana-1019	102	30	in	in	ADP
cana-1019	102	31	the	the	DET
cana-1019	102	32	training	training	NOUN
cana-1019	102	33	process	process	NOUN
cana-1019	102	34	for	for	ADP
cana-1019	102	35	the	the	DET
cana-1019	102	36	model	model	NOUN
cana-1019	102	37	[	[	X
cana-1019	102	38	27	27	NUM
cana-1019	102	39	]	]	PUNCT
cana-1019	102	40	.	.	PUNCT
cana-1019	103	1	after	after	SCONJ
cana-1019	103	2	the	the	DET
cana-1019	103	3	information	information	NOUN
cana-1019	103	4	is	be	AUX
cana-1019	103	5	cleaned	clean	VERB
cana-1019	103	6	,	,	PUNCT
cana-1019	103	7	number	number	NOUN
cana-1019	103	8	and	and	CCONJ
cana-1019	103	9	classification	classification	NOUN
cana-1019	103	10	features	feature	NOUN
cana-1019	103	11	are	be	AUX
cana-1019	103	12	split	split	VERB
cana-1019	103	13	so	so	SCONJ
cana-1019	103	14	that	that	SCONJ
cana-1019	103	15	the	the	DET
cana-1019	103	16	right	right	ADJ
cana-1019	103	17	preparation	preparation	NOUN
cana-1019	103	18	steps	step	NOUN
cana-1019	103	19	can	can	AUX
cana-1019	103	20	be	be	AUX
cana-1019	103	21	done	do	VERB
cana-1019	103	22	.	.	PUNCT
cana-1019	104	1	numerical	numerical	PROPN
cana-1019	104	2	features	feature	NOUN
cana-1019	104	3	may	may	AUX
cana-1019	104	4	be	be	AUX
cana-1019	104	5	scaled	scale	VERB
cana-1019	104	6	to	to	PART
cana-1019	104	7	make	make	VERB
cana-1019	104	8	their	their	PRON
cana-1019	104	9	range	range	NOUN
cana-1019	104	10	more	more	ADV
cana-1019	104	11	uniform	uniform	ADJ
cana-1019	104	12	.	.	PUNCT
cana-1019	105	1	this	this	PRON
cana-1019	105	2	makes	make	VERB
cana-1019	105	3	sure	sure	ADJ
cana-1019	105	4	that	that	SCONJ
cana-1019	105	5	every	every	DET
cana-1019	105	6	feature	feature	NOUN
cana-1019	105	7	adds	add	VERB
cana-1019	105	8	the	the	DET
cana-1019	105	9	same	same	ADJ
cana-1019	105	10	amount	amount	NOUN
cana-1019	105	11	to	to	ADP
cana-1019	105	12	training	train	VERB
cana-1019	105	13	the	the	DET
cana-1019	105	14	model	model	NOUN
cana-1019	105	15	.	.	PUNCT
cana-1019	106	1	using	use	VERB
cana-1019	106	2	one	one	NUM
cana-1019	106	3	-	-	PUNCT
cana-1019	106	4	hot	hot	ADJ
cana-1019	106	5	encoding	encoding	NOUN
cana-1019	106	6	and	and	CCONJ
cana-1019	106	7	other	other	ADJ
cana-1019	106	8	methods	method	NOUN
cana-1019	106	9	,	,	PUNCT
cana-1019	106	10	category	category	NOUN
cana-1019	106	11	traits	trait	NOUN
cana-1019	106	12	like	like	ADP
cana-1019	106	13	genre	genre	NOUN
cana-1019	106	14	or	or	CCONJ
cana-1019	106	15	mood	mood	NOUN
cana-1019	106	16	labels	label	NOUN
cana-1019	106	17	are	be	AUX
cana-1019	106	18	changed	change	VERB
cana-1019	106	19	into	into	ADP
cana-1019	106	20	a	a	DET
cana-1019	106	21	format	format	NOUN
cana-1019	106	22	that	that	SCONJ
cana-1019	106	23	machine	machine	NOUN
cana-1019	106	24	learning	learn	VERB
cana-1019	106	25	algorithms	algorithm	NOUN
cana-1019	106	26	can	can	AUX
cana-1019	106	27	understand	understand	VERB
cana-1019	106	28	.	.	PUNCT
cana-1019	107	1	label	label	NOUN
cana-1019	107	2	encoding	encoding	NOUN
cana-1019	107	3	,	,	PUNCT
cana-1019	107	4	on	on	ADP
cana-1019	107	5	the	the	DET
cana-1019	107	6	other	other	ADJ
cana-1019	107	7	hand	hand	NOUN
cana-1019	107	8	,	,	PUNCT
cana-1019	107	9	is	be	AUX
cana-1019	107	10	used	use	VERB
cana-1019	107	11	to	to	PART
cana-1019	107	12	make	make	VERB
cana-1019	107	13	sure	sure	ADJ
cana-1019	107	14	that	that	SCONJ
cana-1019	107	15	category	category	NOUN
cana-1019	107	16	variables	variable	VERB
cana-1019	107	17	with	with	ADP
cana-1019	107	18	numerical	numerical	ADJ
cana-1019	107	19	relationships	relationship	NOUN
cana-1019	107	20	are	be	AUX
cana-1019	107	21	properly	properly	ADV
cana-1019	107	22	represented	represent	VERB
cana-1019	107	23	in	in	ADP
cana-1019	107	24	the	the	DET
cana-1019	107	25	model	model	NOUN
cana-1019	107	26	.	.	PUNCT
cana-1019	108	1	these	these	DET
cana-1019	108	2	steps	step	NOUN
cana-1019	108	3	make	make	VERB
cana-1019	108	4	sure	sure	ADJ
cana-1019	108	5	that	that	SCONJ
cana-1019	108	6	the	the	DET
cana-1019	108	7	dataset	dataset	NOUN
cana-1019	108	8	is	be	AUX
cana-1019	108	9	set	set	VERB
cana-1019	108	10	up	up	ADP
cana-1019	108	11	in	in	ADP
cana-1019	108	12	the	the	DET
cana-1019	108	13	best	good	ADJ
cana-1019	108	14	way	way	NOUN
cana-1019	108	15	possible	possible	ADJ
cana-1019	108	16	so	so	SCONJ
cana-1019	108	17	that	that	SCONJ
cana-1019	108	18	strong	strong	ADJ
cana-1019	108	19	machine	machine	NOUN
cana-1019	108	20	learning	learning	NOUN
cana-1019	108	21	models	model	NOUN
cana-1019	108	22	can	can	AUX
cana-1019	108	23	be	be	AUX
cana-1019	108	24	trained	train	VERB
cana-1019	108	25	to	to	PART
cana-1019	108	26	correctly	correctly	ADV
cana-1019	108	27	identify	identify	VERB
cana-1019	108	28	music	music	NOUN
cana-1019	108	29	feelings	feeling	NOUN
cana-1019	108	30	from	from	ADP
cana-1019	108	31	a	a	DET
cana-1019	108	32	variety	variety	NOUN
cana-1019	108	33	of	of	ADP
cana-1019	108	34	inputs	input	NOUN
cana-1019	108	35	.	.	PUNCT
cana-1019	109	1	a.	a.	NOUN
cana-1019	109	2	numeric	numeric	PROPN
cana-1019	109	3	and	and	CCONJ
cana-1019	109	4	categorical	categorical	ADJ
cana-1019	109	5	features	feature	NOUN
cana-1019	109	6	separation	separation	NOUN
cana-1019	109	7	:	:	PUNCT
cana-1019	109	8	given	give	VERB
cana-1019	109	9	a	a	DET
cana-1019	109	10	dataset	dataset	NOUN
cana-1019	109	11	d	d	NOUN
cana-1019	109	12	with	with	ADP
cana-1019	109	13	n	n	ADP
cana-1019	109	14	samples	sample	NOUN
cana-1019	109	15	and	and	CCONJ
cana-1019	109	16	m	m	NOUN
cana-1019	109	17	features	feature	NOUN
cana-1019	109	18	,	,	PUNCT
cana-1019	109	19	features	feature	NOUN
cana-1019	109	20	can	can	AUX
cana-1019	109	21	be	be	AUX
cana-1019	109	22	categorized	categorize	VERB
cana-1019	109	23	into	into	ADP
cana-1019	109	24	:	:	PUNCT
cana-1019	109	25	1	1	X
cana-1019	109	26	.	.	X
cana-1019	109	27	numeric	numeric	ADJ
cana-1019	109	28	features	feature	NOUN
cana-1019	109	29	:	:	PUNCT
cana-1019	109	30	numeric	numeric	ADJ
cana-1019	109	31	features	feature	NOUN
cana-1019	109	32	are	be	AUX
cana-1019	109	33	continuous	continuous	ADJ
cana-1019	109	34	variables	variable	NOUN
cana-1019	109	35	denoted	denote	VERB
cana-1019	109	36	as	as	ADP
cana-1019	109	37	𝑋_𝑛𝑢𝑚	𝑋_𝑛𝑢𝑚	NOUN
cana-1019	109	38	=	=	SYM
cana-1019	109	39	{	{	PUNCT
cana-1019	109	40	𝑥_𝑖𝑗}_(𝑛	𝑥_𝑖𝑗}_(𝑛	NOUN
cana-1019	109	41	𝑥	𝑥	X
cana-1019	109	42	𝑚_𝑛𝑢𝑚	𝑚_𝑛𝑢𝑚	PROPN
cana-1019	109	43	)	)	PUNCT
cana-1019	109	44	•	•	NUM
cana-1019	109	45	where	where	SCONJ
cana-1019	109	46	x_ij	x_ij	PROPN
cana-1019	109	47	represents	represent	VERB
cana-1019	109	48	the	the	DET
cana-1019	109	49	j	j	PROPN
cana-1019	109	50	-	-	PUNCT
cana-1019	109	51	th	th	X
cana-1019	109	52	numeric	numeric	ADJ
cana-1019	109	53	feature	feature	NOUN
cana-1019	109	54	of	of	ADP
cana-1019	109	55	the	the	DET
cana-1019	109	56	i	i	PROPN
cana-1019	109	57	-	-	PUNCT
cana-1019	109	58	th	th	X
cana-1019	109	59	sample	sample	NOUN
cana-1019	109	60	.	.	PUNCT
cana-1019	110	1	2	2	X
cana-1019	110	2	.	.	X
cana-1019	110	3	categorical	categorical	ADJ
cana-1019	110	4	features	feature	NOUN
cana-1019	110	5	:	:	PUNCT
cana-1019	110	6	categorical	categorical	ADJ
cana-1019	110	7	features	feature	NOUN
cana-1019	110	8	are	be	AUX
cana-1019	110	9	discrete	discrete	ADJ
cana-1019	110	10	variables	variable	NOUN
cana-1019	110	11	denoted	denote	VERB
cana-1019	110	12	as	as	ADP
cana-1019	110	13	𝑋_𝑐𝑎𝑡	𝑋_𝑐𝑎𝑡	ADV
cana-1019	110	14	=	=	PRON
cana-1019	110	15	{	{	PUNCT
cana-1019	110	16	𝑐_𝑖𝑗}_(𝑛	𝑐_𝑖𝑗}_(𝑛	INTJ
cana-1019	110	17	𝑥	𝑥	PRON
cana-1019	110	18	𝑚_𝑐𝑎𝑡	𝑚_𝑐𝑎𝑡	NUM
cana-1019	110	19	)	)	PUNCT
cana-1019	110	20	•	•	NUM
cana-1019	110	21	where	where	SCONJ
cana-1019	110	22	c_ij	c_ij	NOUN
cana-1019	110	23	represents	represent	VERB
cana-1019	110	24	the	the	DET
cana-1019	110	25	j	j	PROPN
cana-1019	110	26	-	-	PUNCT
cana-1019	110	27	th	th	VERB
cana-1019	110	28	categorical	categorical	ADJ
cana-1019	110	29	feature	feature	NOUN
cana-1019	110	30	of	of	ADP
cana-1019	110	31	the	the	DET
cana-1019	110	32	i	i	PROPN
cana-1019	110	33	-	-	PUNCT
cana-1019	110	34	th	th	VERB
cana-1019	110	35	sample	sample	NOUN
cana-1019	110	36	.	.	PUNCT
cana-1019	111	1	b.	b.	PROPN
cana-1019	112	1	one	one	NUM
cana-1019	112	2	-	-	PUNCT
cana-1019	112	3	hot	hot	ADJ
cana-1019	112	4	encoding	encoding	NOUN
cana-1019	112	5	:	:	PUNCT
cana-1019	112	6	for	for	ADP
cana-1019	112	7	a	a	DET
cana-1019	112	8	categorical	categorical	ADJ
cana-1019	112	9	feature	feature	NOUN
cana-1019	112	10	c_ij	c_ij	NOUN
cana-1019	112	11	with	with	ADP
cana-1019	112	12	q	q	ADJ
cana-1019	112	13	unique	unique	ADJ
cana-1019	112	14	categories	category	NOUN
cana-1019	112	15	,	,	PUNCT
cana-1019	112	16	the	the	DET
cana-1019	112	17	one	one	NUM
cana-1019	112	18	-	-	PUNCT
cana-1019	112	19	hot	hot	ADJ
cana-1019	112	20	encoding	encoding	NOUN
cana-1019	112	21	onehot(c_ij	onehot(c_ij	NOUN
cana-1019	112	22	)	)	PUNCT
cana-1019	112	23	is	be	AUX
cana-1019	112	24	represented	represent	VERB
cana-1019	112	25	as	as	ADP
cana-1019	112	26	a	a	DET
cana-1019	112	27	binary	binary	ADJ
cana-1019	112	28	vector	vector	NOUN
cana-1019	112	29	{	{	PUNCT
cana-1019	112	30	0	0	NUM
cana-1019	112	31	,	,	PUNCT
cana-1019	112	32	1}^q	1}^q	NUM
cana-1019	112	33	where	where	SCONJ
cana-1019	112	34	:	:	PUNCT
cana-1019	112	35	𝑂𝑛𝑒𝐻𝑜𝑡(𝑐_𝑖𝑗	𝑂𝑛𝑒𝐻𝑜𝑡(𝑐_𝑖𝑗	NOUN
cana-1019	112	36	)	)	PUNCT
cana-1019	113	1	=	=	PUNCT
cana-1019	114	1	[	[	X
cana-1019	114	2	0	0	NUM
cana-1019	114	3	,	,	PUNCT
cana-1019	114	4	0	0	NUM
cana-1019	114	5	,	,	PUNCT
cana-1019	114	6	.	.	PUNCT
cana-1019	114	7	.	.	PUNCT
cana-1019	115	1	.	.	PUNCT
cana-1019	116	1	,	,	PUNCT
cana-1019	116	2	1	1	NUM
cana-1019	116	3	,	,	PUNCT
cana-1019	116	4	.	.	PUNCT
cana-1019	116	5	.	.	PUNCT
cana-1019	116	6	.	.	PUNCT
cana-1019	117	1	,	,	PUNCT
cana-1019	117	2	0	0	NUM
cana-1019	117	3	]	]	PUNCT
cana-1019	117	4	•	•	NUM
cana-1019	117	5	where	where	SCONJ
cana-1019	117	6	,	,	PUNCT
cana-1019	117	7	the	the	DET
cana-1019	117	8	position	position	NOUN
cana-1019	117	9	of	of	ADP
cana-1019	117	10	1	1	NUM
cana-1019	117	11	corresponds	correspond	NOUN
cana-1019	117	12	to	to	ADP
cana-1019	117	13	the	the	DET
cana-1019	117	14	category	category	NOUN
cana-1019	117	15	of	of	ADP
cana-1019	117	16	c_ij	c_ij	PROPN
cana-1019	117	17	.	.	PUNCT
cana-1019	118	1	for	for	ADP
cana-1019	118	2	example	example	NOUN
cana-1019	118	3	,	,	PUNCT
cana-1019	118	4	if	if	SCONJ
cana-1019	118	5	c_ij	c_ij	NOUN
cana-1019	118	6	takes	take	VERB
cana-1019	118	7	the	the	DET
cana-1019	118	8	value	value	NOUN
cana-1019	118	9	of	of	ADP
cana-1019	118	10	the	the	DET
cana-1019	118	11	second	second	ADJ
cana-1019	118	12	category	category	NOUN
cana-1019	118	13	out	out	ADP
cana-1019	118	14	of	of	ADP
cana-1019	118	15	q	q	NOUN
cana-1019	118	16	categories	category	NOUN
cana-1019	118	17	,	,	PUNCT
cana-1019	118	18	the	the	DET
cana-1019	118	19	encoding	encoding	NOUN
cana-1019	118	20	would	would	AUX
cana-1019	118	21	be	be	AUX
cana-1019	118	22	[	[	X
cana-1019	118	23	0	0	NUM
cana-1019	118	24	,	,	PUNCT
cana-1019	118	25	1	1	NUM
cana-1019	118	26	,	,	PUNCT
cana-1019	118	27	0	0	NUM
cana-1019	118	28	,	,	PUNCT
cana-1019	118	29	...	...	PUNCT
cana-1019	118	30	,	,	PUNCT
cana-1019	118	31	0	0	NUM
cana-1019	118	32	]	]	PUNCT
cana-1019	118	33	.	.	PUNCT
cana-1019	119	1	c.	c.	PROPN
cana-1019	119	2	label	label	PROPN
cana-1019	119	3	encoding	encoding	NOUN
cana-1019	119	4	:	:	PUNCT
cana-1019	119	5	label	label	NOUN
cana-1019	119	6	encoding	encoding	NOUN
cana-1019	119	7	assigns	assign	NOUN
cana-1019	119	8	integers	integer	NOUN
cana-1019	119	9	to	to	ADP
cana-1019	119	10	categories	category	NOUN
cana-1019	119	11	preserving	preserve	VERB
cana-1019	119	12	their	their	PRON
cana-1019	119	13	order	order	NOUN
cana-1019	119	14	.	.	PUNCT
cana-1019	120	1	for	for	ADP
cana-1019	120	2	a	a	DET
cana-1019	120	3	categorical	categorical	ADJ
cana-1019	120	4	feature	feature	NOUN
cana-1019	120	5	c_ij	c_ij	NOUN
cana-1019	120	6	,	,	PUNCT
cana-1019	120	7	label	label	NOUN
cana-1019	120	8	encode(c_ij	encode(c_ij	NOUN
cana-1019	120	9	)	)	PUNCT
cana-1019	120	10	converts	convert	VERB
cana-1019	120	11	each	each	DET
cana-1019	120	12	category	category	NOUN
cana-1019	120	13	into	into	ADP
cana-1019	120	14	a	a	DET
cana-1019	120	15	unique	unique	ADJ
cana-1019	120	16	integer	integer	NOUN
cana-1019	120	17	:	:	PUNCT
cana-1019	120	18	communications	communication	NOUN
cana-1019	120	19	on	on	ADP
cana-1019	120	20	applied	apply	VERB
cana-1019	120	21	nonlinear	nonlinear	ADJ
cana-1019	120	22	analysis	analysis	NOUN
cana-1019	120	23	issn	issn	NOUN
cana-1019	120	24	:	:	PUNCT
cana-1019	120	25	1074	1074	NUM
cana-1019	120	26	-	-	PUNCT
cana-1019	120	27	133x	133x	NUM
cana-1019	120	28	vol	vol	NOUN
cana-1019	120	29	31	31	NUM
cana-1019	120	30	no	no	NOUN
cana-1019	120	31	.	.	PUNCT
cana-1019	121	1	5s	5s	NUM
cana-1019	121	2	(	(	PUNCT
cana-1019	121	3	2024	2024	NUM
cana-1019	121	4	)	)	PUNCT
cana-1019	121	5	240	240	NUM
cana-1019	121	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1019	121	7	𝐿𝑎𝑏𝑒𝑙𝐸𝑛𝑐𝑜𝑑𝑒(𝑐_𝑖𝑗	𝐿𝑎𝑏𝑒𝑙𝐸𝑛𝑐𝑜𝑑𝑒(𝑐_𝑖𝑗	NOUN
cana-1019	121	8	)	)	PUNCT
cana-1019	121	9	=	=	PRON
cana-1019	121	10	{	{	PUNCT
cana-1019	121	11	0	0	NUM
cana-1019	121	12	,	,	PUNCT
cana-1019	121	13	1	1	NUM
cana-1019	121	14	,	,	PUNCT
cana-1019	121	15	2	2	NUM
cana-1019	121	16	,	,	PUNCT
cana-1019	121	17	.	.	PUNCT
cana-1019	121	18	.	.	PUNCT
cana-1019	121	19	.	.	PUNCT
cana-1019	122	1	,	,	PUNCT
cana-1019	122	2	𝑞	𝑞	X
cana-1019	122	3	−	−	PROPN
cana-1019	122	4	1	1	NUM
cana-1019	122	5	}	}	PUNCT
cana-1019	122	6	•	•	ADP
cana-1019	122	7	where	where	SCONJ
cana-1019	122	8	,	,	PUNCT
cana-1019	122	9	q	q	X
cana-1019	122	10	is	be	AUX
cana-1019	122	11	the	the	DET
cana-1019	122	12	number	number	NOUN
cana-1019	122	13	of	of	ADP
cana-1019	122	14	unique	unique	ADJ
cana-1019	122	15	categories	category	NOUN
cana-1019	122	16	in	in	ADP
cana-1019	122	17	c_ij	c_ij	PROPN
cana-1019	122	18	.	.	PUNCT
cana-1019	123	1	label	label	NOUN
cana-1019	123	2	encoding	encoding	NOUN
cana-1019	123	3	is	be	AUX
cana-1019	123	4	suitable	suitable	ADJ
cana-1019	123	5	for	for	ADP
cana-1019	123	6	algorithms	algorithm	NOUN
cana-1019	123	7	that	that	PRON
cana-1019	123	8	interpret	interpret	VERB
cana-1019	123	9	ordinal	ordinal	ADJ
cana-1019	123	10	relationships	relationship	NOUN
cana-1019	123	11	among	among	ADP
cana-1019	123	12	categories	category	NOUN
cana-1019	123	13	,	,	PUNCT
cana-1019	123	14	such	such	ADJ
cana-1019	123	15	as	as	ADP
cana-1019	123	16	decision	decision	NOUN
cana-1019	123	17	trees	tree	NOUN
cana-1019	123	18	or	or	CCONJ
cana-1019	123	19	regression	regression	NOUN
cana-1019	123	20	models	model	NOUN
cana-1019	123	21	.	.	PUNCT
cana-1019	124	1	d.	d.	PROPN
cana-1019	124	2	data	data	PROPN
cana-1019	124	3	normalization	normalization	PROPN
cana-1019	124	4	information	information	PROPN
cana-1019	124	5	normalization	normalization	NOUN
cana-1019	124	6	,	,	PUNCT
cana-1019	124	7	particularly	particularly	ADV
cana-1019	124	8	utilizing	utilize	VERB
cana-1019	124	9	standard	standard	ADJ
cana-1019	124	10	scaler	scaler	NOUN
cana-1019	124	11	,	,	PUNCT
cana-1019	124	12	could	could	AUX
cana-1019	124	13	be	be	AUX
cana-1019	124	14	a	a	DET
cana-1019	124	15	preprocessing	preprocessing	NOUN
cana-1019	124	16	procedure	procedure	NOUN
cana-1019	124	17	fundamental	fundamental	ADJ
cana-1019	124	18	for	for	ADP
cana-1019	124	19	machine	machine	NOUN
cana-1019	124	20	learning	learn	VERB
cana-1019	124	21	assignments	assignment	NOUN
cana-1019	124	22	.	.	PUNCT
cana-1019	125	1	standard	standard	ADJ
cana-1019	125	2	scaler	scaler	NOUN
cana-1019	125	3	changes	change	NOUN
cana-1019	125	4	numerical	numerical	ADJ
cana-1019	125	5	highlights	highlight	NOUN
cana-1019	125	6	to	to	PART
cana-1019	125	7	have	have	AUX
cana-1019	125	8	a	a	DET
cana-1019	125	9	cruel	cruel	ADJ
cana-1019	125	10	of	of	ADP
cana-1019	125	11	and	and	CCONJ
cana-1019	125	12	a	a	DET
cana-1019	125	13	standard	standard	ADJ
cana-1019	125	14	deviation	deviation	NOUN
cana-1019	125	15	of	of	ADP
cana-1019	125	16	1	1	NUM
cana-1019	125	17	,	,	PUNCT
cana-1019	125	18	guaranteeing	guarantee	VERB
cana-1019	125	19	that	that	SCONJ
cana-1019	125	20	all	all	DET
cana-1019	125	21	highlights	highlight	NOUN
cana-1019	125	22	are	be	AUX
cana-1019	125	23	on	on	ADP
cana-1019	125	24	the	the	DET
cana-1019	125	25	same	same	ADJ
cana-1019	125	26	scale	scale	NOUN
cana-1019	125	27	.	.	PUNCT
cana-1019	126	1	standard	standard	ADJ
cana-1019	126	2	scaler	scaler	ADJ
cana-1019	126	3	step	step	NOUN
cana-1019	126	4	wise	wise	ADJ
cana-1019	126	5	model	model	NOUN
cana-1019	126	6	1	1	NUM
cana-1019	126	7	.	.	PUNCT
cana-1019	126	8	compute	compute	NOUN
cana-1019	126	9	mean	mean	NOUN
cana-1019	126	10	(	(	PUNCT
cana-1019	126	11	μ	μ	NOUN
cana-1019	126	12	)	)	PUNCT
cana-1019	126	13	of	of	ADP
cana-1019	126	14	each	each	DET
cana-1019	126	15	feature	feature	NOUN
cana-1019	126	16	:	:	PUNCT
cana-1019	126	17	𝜇𝑗	𝜇𝑗	X
cana-1019	126	18	=	=	SYM
cana-1019	126	19	(	(	PUNCT
cana-1019	126	20	1	1	NUM
cana-1019	126	21	𝑛	𝑛	NOUN
cana-1019	126	22	)	)	PUNCT
cana-1019	127	1	𝛴𝑖	𝛴𝑖	PROPN
cana-1019	127	2	=	=	SYM
cana-1019	127	3	1𝑛𝑥𝑖𝑗	1𝑛𝑥𝑖𝑗	NUM
cana-1019	127	4	•	•	NUM
cana-1019	127	5	calculate	calculate	VERB
cana-1019	127	6	the	the	DET
cana-1019	127	7	mean	mean	ADJ
cana-1019	127	8	value	value	NOUN
cana-1019	127	9	for	for	ADP
cana-1019	127	10	each	each	DET
cana-1019	127	11	feature	feature	NOUN
cana-1019	127	12	j	j	PROPN
cana-1019	127	13	across	across	ADP
cana-1019	127	14	all	all	DET
cana-1019	127	15	samples	sample	NOUN
cana-1019	127	16	i.	i.	NOUN
cana-1019	127	17	2	2	NUM
cana-1019	127	18	.	.	PUNCT
cana-1019	127	19	compute	compute	PROPN
cana-1019	127	20	standard	standard	ADJ
cana-1019	127	21	deviation	deviation	NOUN
cana-1019	127	22	(	(	PUNCT
cana-1019	127	23	σ	σ	NOUN
cana-1019	127	24	)	)	PUNCT
cana-1019	127	25	of	of	ADP
cana-1019	127	26	each	each	DET
cana-1019	127	27	feature	feature	NOUN
cana-1019	127	28	:	:	PUNCT
cana-1019	127	29	𝜎𝑗	𝜎𝑗	X
cana-1019	127	30	=	=	SYM
cana-1019	127	31	𝑠𝑞𝑟𝑡	𝑠𝑞𝑟𝑡	X
cana-1019	127	32	(	(	PUNCT
cana-1019	127	33	(	(	PUNCT
cana-1019	127	34	1	1	NUM
cana-1019	127	35	𝑛	𝑛	NOUN
cana-1019	127	36	)	)	PUNCT
cana-1019	128	1	𝛴𝑖	𝛴𝑖	NOUN
cana-1019	128	2	=	=	SYM
cana-1019	128	3	1𝑛(𝑥𝑖𝑗	1𝑛(𝑥𝑖𝑗	NUM
cana-1019	128	4	−	−	NOUN
cana-1019	128	5	𝜇𝑗	𝜇𝑗	NOUN
cana-1019	128	6	)	)	PUNCT
cana-1019	128	7	2	2	NUM
cana-1019	128	8	)	)	PUNCT
cana-1019	128	9	•	•	NOUN
cana-1019	128	10	calculate	calculate	VERB
cana-1019	128	11	the	the	DET
cana-1019	128	12	standard	standard	ADJ
cana-1019	128	13	deviation	deviation	NOUN
cana-1019	128	14	for	for	ADP
cana-1019	128	15	each	each	DET
cana-1019	128	16	feature	feature	NOUN
cana-1019	128	17	j	j	PROPN
cana-1019	128	18	across	across	ADP
cana-1019	128	19	all	all	DET
cana-1019	128	20	samples	sample	NOUN
cana-1019	128	21	i.	i.	NOUN
cana-1019	128	22	3	3	NUM
cana-1019	128	23	.	.	PUNCT
cana-1019	128	24	standardize	standardize	VERB
cana-1019	128	25	each	each	DET
cana-1019	128	26	feature	feature	NOUN
cana-1019	128	27	:	:	PUNCT
cana-1019	128	28	�	�	PROPN
cana-1019	128	29	̂	̂	NOUN
cana-1019	128	30	�	�	NOUN
cana-1019	128	31	𝑖𝑗	𝑖𝑗	NOUN
cana-1019	128	32	=	=	SYM
cana-1019	128	33	(	(	PUNCT
cana-1019	128	34	𝑥𝑖𝑗	𝑥𝑖𝑗	PROPN
cana-1019	128	35	−	−	PROPN
cana-1019	128	36	𝜇𝑗	𝜇𝑗	PROPN
cana-1019	128	37	)	)	PUNCT
cana-1019	128	38	𝜎𝑗	𝜎𝑗	NOUN
cana-1019	128	39	•	•	NOUN
cana-1019	128	40	standardize	standardize	VERB
cana-1019	128	41	each	each	DET
cana-1019	128	42	feature	feature	NOUN
cana-1019	128	43	j	j	PROPN
cana-1019	128	44	by	by	ADP
cana-1019	128	45	subtracting	subtract	VERB
cana-1019	128	46	its	its	PRON
cana-1019	128	47	mean	mean	NOUN
cana-1019	128	48	μ_j	μ_j	NUM
cana-1019	128	49	and	and	CCONJ
cana-1019	128	50	dividing	divide	VERB
cana-1019	128	51	by	by	ADP
cana-1019	128	52	its	its	PRON
cana-1019	128	53	standard	standard	ADJ
cana-1019	128	54	deviation	deviation	NOUN
cana-1019	128	55	σ_j	σ_j	PRON
cana-1019	128	56	.	.	PUNCT
cana-1019	129	1	this	this	PRON
cana-1019	129	2	centers	center	VERB
cana-1019	129	3	the	the	DET
cana-1019	129	4	feature	feature	NOUN
cana-1019	129	5	distribution	distribution	NOUN
cana-1019	129	6	around	around	ADP
cana-1019	129	7	0	0	NUM
cana-1019	129	8	with	with	ADP
cana-1019	129	9	a	a	DET
cana-1019	129	10	standard	standard	ADJ
cana-1019	129	11	deviation	deviation	NOUN
cana-1019	129	12	of	of	ADP
cana-1019	129	13	1	1	NUM
cana-1019	129	14	.	.	NOUN
cana-1019	129	15	4	4	NUM
cana-1019	129	16	.	.	NUM
cana-1019	129	17	transformed	transform	VERB
cana-1019	129	18	feature	feature	NOUN
cana-1019	129	19	calculation	calculation	NOUN
cana-1019	129	20	:	:	PUNCT
cana-1019	129	21	�	�	PROPN
cana-1019	129	22	̂	̂	SYM
cana-1019	129	23	�	�	NOUN
cana-1019	129	24	=	=	SYM
cana-1019	129	25	(	(	PUNCT
cana-1019	129	26	𝑋	𝑋	PROPN
cana-1019	129	27	−	−	PROPN
cana-1019	129	28	𝜇	𝜇	NOUN
cana-1019	129	29	)	)	PUNCT
cana-1019	129	30	𝜎	𝜎	NOUN
cana-1019	129	31	•	•	NOUN
cana-1019	129	32	apply	apply	VERB
cana-1019	129	33	the	the	DET
cana-1019	129	34	transformation	transformation	NOUN
cana-1019	129	35	across	across	ADP
cana-1019	129	36	all	all	DET
cana-1019	129	37	features	feature	NOUN
cana-1019	129	38	x	x	X
cana-1019	129	39	,	,	PUNCT
cana-1019	129	40	where	where	SCONJ
cana-1019	129	41	μ	μ	PROPN
cana-1019	129	42	is	be	AUX
cana-1019	129	43	the	the	DET
cana-1019	129	44	mean	mean	ADJ
cana-1019	129	45	vector	vector	NOUN
cana-1019	129	46	and	and	CCONJ
cana-1019	129	47	σ	σ	PROPN
cana-1019	129	48	is	be	AUX
cana-1019	129	49	the	the	DET
cana-1019	129	50	standard	standard	ADJ
cana-1019	129	51	deviation	deviation	NOUN
cana-1019	129	52	vector	vector	NOUN
cana-1019	129	53	calculated	calculate	VERB
cana-1019	129	54	for	for	ADP
cana-1019	129	55	each	each	DET
cana-1019	129	56	feature	feature	NOUN
cana-1019	129	57	.	.	PUNCT
cana-1019	130	1	5	5	X
cana-1019	130	2	.	.	X
cana-1019	130	3	inverse	inverse	NOUN
cana-1019	130	4	transform	transform	NOUN
cana-1019	130	5	:	:	PUNCT
cana-1019	130	6	𝑋𝑜𝑟𝑖𝑔𝑖𝑛𝑎𝑙	𝑋𝑜𝑟𝑖𝑔𝑖𝑛𝑎𝑙	PROPN
cana-1019	130	7	=	=	SYM
cana-1019	130	8	�	�	PROPN
cana-1019	130	9	̂	̂	PROPN
cana-1019	130	10	�	�	PROPN
cana-1019	130	11	∗	∗	NOUN
cana-1019	130	12	𝜎	𝜎	PROPN
cana-1019	131	1	+	+	CCONJ
cana-1019	131	2	𝜇	𝜇	ADP
cana-1019	131	3	•	•	NOUN
cana-1019	131	4	the	the	DET
cana-1019	131	5	transform	transform	NOUN
cana-1019	131	6	the	the	DET
cana-1019	131	7	standardized	standardized	ADJ
cana-1019	131	8	data	datum	NOUN
cana-1019	131	9	back	back	ADV
cana-1019	131	10	to	to	ADP
cana-1019	131	11	its	its	PRON
cana-1019	131	12	original	original	ADJ
cana-1019	131	13	scale	scale	NOUN
cana-1019	131	14	by	by	ADP
cana-1019	131	15	multiplying	multiply	VERB
cana-1019	131	16	with	with	ADP
cana-1019	131	17	the	the	DET
cana-1019	131	18	standard	standard	ADJ
cana-1019	131	19	deviation	deviation	NOUN
cana-1019	131	20	vector	vector	NOUN
cana-1019	131	21	σ	σ	NOUN
cana-1019	131	22	and	and	CCONJ
cana-1019	131	23	adding	add	VERB
cana-1019	131	24	the	the	DET
cana-1019	131	25	mean	mean	PROPN
cana-1019	131	26	vector	vector	PROPN
cana-1019	131	27	μ	μ	NOUN
cana-1019	131	28	.	.	PUNCT
cana-1019	132	1	this	this	DET
cana-1019	132	2	normalization	normalization	NOUN
cana-1019	132	3	is	be	AUX
cana-1019	132	4	significant	significant	ADJ
cana-1019	132	5	for	for	ADP
cana-1019	132	6	calculations	calculation	NOUN
cana-1019	132	7	touchy	touchy	ADJ
cana-1019	132	8	to	to	ADP
cana-1019	132	9	the	the	DET
cana-1019	132	10	scale	scale	NOUN
cana-1019	132	11	of	of	ADP
cana-1019	132	12	input	input	NOUN
cana-1019	132	13	information	information	NOUN
cana-1019	132	14	,	,	PUNCT
cana-1019	132	15	such	such	ADJ
cana-1019	132	16	as	as	ADP
cana-1019	132	17	bolster	bolster	VERB
cana-1019	132	18	vector	vector	NOUN
cana-1019	132	19	machines	machine	NOUN
cana-1019	132	20	and	and	CCONJ
cana-1019	132	21	k	k	NOUN
cana-1019	132	22	-	-	PUNCT
cana-1019	132	23	nearest	near	ADJ
cana-1019	132	24	neighbours	neighbour	NOUN
cana-1019	132	25	.	.	PUNCT
cana-1019	133	1	by	by	ADP
cana-1019	133	2	standardizing	standardize	VERB
cana-1019	133	3	highlights	highlight	NOUN
cana-1019	133	4	,	,	PUNCT
cana-1019	133	5	standard	standard	ADJ
cana-1019	133	6	scaler	scaler	NOUN
cana-1019	133	7	communications	communication	NOUN
cana-1019	133	8	on	on	ADP
cana-1019	133	9	applied	apply	VERB
cana-1019	133	10	nonlinear	nonlinear	ADJ
cana-1019	133	11	analysis	analysis	NOUN
cana-1019	133	12	issn	issn	NOUN
cana-1019	133	13	:	:	PUNCT
cana-1019	133	14	1074	1074	NUM
cana-1019	133	15	-	-	PUNCT
cana-1019	133	16	133x	133x	NUM
cana-1019	133	17	vol	vol	NOUN
cana-1019	133	18	31	31	NUM
cana-1019	133	19	no	no	NOUN
cana-1019	133	20	.	.	PUNCT
cana-1019	134	1	5s	5s	NUM
cana-1019	134	2	(	(	PUNCT
cana-1019	134	3	2024	2024	NUM
cana-1019	134	4	)	)	PUNCT
cana-1019	134	5	241	241	NUM
cana-1019	135	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-1019	135	2	makes	make	VERB
cana-1019	135	3	strides	stride	NOUN
cana-1019	135	4	the	the	DET
cana-1019	135	5	meeting	meeting	NOUN
cana-1019	135	6	speed	speed	NOUN
cana-1019	135	7	of	of	ADP
cana-1019	135	8	gradient	gradient	NOUN
cana-1019	135	9	-	-	PUNCT
cana-1019	135	10	based	base	VERB
cana-1019	135	11	calculations	calculation	NOUN
cana-1019	135	12	and	and	CCONJ
cana-1019	135	13	anticipates	anticipate	VERB
cana-1019	135	14	overwhelming	overwhelming	ADJ
cana-1019	135	15	highlights	highlight	NOUN
cana-1019	135	16	from	from	ADP
cana-1019	135	17	dominating	dominate	VERB
cana-1019	135	18	others	other	NOUN
cana-1019	135	19	,	,	PUNCT
cana-1019	135	20	normalized	normalize	VERB
cana-1019	135	21	dataset	dataset	NOUN
cana-1019	135	22	shown	show	VERB
cana-1019	135	23	in	in	ADP
cana-1019	135	24	figure	figure	NOUN
cana-1019	135	25	4	4	NUM
cana-1019	135	26	.	.	PUNCT
cana-1019	135	27	figure	figure	VERB
cana-1019	135	28	4	4	NUM
cana-1019	135	29	:	:	PUNCT
cana-1019	135	30	representation	representation	NOUN
cana-1019	135	31	of	of	ADP
cana-1019	135	32	normalized	normalize	VERB
cana-1019	135	33	dataset	dataset	NOUN
cana-1019	135	34	it	it	PRON
cana-1019	135	35	moreover	moreover	ADV
cana-1019	135	36	makes	make	VERB
cana-1019	135	37	a	a	DET
cana-1019	135	38	difference	difference	NOUN
cana-1019	135	39	in	in	ADP
cana-1019	135	40	deciphering	decipher	VERB
cana-1019	135	41	show	show	NOUN
cana-1019	135	42	coefficients	coefficient	NOUN
cana-1019	135	43	,	,	PUNCT
cana-1019	135	44	making	make	VERB
cana-1019	135	45	it	it	PRON
cana-1019	135	46	less	less	ADV
cana-1019	135	47	demanding	demanding	ADJ
cana-1019	135	48	to	to	PART
cana-1019	135	49	compare	compare	VERB
cana-1019	135	50	the	the	DET
cana-1019	135	51	significance	significance	NOUN
cana-1019	135	52	of	of	ADP
cana-1019	135	53	diverse	diverse	ADJ
cana-1019	135	54	highlights	highlight	NOUN
cana-1019	135	55	within	within	ADP
cana-1019	135	56	the	the	DET
cana-1019	135	57	prescient	prescient	NOUN
cana-1019	135	58	demonstrate	demonstrate	NOUN
cana-1019	135	59	.	.	PUNCT
cana-1019	136	1	in	in	ADP
cana-1019	136	2	this	this	DET
cana-1019	136	3	way	way	NOUN
cana-1019	136	4	,	,	PUNCT
cana-1019	136	5	standard	standard	ADJ
cana-1019	136	6	scaler	scaler	NOUN
cana-1019	136	7	plays	play	VERB
cana-1019	136	8	a	a	DET
cana-1019	136	9	key	key	ADJ
cana-1019	136	10	part	part	NOUN
cana-1019	136	11	in	in	ADP
cana-1019	136	12	planning	plan	VERB
cana-1019	136	13	information	information	NOUN
cana-1019	136	14	for	for	ADP
cana-1019	136	15	strong	strong	ADJ
cana-1019	136	16	and	and	CCONJ
cana-1019	136	17	effective	effective	ADJ
cana-1019	136	18	machine	machine	NOUN
cana-1019	136	19	learning	learning	NOUN
cana-1019	136	20	show	show	NOUN
cana-1019	136	21	preparing	prepare	VERB
cana-1019	136	22	.	.	PUNCT
cana-1019	137	1	b.	b.	PROPN
cana-1019	137	2	machine	machine	NOUN
cana-1019	137	3	learning	learn	VERB
cana-1019	137	4	classification	classification	NOUN
cana-1019	137	5	algorithms	algorithm	NOUN
cana-1019	137	6	a.	a.	NOUN
cana-1019	137	7	logistic	logistic	PROPN
cana-1019	137	8	regression	regression	NOUN
cana-1019	137	9	:	:	PUNCT
cana-1019	137	10	this	this	PRON
cana-1019	137	11	is	be	AUX
cana-1019	137	12	a	a	DET
cana-1019	137	13	linear	linear	ADJ
cana-1019	137	14	model	model	NOUN
cana-1019	137	15	that	that	PRON
cana-1019	137	16	is	be	AUX
cana-1019	137	17	often	often	ADV
cana-1019	137	18	used	use	VERB
cana-1019	137	19	for	for	ADP
cana-1019	137	20	jobs	job	NOUN
cana-1019	137	21	that	that	PRON
cana-1019	137	22	need	need	VERB
cana-1019	137	23	to	to	PART
cana-1019	137	24	classify	classify	VERB
cana-1019	137	25	things	thing	NOUN
cana-1019	137	26	into	into	ADP
cana-1019	137	27	two	two	NUM
cana-1019	137	28	groups	group	NOUN
cana-1019	137	29	.	.	PUNCT
cana-1019	138	1	it	it	PRON
cana-1019	138	2	uses	use	VERB
cana-1019	138	3	a	a	DET
cana-1019	138	4	logistic	logistic	ADJ
cana-1019	138	5	function	function	NOUN
cana-1019	138	6	to	to	PART
cana-1019	138	7	predict	predict	VERB
cana-1019	138	8	odds	odd	NOUN
cana-1019	138	9	,	,	PUNCT
cana-1019	138	10	which	which	PRON
cana-1019	138	11	means	mean	VERB
cana-1019	138	12	it	it	PRON
cana-1019	138	13	can	can	AUX
cana-1019	138	14	be	be	AUX
cana-1019	138	15	used	use	VERB
cana-1019	138	16	to	to	PART
cana-1019	138	17	figure	figure	VERB
cana-1019	138	18	out	out	ADP
cana-1019	138	19	how	how	SCONJ
cana-1019	138	20	likely	likely	ADJ
cana-1019	138	21	it	it	PRON
cana-1019	138	22	is	be	AUX
cana-1019	138	23	that	that	SCONJ
cana-1019	138	24	a	a	DET
cana-1019	138	25	sample	sample	NOUN
cana-1019	138	26	belongs	belong	VERB
cana-1019	138	27	to	to	ADP
cana-1019	138	28	a	a	DET
cana-1019	138	29	certain	certain	ADJ
cana-1019	138	30	class	class	NOUN
cana-1019	138	31	based	base	VERB
cana-1019	138	32	on	on	ADP
cana-1019	138	33	its	its	PRON
cana-1019	138	34	traits	trait	NOUN
cana-1019	138	35	.	.	PUNCT
cana-1019	139	1	algorithm	algorithm	NOUN
cana-1019	139	2	:	:	PUNCT
cana-1019	139	3	1	1	X
cana-1019	139	4	.	.	PUNCT
cana-1019	139	5	model	model	NOUN
cana-1019	139	6	hypothesis	hypothesis	NOUN
cana-1019	139	7	:	:	PUNCT
cana-1019	139	8	logistic	logistic	ADJ
cana-1019	139	9	regression	regression	NOUN
cana-1019	139	10	models	model	VERB
cana-1019	139	11	the	the	DET
cana-1019	139	12	probability	probability	NOUN
cana-1019	139	13	𝑃(𝑦_𝑖	𝑃(𝑦_𝑖	NOUN
cana-1019	139	14	=	=	NOUN
cana-1019	139	15	1	1	NUM
cana-1019	139	16	|	|	ADV
cana-1019	139	17	𝑥_𝑖	𝑥_𝑖	CCONJ
cana-1019	139	18	)	)	PUNCT
cana-1019	139	19	that	that	SCONJ
cana-1019	139	20	a	a	DET
cana-1019	139	21	sample	sample	NOUN
cana-1019	139	22	x_i	x_i	PUNCT
cana-1019	139	23	belongs	belong	VERB
cana-1019	139	24	to	to	ADP
cana-1019	139	25	class	class	NOUN
cana-1019	139	26	1	1	NUM
cana-1019	139	27	(	(	PUNCT
cana-1019	139	28	positive	positive	ADJ
cana-1019	139	29	mood	mood	NOUN
cana-1019	139	30	)	)	PUNCT
cana-1019	139	31	using	use	VERB
cana-1019	139	32	a	a	DET
cana-1019	139	33	sigmoid	sigmoid	NOUN
cana-1019	139	34	function	function	NOUN
cana-1019	139	35	:	:	PUNCT
cana-1019	140	1	𝑃(𝑦_𝑖	𝑃(𝑦_𝑖	NOUN
cana-1019	140	2	=	=	NOUN
cana-1019	140	3	1	1	NUM
cana-1019	140	4	|	|	ADV
cana-1019	140	5	𝑥_𝑖	𝑥_𝑖	NUM
cana-1019	140	6	;	;	PUNCT
cana-1019	140	7	𝑤	𝑤	X
cana-1019	140	8	,	,	PUNCT
cana-1019	140	9	𝑏	𝑏	NOUN
cana-1019	140	10	)	)	PUNCT
cana-1019	140	11	=	=	SYM
cana-1019	140	12	𝜎(𝑤^𝑇	𝜎(𝑤^𝑇	PROPN
cana-1019	140	13	𝑥_𝑖	𝑥_𝑖	PART
cana-1019	141	1	+	+	NUM
cana-1019	141	2	𝑏	𝑏	NOUN
cana-1019	141	3	)	)	PUNCT
cana-1019	141	4	2	2	NUM
cana-1019	141	5	.	.	PUNCT
cana-1019	141	6	cost	cost	NOUN
cana-1019	141	7	function	function	NOUN
cana-1019	141	8	(	(	PUNCT
cana-1019	141	9	log	log	NOUN
cana-1019	141	10	-	-	PUNCT
cana-1019	141	11	loss	loss	NOUN
cana-1019	142	1	):	):	PUNCT
cana-1019	142	2	the	the	DET
cana-1019	142	3	objective	objective	NOUN
cana-1019	142	4	is	be	AUX
cana-1019	142	5	to	to	PART
cana-1019	142	6	maximize	maximize	VERB
cana-1019	142	7	the	the	DET
cana-1019	142	8	likelihood	likelihood	NOUN
cana-1019	142	9	of	of	ADP
cana-1019	142	10	the	the	DET
cana-1019	142	11	observed	observe	VERB
cana-1019	142	12	data	datum	NOUN
cana-1019	142	13	.	.	PUNCT
cana-1019	143	1	the	the	DET
cana-1019	143	2	cost	cost	NOUN
cana-1019	143	3	function	function	NOUN
cana-1019	143	4	for	for	ADP
cana-1019	143	5	logistic	logistic	ADJ
cana-1019	143	6	regression	regression	NOUN
cana-1019	143	7	is	be	AUX
cana-1019	143	8	the	the	DET
cana-1019	143	9	log	log	NOUN
cana-1019	143	10	-	-	PUNCT
cana-1019	143	11	loss	loss	NOUN
cana-1019	143	12	function	function	NOUN
cana-1019	143	13	:	:	PUNCT
cana-1019	143	14	𝐽(𝑤	𝐽(𝑤	NOUN
cana-1019	143	15	,	,	PUNCT
cana-1019	143	16	𝑏	𝑏	NOUN
cana-1019	143	17	)	)	PUNCT
cana-1019	143	18	=	=	SYM
cana-1019	144	1	−	−	PROPN
cana-1019	144	2	1	1	NUM
cana-1019	144	3	𝑛	𝑛	DET
cana-1019	144	4	𝛴𝑖	𝛴𝑖	PROPN
cana-1019	144	5	=	=	SYM
cana-1019	144	6	1𝑛[𝑦𝑖	1𝑛[𝑦𝑖	PROPN
cana-1019	144	7	log(𝜎(𝑤𝑇𝑥𝑖	log(𝜎(𝑤𝑇𝑥𝑖	PROPN
cana-1019	144	8	+	+	CCONJ
cana-1019	144	9	𝑏	𝑏	NOUN
cana-1019	144	10	)	)	PUNCT
cana-1019	144	11	)	)	PUNCT
cana-1019	145	1	+	+	CCONJ
cana-1019	145	2	(	(	PUNCT
cana-1019	145	3	1	1	NUM
cana-1019	145	4	−	−	NOUN
cana-1019	145	5	𝑦𝑖	𝑦𝑖	PROPN
cana-1019	145	6	)	)	PUNCT
cana-1019	145	7	log(1	log(1	NOUN
cana-1019	146	1	−	−	NOUN
cana-1019	146	2	𝜎(𝑤𝑇𝑥𝑖	𝜎(𝑤𝑇𝑥𝑖	ADV
cana-1019	146	3	+	+	NUM
cana-1019	146	4	𝑏	𝑏	NOUN
cana-1019	146	5	)	)	PUNCT
cana-1019	146	6	)	)	PUNCT
cana-1019	146	7	]	]	PUNCT
cana-1019	147	1	3	3	X
cana-1019	147	2	.	.	X
cana-1019	147	3	gradient	gradient	ADJ
cana-1019	147	4	descent	descent	NOUN
cana-1019	147	5	:	:	PUNCT
cana-1019	147	6	communications	communication	NOUN
cana-1019	147	7	on	on	ADP
cana-1019	147	8	applied	apply	VERB
cana-1019	147	9	nonlinear	nonlinear	ADJ
cana-1019	147	10	analysis	analysis	NOUN
cana-1019	147	11	issn	issn	NOUN
cana-1019	147	12	:	:	PUNCT
cana-1019	147	13	1074	1074	NUM
cana-1019	147	14	-	-	PUNCT
cana-1019	147	15	133x	133x	NUM
cana-1019	147	16	vol	vol	NOUN
cana-1019	147	17	31	31	NUM
cana-1019	147	18	no	no	NOUN
cana-1019	147	19	.	.	PUNCT
cana-1019	148	1	5s	5s	NUM
cana-1019	148	2	(	(	PUNCT
cana-1019	148	3	2024	2024	NUM
cana-1019	148	4	)	)	PUNCT
cana-1019	148	5	242	242	NUM
cana-1019	149	1	https://internationalpubls.com	https://internationalpubls.com	X
cana-1019	149	2	update	update	VERB
cana-1019	149	3	the	the	DET
cana-1019	149	4	parameters	parameter	NOUN
cana-1019	149	5	w	w	PROPN
cana-1019	149	6	and	and	CCONJ
cana-1019	149	7	b	b	NOUN
cana-1019	149	8	iteratively	iteratively	ADV
cana-1019	149	9	to	to	PART
cana-1019	149	10	minimize	minimize	VERB
cana-1019	149	11	the	the	DET
cana-1019	149	12	cost	cost	NOUN
cana-1019	149	13	function	function	NOUN
cana-1019	149	14	:	:	PUNCT
cana-1019	149	15	𝑤𝑛𝑒𝑤	𝑤𝑛𝑒𝑤	NOUN
cana-1019	149	16	=	=	SYM
cana-1019	149	17	𝑤𝑜𝑙𝑑	𝑤𝑜𝑙𝑑	PROPN
cana-1019	149	18	–	–	PUNCT
cana-1019	149	19	𝛼	𝛼	X
cana-1019	149	20	∗	∗	NOUN
cana-1019	149	21	𝜕𝐽(𝑤	𝜕𝐽(𝑤	NOUN
cana-1019	149	22	,	,	PUNCT
cana-1019	149	23	𝑏	𝑏	NOUN
cana-1019	149	24	)	)	PUNCT
cana-1019	149	25	𝜕𝑤	𝜕𝑤	VERB
cana-1019	149	26	𝑏𝑛𝑒𝑤	𝑏𝑛𝑒𝑤	NOUN
cana-1019	149	27	=	=	PRON
cana-1019	150	1	𝑏𝑜𝑙𝑑	𝑏𝑜𝑙𝑑	VERB
cana-1019	150	2	−	−	PROPN
cana-1019	150	3	𝛼	𝛼	NOUN
cana-1019	150	4	∗	∗	NOUN
cana-1019	150	5	𝜕𝐽(𝑤	𝜕𝐽(𝑤	NOUN
cana-1019	150	6	,	,	PUNCT
cana-1019	150	7	𝑏	𝑏	NOUN
cana-1019	150	8	)	)	PUNCT
cana-1019	150	9	𝜕𝑏	𝜕𝑏	ADV
cana-1019	150	10	4	4	NUM
cana-1019	150	11	.	.	PUNCT
cana-1019	151	1	gradient	gradient	ADJ
cana-1019	151	2	calculation	calculation	NOUN
cana-1019	151	3	:	:	PUNCT
cana-1019	151	4	compute	compute	VERB
cana-1019	151	5	the	the	DET
cana-1019	151	6	gradients	gradient	NOUN
cana-1019	151	7	of	of	ADP
cana-1019	151	8	the	the	DET
cana-1019	151	9	cost	cost	NOUN
cana-1019	151	10	function	function	NOUN
cana-1019	151	11	with	with	ADP
cana-1019	151	12	respect	respect	NOUN
cana-1019	151	13	to	to	ADP
cana-1019	151	14	w	w	PROPN
cana-1019	151	15	and	and	CCONJ
cana-1019	151	16	b	b	NOUN
cana-1019	151	17	:	:	PUNCT
cana-1019	151	18	𝜕𝐽(𝑤	𝜕𝐽(𝑤	NOUN
cana-1019	151	19	,	,	PUNCT
cana-1019	151	20	𝑏	𝑏	NOUN
cana-1019	151	21	)	)	PUNCT
cana-1019	151	22	𝜕𝑤	𝜕𝑤	VERB
cana-1019	151	23	=	=	SYM
cana-1019	152	1	1	1	NUM
cana-1019	152	2	𝑛	𝑛	DET
cana-1019	152	3	𝛴𝑖	𝛴𝑖	PROPN
cana-1019	152	4	=	=	PUNCT
cana-1019	152	5	1𝑛(𝜎(𝑤𝑇𝑥𝑖	1𝑛(𝜎(𝑤𝑇𝑥𝑖	NOUN
cana-1019	153	1	+	+	CCONJ
cana-1019	153	2	𝑏	𝑏	NOUN
cana-1019	153	3	)	)	PUNCT
cana-1019	153	4	−	−	PROPN
cana-1019	153	5	𝑦𝑖	𝑦𝑖	PROPN
cana-1019	153	6	)	)	PUNCT
cana-1019	153	7	∗	∗	NOUN
cana-1019	153	8	𝑥𝑖	𝑥𝑖	ADP
cana-1019	153	9	𝜕𝐽(𝑤	𝜕𝐽(𝑤	NOUN
cana-1019	153	10	,	,	PUNCT
cana-1019	153	11	𝑏	𝑏	NOUN
cana-1019	153	12	)	)	PUNCT
cana-1019	153	13	𝜕𝑏	𝜕𝑏	PROPN
cana-1019	153	14	=	=	SYM
cana-1019	153	15	1	1	NUM
cana-1019	153	16	𝑛	𝑛	DET
cana-1019	153	17	𝛴𝑖	𝛴𝑖	PROPN
cana-1019	153	18	=	=	PUNCT
cana-1019	153	19	1𝑛(𝜎(𝑤𝑇𝑥𝑖	1𝑛(𝜎(𝑤𝑇𝑥𝑖	NOUN
cana-1019	154	1	+	+	CCONJ
cana-1019	154	2	𝑏	𝑏	NOUN
cana-1019	154	3	)	)	PUNCT
cana-1019	154	4	−	−	PROPN
cana-1019	154	5	𝑦𝑖	𝑦𝑖	PROPN
cana-1019	154	6	)	)	PUNCT
cana-1019	154	7	5	5	NUM
cana-1019	154	8	.	.	PUNCT
cana-1019	154	9	prediction	prediction	NOUN
cana-1019	154	10	:	:	PUNCT
cana-1019	154	11	after	after	ADP
cana-1019	154	12	training	training	NOUN
cana-1019	154	13	,	,	PUNCT
cana-1019	154	14	predict	predict	VERB
cana-1019	154	15	the	the	DET
cana-1019	154	16	probability	probability	NOUN
cana-1019	154	17	𝑃(𝑦	𝑃(𝑦	X
cana-1019	154	18	=	=	SYM
cana-1019	154	19	1	1	NUM
cana-1019	154	20	|	|	ADV
cana-1019	154	21	𝑥	𝑥	NOUN
cana-1019	154	22	)	)	PUNCT
cana-1019	154	23	for	for	ADP
cana-1019	154	24	a	a	DET
cana-1019	154	25	new	new	ADJ
cana-1019	154	26	sample	sample	NOUN
cana-1019	155	1	x	x	X
cana-1019	155	2	:	:	PUNCT
cana-1019	155	3	𝑃(𝑦	𝑃(𝑦	X
cana-1019	155	4	=	=	SYM
cana-1019	155	5	1	1	NUM
cana-1019	155	6	|	|	ADV
cana-1019	155	7	𝑥	𝑥	NOUN
cana-1019	155	8	;	;	PUNCT
cana-1019	155	9	𝑤	𝑤	ADP
cana-1019	155	10	,	,	PUNCT
cana-1019	155	11	𝑏	𝑏	NOUN
cana-1019	155	12	)	)	PUNCT
cana-1019	155	13	=	=	NOUN
cana-1019	156	1	𝜎(𝑤^𝑇	𝜎(𝑤^𝑇	PROPN
cana-1019	156	2	𝑥	𝑥	NOUN
cana-1019	156	3	+	+	CCONJ
cana-1019	156	4	𝑏	𝑏	NOUN
cana-1019	156	5	)	)	PUNCT
cana-1019	156	6	classify	classify	VERB
cana-1019	156	7	based	base	VERB
cana-1019	156	8	on	on	ADP
cana-1019	156	9	the	the	DET
cana-1019	156	10	probability	probability	NOUN
cana-1019	156	11	threshold	threshold	NOUN
cana-1019	156	12	(	(	PUNCT
cana-1019	156	13	e.g.	e.g.	ADV
cana-1019	156	14	,	,	PUNCT
cana-1019	156	15	0.5	0.5	NUM
cana-1019	156	16	)	)	PUNCT
cana-1019	156	17	.	.	PUNCT
cana-1019	157	1	6	6	X
cana-1019	157	2	.	.	X
cana-1019	157	3	regularization	regularization	NOUN
cana-1019	157	4	:	:	PUNCT
cana-1019	157	5	optionally	optionally	ADV
cana-1019	157	6	,	,	PUNCT
cana-1019	157	7	include	include	VERB
cana-1019	157	8	regularization	regularization	NOUN
cana-1019	157	9	to	to	PART
cana-1019	157	10	prevent	prevent	VERB
cana-1019	157	11	overfitting	overfitting	NOUN
cana-1019	157	12	:	:	PUNCT
cana-1019	157	13	𝐽(𝑤	𝐽(𝑤	NOUN
cana-1019	157	14	,	,	PUNCT
cana-1019	157	15	𝑏	𝑏	NOUN
cana-1019	157	16	)	)	PUNCT
cana-1019	157	17	=	=	SYM
cana-1019	157	18	−	−	PROPN
cana-1019	157	19	1	1	NUM
cana-1019	157	20	𝑛	𝑛	DET
cana-1019	157	21	𝛴𝑖	𝛴𝑖	PROPN
cana-1019	157	22	=	=	SYM
cana-1019	157	23	1𝑛[𝑦𝑖	1𝑛[𝑦𝑖	PROPN
cana-1019	157	24	log(𝜎(𝑤𝑇𝑥𝑖	log(𝜎(𝑤𝑇𝑥𝑖	PROPN
cana-1019	157	25	+	+	CCONJ
cana-1019	157	26	𝑏	𝑏	NOUN
cana-1019	157	27	)	)	PUNCT
cana-1019	157	28	)	)	PUNCT
cana-1019	158	1	+	+	CCONJ
cana-1019	158	2	(	(	PUNCT
cana-1019	158	3	1	1	NUM
cana-1019	158	4	−	−	NOUN
cana-1019	158	5	𝑦𝑖	𝑦𝑖	PROPN
cana-1019	158	6	)	)	PUNCT
cana-1019	158	7	log(1	log(1	NOUN
cana-1019	159	1	−	−	NOUN
cana-1019	159	2	𝜎(𝑤𝑇𝑥𝑖	𝜎(𝑤𝑇𝑥𝑖	ADV
cana-1019	159	3	+	+	NUM
cana-1019	159	4	𝑏	𝑏	NOUN
cana-1019	159	5	)	)	PUNCT
cana-1019	159	6	)	)	PUNCT
cana-1019	159	7	]	]	PUNCT
cana-1019	160	1	+	+	CCONJ
cana-1019	160	2	𝜆	𝜆	X
cana-1019	160	3	||𝑤||	||𝑤||	NOUN
cana-1019	160	4	2	2	NUM
cana-1019	160	5	where	where	SCONJ
cana-1019	160	6	λ	λ	PROPN
cana-1019	160	7	is	be	AUX
cana-1019	160	8	the	the	DET
cana-1019	160	9	regularization	regularization	NOUN
cana-1019	160	10	parameter	parameter	NOUN
cana-1019	160	11	and	and	CCONJ
cana-1019	160	12	||w||^2	||w||^2	NUM
cana-1019	160	13	is	be	AUX
cana-1019	160	14	the	the	DET
cana-1019	160	15	l2	l2	NOUN
cana-1019	160	16	norm	norm	NOUN
cana-1019	160	17	of	of	ADP
cana-1019	160	18	w.	w.	PROPN
cana-1019	160	19	b.	b.	PROPN
cana-1019	161	1	the	the	DET
cana-1019	161	2	stochastic	stochastic	ADJ
cana-1019	161	3	gradient	gradient	ADJ
cana-1019	161	4	descent	descent	NOUN
cana-1019	161	5	(	(	PUNCT
cana-1019	161	6	sgd	sgd	NOUN
cana-1019	161	7	)	)	PUNCT
cana-1019	161	8	classifier	classifier	NOUN
cana-1019	161	9	:	:	PUNCT
cana-1019	161	10	it	it	PRON
cana-1019	161	11	makes	make	VERB
cana-1019	161	12	linear	linear	PROPN
cana-1019	161	13	classifiers	classifier	NOUN
cana-1019	161	14	work	work	VERB
cana-1019	161	15	better	well	ADV
cana-1019	161	16	with	with	ADP
cana-1019	161	17	convex	convex	ADJ
cana-1019	161	18	loss	loss	NOUN
cana-1019	161	19	functions	function	NOUN
cana-1019	161	20	.	.	PUNCT
cana-1019	162	1	this	this	PRON
cana-1019	162	2	makes	make	VERB
cana-1019	162	3	it	it	PRON
cana-1019	162	4	useful	useful	ADJ
cana-1019	162	5	for	for	ADP
cana-1019	162	6	learning	learn	VERB
cana-1019	162	7	on	on	ADP
cana-1019	162	8	a	a	DET
cana-1019	162	9	big	big	ADJ
cana-1019	162	10	scale	scale	NOUN
cana-1019	162	11	.	.	PUNCT
cana-1019	163	1	it	it	PRON
cana-1019	163	2	changes	change	VERB
cana-1019	163	3	the	the	DET
cana-1019	163	4	model	model	NOUN
cana-1019	163	5	parameters	parameter	NOUN
cana-1019	163	6	over	over	ADV
cana-1019	163	7	and	and	CCONJ
cana-1019	163	8	over	over	ADV
cana-1019	163	9	,	,	PUNCT
cana-1019	163	10	which	which	PRON
cana-1019	163	11	works	work	VERB
cana-1019	163	12	well	well	ADV
cana-1019	163	13	for	for	ADP
cana-1019	163	14	situations	situation	NOUN
cana-1019	163	15	with	with	ADP
cana-1019	163	16	a	a	DET
cana-1019	163	17	lot	lot	NOUN
cana-1019	163	18	of	of	ADP
cana-1019	163	19	dimensions	dimension	NOUN
cana-1019	163	20	and	and	CCONJ
cana-1019	163	21	big	big	ADJ
cana-1019	163	22	datasets	dataset	NOUN
cana-1019	163	23	.	.	PUNCT
cana-1019	164	1	stochastic	stochastic	ADJ
cana-1019	164	2	gradient	gradient	ADJ
cana-1019	164	3	descent	descent	NOUN
cana-1019	164	4	(	(	PUNCT
cana-1019	164	5	sgd	sgd	NOUN
cana-1019	164	6	)	)	PUNCT
cana-1019	164	7	classifier	classifier	NOUN
cana-1019	164	8	:	:	PUNCT
cana-1019	164	9	1	1	X
cana-1019	164	10	.	.	PUNCT
cana-1019	164	11	model	model	NOUN
cana-1019	164	12	hypothesis	hypothesis	NOUN
cana-1019	164	13	:	:	PUNCT
cana-1019	164	14	the	the	DET
cana-1019	164	15	sgd	sgd	PROPN
cana-1019	164	16	classifier	classifier	NOUN
cana-1019	164	17	optimizes	optimize	VERB
cana-1019	164	18	a	a	DET
cana-1019	164	19	linear	linear	ADJ
cana-1019	164	20	model	model	NOUN
cana-1019	164	21	for	for	ADP
cana-1019	164	22	binary	binary	ADJ
cana-1019	164	23	classification	classification	NOUN
cana-1019	164	24	tasks	task	NOUN
cana-1019	164	25	using	use	VERB
cana-1019	164	26	a	a	DET
cana-1019	164	27	stochastic	stochastic	ADJ
cana-1019	164	28	gradient	gradient	ADJ
cana-1019	164	29	descent	descent	NOUN
cana-1019	164	30	approach	approach	NOUN
cana-1019	164	31	.	.	PUNCT
cana-1019	165	1	it	it	PRON
cana-1019	165	2	estimates	estimate	VERB
cana-1019	165	3	the	the	DET
cana-1019	165	4	𝑝𝑟𝑜𝑏𝑎𝑏𝑖𝑙𝑖𝑡𝑦	𝑝𝑟𝑜𝑏𝑎𝑏𝑖𝑙𝑖𝑡𝑦	NOUN
cana-1019	165	5	𝑃(𝑦_𝑖	𝑃(𝑦_𝑖	NOUN
cana-1019	165	6	=	=	NOUN
cana-1019	165	7	1	1	NUM
cana-1019	165	8	|	|	ADV
cana-1019	165	9	𝑥_𝑖	𝑥_𝑖	CCONJ
cana-1019	165	10	)	)	PUNCT
cana-1019	165	11	that	that	SCONJ
cana-1019	165	12	a	a	DET
cana-1019	165	13	sample	sample	NOUN
cana-1019	165	14	x_i	x_i	PUNCT
cana-1019	165	15	belongs	belong	VERB
cana-1019	165	16	to	to	ADP
cana-1019	165	17	class	class	NOUN
cana-1019	165	18	1	1	NUM
cana-1019	165	19	(	(	PUNCT
cana-1019	165	20	positive	positive	ADJ
cana-1019	165	21	mood	mood	NOUN
cana-1019	165	22	)	)	PUNCT
cana-1019	165	23	.	.	PUNCT
cana-1019	166	1	2	2	X
cana-1019	166	2	.	.	X
cana-1019	166	3	loss	loss	NOUN
cana-1019	166	4	function	function	NOUN
cana-1019	166	5	:	:	PUNCT
cana-1019	166	6	the	the	DET
cana-1019	166	7	objective	objective	NOUN
cana-1019	166	8	is	be	AUX
cana-1019	166	9	to	to	PART
cana-1019	166	10	minimize	minimize	VERB
cana-1019	166	11	the	the	DET
cana-1019	166	12	loss	loss	NOUN
cana-1019	166	13	function	function	NOUN
cana-1019	166	14	,	,	PUNCT
cana-1019	166	15	typically	typically	ADV
cana-1019	166	16	the	the	DET
cana-1019	166	17	logistic	logistic	ADJ
cana-1019	166	18	loss	loss	NOUN
cana-1019	166	19	for	for	ADP
cana-1019	166	20	binary	binary	ADJ
cana-1019	166	21	communications	communication	NOUN
cana-1019	166	22	on	on	ADP
cana-1019	166	23	applied	apply	VERB
cana-1019	166	24	nonlinear	nonlinear	ADJ
cana-1019	166	25	analysis	analysis	NOUN
cana-1019	166	26	issn	issn	NOUN
cana-1019	166	27	:	:	PUNCT
cana-1019	166	28	1074	1074	NUM
cana-1019	166	29	-	-	PUNCT
cana-1019	166	30	133x	133x	NUM
cana-1019	166	31	vol	vol	NOUN
cana-1019	166	32	31	31	NUM
cana-1019	166	33	no	no	NOUN
cana-1019	166	34	.	.	PUNCT
cana-1019	167	1	5s	5s	NUM
cana-1019	167	2	(	(	PUNCT
cana-1019	167	3	2024	2024	NUM
cana-1019	167	4	)	)	PUNCT
cana-1019	167	5	243	243	NUM
cana-1019	167	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1019	167	7	classification	classification	NOUN
cana-1019	167	8	:	:	PUNCT
cana-1019	167	9	𝐿(𝑤	𝐿(𝑤	NUM
cana-1019	167	10	,	,	PUNCT
cana-1019	167	11	𝑏	𝑏	NOUN
cana-1019	167	12	)	)	PUNCT
cana-1019	167	13	=	=	SYM
cana-1019	167	14	log(1	log(1	NOUN
cana-1019	168	1	+	+	CCONJ
cana-1019	168	2	exp(−𝑦𝑖	exp(−𝑦𝑖	ADJ
cana-1019	168	3	∗	∗	NOUN
cana-1019	168	4	(	(	PUNCT
cana-1019	168	5	𝑤𝑇𝑥𝑖	𝑤𝑇𝑥𝑖	PROPN
cana-1019	168	6	+	+	NOUN
cana-1019	168	7	𝑏	𝑏	NOUN
cana-1019	168	8	)	)	PUNCT
cana-1019	168	9	)	)	PUNCT
cana-1019	168	10	)	)	PUNCT
cana-1019	169	1	3	3	X
cana-1019	169	2	.	.	X
cana-1019	169	3	gradient	gradient	ADJ
cana-1019	169	4	calculation	calculation	NOUN
cana-1019	169	5	:	:	PUNCT
cana-1019	169	6	compute	compute	VERB
cana-1019	169	7	the	the	DET
cana-1019	169	8	gradient	gradient	NOUN
cana-1019	169	9	of	of	ADP
cana-1019	169	10	the	the	DET
cana-1019	169	11	loss	loss	NOUN
cana-1019	169	12	function	function	NOUN
cana-1019	169	13	with	with	ADP
cana-1019	169	14	respect	respect	NOUN
cana-1019	169	15	to	to	ADP
cana-1019	169	16	the	the	DET
cana-1019	169	17	parameters	parameter	NOUN
cana-1019	169	18	w	w	PROPN
cana-1019	169	19	(	(	PUNCT
cana-1019	169	20	weight	weight	NOUN
cana-1019	169	21	vector	vector	NOUN
cana-1019	169	22	)	)	PUNCT
cana-1019	169	23	and	and	CCONJ
cana-1019	169	24	b	b	X
cana-1019	169	25	(	(	PUNCT
cana-1019	169	26	bias	bias	NOUN
cana-1019	169	27	term	term	NOUN
cana-1019	169	28	):	):	PUNCT
cana-1019	169	29	𝜕𝐿(𝑤	𝜕𝐿(𝑤	NUM
cana-1019	169	30	,	,	PUNCT
cana-1019	169	31	𝑏	𝑏	NOUN
cana-1019	169	32	)	)	PUNCT
cana-1019	169	33	𝜕𝑤	𝜕𝑤	VERB
cana-1019	169	34	=	=	SYM
cana-1019	169	35	−𝑦𝑖	−𝑦𝑖	NOUN
cana-1019	169	36	∗	∗	NOUN
cana-1019	169	37	𝑥𝑖	𝑥𝑖	PROPN
cana-1019	170	1	(	(	PUNCT
cana-1019	170	2	1	1	NUM
cana-1019	170	3	+	+	NUM
cana-1019	170	4	exp(𝑦𝑖	exp(𝑦𝑖	NUM
cana-1019	170	5	∗	∗	NOUN
cana-1019	170	6	(	(	PUNCT
cana-1019	170	7	𝑤𝑇𝑥𝑖	𝑤𝑇𝑥𝑖	PROPN
cana-1019	170	8	+	+	NOUN
cana-1019	170	9	𝑏	𝑏	NOUN
cana-1019	170	10	)	)	PUNCT
cana-1019	170	11	)	)	PUNCT
cana-1019	170	12	)	)	PUNCT
cana-1019	171	1	𝜕𝐿(𝑤	𝜕𝐿(𝑤	NOUN
cana-1019	171	2	,	,	PUNCT
cana-1019	171	3	𝑏	𝑏	NOUN
cana-1019	171	4	)	)	PUNCT
cana-1019	171	5	𝜕𝑏	𝜕𝑏	PROPN
cana-1019	171	6	=	=	SYM
cana-1019	171	7	−	−	PROPN
cana-1019	171	8	𝑦𝑖	𝑦𝑖	PROPN
cana-1019	171	9	(	(	PUNCT
cana-1019	171	10	1	1	NUM
cana-1019	171	11	+	+	NUM
cana-1019	171	12	exp(𝑦𝑖	exp(𝑦𝑖	NUM
cana-1019	171	13	∗	∗	NOUN
cana-1019	171	14	(	(	PUNCT
cana-1019	171	15	𝑤𝑇𝑥𝑖	𝑤𝑇𝑥𝑖	PROPN
cana-1019	171	16	+	+	NOUN
cana-1019	171	17	𝑏	𝑏	NOUN
cana-1019	171	18	)	)	PUNCT
cana-1019	171	19	)	)	PUNCT
cana-1019	171	20	)	)	PUNCT
cana-1019	172	1	4	4	X
cana-1019	172	2	.	.	X
cana-1019	172	3	update	update	NOUN
cana-1019	172	4	parameters	parameter	NOUN
cana-1019	172	5	:	:	PUNCT
cana-1019	172	6	update	update	VERB
cana-1019	172	7	the	the	DET
cana-1019	172	8	parameters	parameter	NOUN
cana-1019	172	9	w	w	PROPN
cana-1019	172	10	and	and	CCONJ
cana-1019	172	11	b	b	PROPN
cana-1019	172	12	iteratively	iteratively	ADV
cana-1019	172	13	using	use	VERB
cana-1019	172	14	the	the	DET
cana-1019	172	15	gradients	gradient	NOUN
cana-1019	172	16	and	and	CCONJ
cana-1019	172	17	a	a	DET
cana-1019	172	18	learning	learning	NOUN
cana-1019	172	19	rate	rate	NOUN
cana-1019	172	20	α	α	NOUN
cana-1019	172	21	:	:	PUNCT
cana-1019	172	22	𝑤𝑛𝑒𝑤	𝑤𝑛𝑒𝑤	NOUN
cana-1019	172	23	=	=	SYM
cana-1019	172	24	𝑤𝑜𝑙𝑑	𝑤𝑜𝑙𝑑	PROPN
cana-1019	172	25	–	–	PUNCT
cana-1019	172	26	𝛼	𝛼	X
cana-1019	172	27	∗	∗	NOUN
cana-1019	172	28	𝜕𝐽(𝑤	𝜕𝐽(𝑤	NOUN
cana-1019	172	29	,	,	PUNCT
cana-1019	172	30	𝑏	𝑏	NOUN
cana-1019	172	31	)	)	PUNCT
cana-1019	172	32	𝜕𝑤	𝜕𝑤	VERB
cana-1019	172	33	𝑏𝑛𝑒𝑤	𝑏𝑛𝑒𝑤	NOUN
cana-1019	172	34	=	=	PRON
cana-1019	173	1	𝑏𝑜𝑙𝑑	𝑏𝑜𝑙𝑑	VERB
cana-1019	173	2	−	−	PROPN
cana-1019	173	3	𝛼	𝛼	NOUN
cana-1019	173	4	∗	∗	NOUN
cana-1019	173	5	𝜕𝐽(𝑤	𝜕𝐽(𝑤	NOUN
cana-1019	173	6	,	,	PUNCT
cana-1019	173	7	𝑏	𝑏	NOUN
cana-1019	173	8	)	)	PUNCT
cana-1019	173	9	𝜕𝑏	𝜕𝑏	ADV
cana-1019	173	10	5	5	NUM
cana-1019	173	11	.	.	PUNCT
cana-1019	174	1	prediction	prediction	NOUN
cana-1019	174	2	:	:	PUNCT
cana-1019	174	3	after	after	ADP
cana-1019	174	4	training	training	NOUN
cana-1019	174	5	,	,	PUNCT
cana-1019	174	6	predict	predict	VERB
cana-1019	174	7	the	the	DET
cana-1019	174	8	probability	probability	NOUN
cana-1019	174	9	p(y	p(y	PROPN
cana-1019	174	10	=	=	SYM
cana-1019	174	11	1	1	NUM
cana-1019	174	12	|	|	NOUN
cana-1019	174	13	x	x	NOUN
cana-1019	174	14	)	)	PUNCT
cana-1019	174	15	for	for	ADP
cana-1019	174	16	a	a	DET
cana-1019	174	17	new	new	ADJ
cana-1019	174	18	sample	sample	NOUN
cana-1019	174	19	x	x	PUNCT
cana-1019	174	20	using	use	VERB
cana-1019	174	21	the	the	DET
cana-1019	174	22	updated	update	VERB
cana-1019	174	23	parameters	parameter	NOUN
cana-1019	174	24	:	:	PUNCT
cana-1019	175	1	𝑃(𝑦	𝑃(𝑦	X
cana-1019	175	2	=	=	SYM
cana-1019	175	3	1	1	NUM
cana-1019	175	4	|	|	ADV
cana-1019	175	5	𝑥	𝑥	NOUN
cana-1019	175	6	;	;	PUNCT
cana-1019	175	7	𝑤	𝑤	ADP
cana-1019	175	8	,	,	PUNCT
cana-1019	175	9	𝑏	𝑏	NOUN
cana-1019	175	10	)	)	PUNCT
cana-1019	175	11	=	=	SYM
cana-1019	175	12	1	1	NUM
cana-1019	175	13	/	/	SYM
cana-1019	175	14	(	(	PUNCT
cana-1019	175	15	1	1	NUM
cana-1019	175	16	+	+	NUM
cana-1019	175	17	exp	exp	NOUN
cana-1019	175	18	(	(	PUNCT
cana-1019	175	19	−(𝑤^𝑇	−(𝑤^𝑇	NOUN
cana-1019	175	20	𝑥	𝑥	NOUN
cana-1019	175	21	+	+	CCONJ
cana-1019	175	22	𝑏	𝑏	NOUN
cana-1019	175	23	)	)	PUNCT
cana-1019	175	24	)	)	PUNCT
cana-1019	175	25	)	)	PUNCT
cana-1019	175	26	classify	classify	VERB
cana-1019	175	27	based	base	VERB
cana-1019	175	28	on	on	ADP
cana-1019	175	29	the	the	DET
cana-1019	175	30	probability	probability	NOUN
cana-1019	175	31	threshold	threshold	NOUN
cana-1019	175	32	(	(	PUNCT
cana-1019	175	33	e.g.	e.g.	ADV
cana-1019	175	34	,	,	PUNCT
cana-1019	175	35	0.5	0.5	NUM
cana-1019	175	36	)	)	PUNCT
cana-1019	175	37	.	.	PUNCT
cana-1019	176	1	c.	c.	PROPN
cana-1019	176	2	gaussian	gaussian	PROPN
cana-1019	176	3	naive	naive	ADJ
cana-1019	176	4	bayes	bayes	NOUN
cana-1019	176	5	:	:	PUNCT
cana-1019	176	6	it	it	PRON
cana-1019	176	7	assumes	assume	VERB
cana-1019	176	8	that	that	SCONJ
cana-1019	176	9	features	feature	NOUN
cana-1019	176	10	are	be	AUX
cana-1019	176	11	independent	independent	ADJ
cana-1019	176	12	of	of	ADP
cana-1019	176	13	each	each	DET
cana-1019	176	14	other	other	ADJ
cana-1019	176	15	and	and	CCONJ
cana-1019	176	16	uses	use	VERB
cana-1019	176	17	the	the	DET
cana-1019	176	18	gaussian	gaussian	ADJ
cana-1019	176	19	distribution	distribution	NOUN
cana-1019	176	20	for	for	ADP
cana-1019	176	21	features	feature	NOUN
cana-1019	176	22	that	that	PRON
cana-1019	176	23	are	be	AUX
cana-1019	176	24	continuous	continuous	ADJ
cana-1019	176	25	.	.	PUNCT
cana-1019	177	1	even	even	ADV
cana-1019	177	2	though	though	SCONJ
cana-1019	177	3	it	it	PRON
cana-1019	177	4	makes	make	VERB
cana-1019	177	5	some	some	DET
cana-1019	177	6	assumptions	assumption	NOUN
cana-1019	177	7	that	that	PRON
cana-1019	177	8	are	be	AUX
cana-1019	177	9	too	too	ADV
cana-1019	177	10	simple	simple	ADJ
cana-1019	177	11	,	,	PUNCT
cana-1019	177	12	it	it	PRON
cana-1019	177	13	does	do	VERB
cana-1019	177	14	well	well	ADV
cana-1019	177	15	at	at	ADP
cana-1019	177	16	many	many	ADJ
cana-1019	177	17	classification	classification	NOUN
cana-1019	177	18	tasks	task	NOUN
cana-1019	177	19	,	,	PUNCT
cana-1019	177	20	especially	especially	ADV
cana-1019	177	21	text	text	NOUN
cana-1019	177	22	classification	classification	NOUN
cana-1019	177	23	.	.	PUNCT
cana-1019	178	1	algorithm	algorithm	NOUN
cana-1019	178	2	:	:	PUNCT
cana-1019	178	3	1	1	X
cana-1019	178	4	.	.	PUNCT
cana-1019	178	5	model	model	NOUN
cana-1019	178	6	assumption	assumption	NOUN
cana-1019	178	7	:	:	PUNCT
cana-1019	178	8	gaussian	gaussian	ADJ
cana-1019	178	9	naive	naive	ADJ
cana-1019	178	10	bayes	bayes	PROPN
cana-1019	178	11	assumes	assume	VERB
cana-1019	178	12	that	that	SCONJ
cana-1019	178	13	features	feature	NOUN
cana-1019	178	14	are	be	AUX
cana-1019	178	15	conditionally	conditionally	ADV
cana-1019	178	16	independent	independent	ADJ
cana-1019	178	17	given	give	VERB
cana-1019	178	18	the	the	DET
cana-1019	178	19	class	class	NOUN
cana-1019	178	20	label	label	NOUN
cana-1019	178	21	y.	y.	NOUN
cana-1019	178	22	it	it	PRON
cana-1019	178	23	models	model	VERB
cana-1019	178	24	the	the	DET
cana-1019	178	25	likelihood	likelihood	NOUN
cana-1019	178	26	of	of	ADP
cana-1019	178	27	observing	observe	VERB
cana-1019	178	28	feature	feature	NOUN
cana-1019	178	29	values	value	NOUN
cana-1019	178	30	𝑥_𝑖	𝑥_𝑖	PUNCT
cana-1019	179	1	=	=	PRON
cana-1019	179	2	(	(	PUNCT
cana-1019	179	3	𝑥_{𝑖1	𝑥_{𝑖1	NOUN
cana-1019	179	4	}	}	PUNCT
cana-1019	179	5	,	,	PUNCT
cana-1019	179	6	𝑥_{𝑖2	𝑥_{𝑖2	NOUN
cana-1019	179	7	}	}	PUNCT
cana-1019	179	8	,	,	PUNCT
cana-1019	179	9	.	.	PUNCT
cana-1019	179	10	.	.	PUNCT
cana-1019	179	11	.	.	PUNCT
cana-1019	180	1	,	,	PUNCT
cana-1019	180	2	𝑥_{𝑖𝑑	𝑥_{𝑖𝑑	PROPN
cana-1019	180	3	}	}	PUNCT
cana-1019	180	4	)	)	PUNCT
cana-1019	180	5	given	give	VERB
cana-1019	180	6	class	class	NOUN
cana-1019	180	7	y_i	y_i	NOUN
cana-1019	180	8	using	use	VERB
cana-1019	180	9	gaussian	gaussian	ADJ
cana-1019	180	10	distribution	distribution	NOUN
cana-1019	180	11	:	:	PUNCT
cana-1019	180	12	𝑃(𝑥_𝑖	𝑃(𝑥_𝑖	PROPN
cana-1019	180	13	|	|	CCONJ
cana-1019	180	14	𝑦_𝑖	𝑦_𝑖	NUM
cana-1019	180	15	,	,	PUNCT
cana-1019	180	16	𝜃_{𝑦_𝑖	𝜃_{𝑦_𝑖	NOUN
cana-1019	180	17	}	}	PUNCT
cana-1019	180	18	)	)	PUNCT
cana-1019	180	19	=	=	SYM
cana-1019	181	1	𝛱_{𝑗	𝛱_{𝑗	NUM
cana-1019	181	2	=	=	SYM
cana-1019	181	3	1}^{𝑑	1}^{𝑑	X
cana-1019	181	4	}	}	PUNCT
cana-1019	181	5	𝑃(𝑥	𝑃(𝑥	PRON
cana-1019	181	6	{	{	PUNCT
cana-1019	181	7	𝑖𝑗}|	𝑖𝑗}|	NUM
cana-1019	181	8	𝑦𝑖,𝜃{𝑦𝑖	𝑦𝑖,𝜃{𝑦𝑖	NOUN
cana-1019	181	9	}	}	PUNCT
cana-1019	181	10	)	)	PUNCT
cana-1019	181	11	)	)	PUNCT
cana-1019	182	1	2	2	X
cana-1019	182	2	.	.	X
cana-1019	182	3	parameter	parameter	PROPN
cana-1019	182	4	estimation	estimation	PROPN
cana-1019	182	5	:	:	PUNCT
cana-1019	182	6	estimate	estimate	VERB
cana-1019	182	7	the	the	DET
cana-1019	182	8	parameters	parameter	NOUN
cana-1019	182	9	θ_{y_i	θ_{y_i	NOUN
cana-1019	182	10	}	}	PUNCT
cana-1019	182	11	of	of	ADP
cana-1019	182	12	the	the	DET
cana-1019	182	13	gaussian	gaussian	ADJ
cana-1019	182	14	distribution	distribution	NOUN
cana-1019	182	15	for	for	ADP
cana-1019	182	16	each	each	DET
cana-1019	182	17	class	class	NOUN
cana-1019	182	18	y_i	y_i	NOUN
cana-1019	182	19	:	:	PUNCT
cana-1019	182	20	communications	communication	NOUN
cana-1019	182	21	on	on	ADP
cana-1019	182	22	applied	apply	VERB
cana-1019	182	23	nonlinear	nonlinear	ADJ
cana-1019	182	24	analysis	analysis	NOUN
cana-1019	182	25	issn	issn	NOUN
cana-1019	182	26	:	:	PUNCT
cana-1019	182	27	1074	1074	NUM
cana-1019	182	28	-	-	PUNCT
cana-1019	182	29	133x	133x	NUM
cana-1019	182	30	vol	vol	NOUN
cana-1019	182	31	31	31	NUM
cana-1019	182	32	no	no	NOUN
cana-1019	182	33	.	.	PUNCT
cana-1019	183	1	5s	5s	NUM
cana-1019	183	2	(	(	PUNCT
cana-1019	183	3	2024	2024	NUM
cana-1019	183	4	)	)	PUNCT
cana-1019	183	5	244	244	NUM
cana-1019	183	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1019	183	7	−	−	PROPN
cana-1019	184	1	𝑀𝑒𝑎𝑛	𝑀𝑒𝑎𝑛	PROPN
cana-1019	184	2	𝜇{𝑦𝑖,𝑗	𝜇{𝑦𝑖,𝑗	NOUN
cana-1019	184	3	}	}	PUNCT
cana-1019	184	4	=	=	SYM
cana-1019	184	5	1	1	NUM
cana-1019	184	6	|{𝑖	|{𝑖	NOUN
cana-1019	184	7	∶	∶	NOUN
cana-1019	184	8	𝑦𝑖	𝑦𝑖	PROPN
cana-1019	184	9	=	=	SYM
cana-1019	184	10	𝑦}|𝛴{𝑖:𝑦𝑖=𝑦}𝑥{𝑖𝑗	𝑦}|𝛴{𝑖:𝑦𝑖=𝑦}𝑥{𝑖𝑗	PROPN
cana-1019	184	11	}	}	PUNCT
cana-1019	184	12	−	−	PROPN
cana-1019	184	13	𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒	𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒	PROPN
cana-1019	184	14	𝜎{𝑦𝑖,𝑗	𝜎{𝑦𝑖,𝑗	NOUN
cana-1019	184	15	}	}	PUNCT
cana-1019	184	16	2	2	NUM
cana-1019	184	17	=	=	SYM
cana-1019	184	18	1	1	NUM
cana-1019	184	19	|{𝑖	|{𝑖	NOUN
cana-1019	184	20	∶	∶	NOUN
cana-1019	184	21	𝑦𝑖	𝑦𝑖	X
cana-1019	184	22	=	=	PRON
cana-1019	184	23	𝑦}|𝛴	𝑦}|𝛴	PUNCT
cana-1019	184	24	{	{	PUNCT
cana-1019	184	25	𝑖:𝑦𝑖=𝑦}(𝑥{𝑖𝑗}−	𝑖:𝑦𝑖=𝑦}(𝑥{𝑖𝑗}−	X
cana-1019	184	26	𝜇{𝑦𝑖,𝑗	𝜇{𝑦𝑖,𝑗	NOUN
cana-1019	184	27	}	}	PUNCT
cana-1019	184	28	)	)	PUNCT
cana-1019	184	29	2	2	NUM
cana-1019	184	30	3	3	NUM
cana-1019	184	31	.	.	PUNCT
cana-1019	184	32	class	class	NOUN
cana-1019	184	33	prior	prior	ADJ
cana-1019	184	34	probability	probability	NOUN
cana-1019	184	35	:	:	PUNCT
cana-1019	184	36	estimate	estimate	VERB
cana-1019	184	37	the	the	DET
cana-1019	184	38	prior	prior	ADJ
cana-1019	184	39	probability	probability	NOUN
cana-1019	184	40	p(y_i	p(y_i	PROPN
cana-1019	184	41	):	):	PUNCT
cana-1019	184	42	𝑃(𝑦𝑖	𝑃(𝑦𝑖	X
cana-1019	184	43	)	)	PUNCT
cana-1019	184	44	=	=	SYM
cana-1019	184	45	|{𝑖	|{𝑖	NOUN
cana-1019	184	46	∶	∶	NOUN
cana-1019	184	47	𝑦𝑖	𝑦𝑖	X
cana-1019	184	48	=	=	SYM
cana-1019	184	49	𝑦}|	𝑦}|	NOUN
cana-1019	184	50	𝑛	𝑛	VERB
cana-1019	184	51	where	where	SCONJ
cana-1019	184	52	n	n	PRON
cana-1019	184	53	is	be	AUX
cana-1019	184	54	the	the	DET
cana-1019	184	55	total	total	ADJ
cana-1019	184	56	number	number	NOUN
cana-1019	184	57	of	of	ADP
cana-1019	184	58	samples	sample	NOUN
cana-1019	184	59	.	.	PUNCT
cana-1019	185	1	4	4	X
cana-1019	185	2	.	.	X
cana-1019	185	3	predictive	predictive	ADJ
cana-1019	185	4	probability	probability	NOUN
cana-1019	185	5	:	:	PUNCT
cana-1019	185	6	calculate	calculate	VERB
cana-1019	185	7	the	the	DET
cana-1019	185	8	posterior	posterior	ADJ
cana-1019	185	9	probability	probability	NOUN
cana-1019	185	10	p(y_i	p(y_i	PROPN
cana-1019	185	11	|	|	NOUN
cana-1019	185	12	x_i	x_i	X
cana-1019	185	13	)	)	PUNCT
cana-1019	185	14	using	use	VERB
cana-1019	185	15	bayes	bayes	PROPN
cana-1019	185	16	'	'	PART
cana-1019	185	17	theorem	theorem	NOUN
cana-1019	185	18	:	:	PUNCT
cana-1019	185	19	𝑃(𝑦_𝑖	𝑃(𝑦_𝑖	ADV
cana-1019	185	20	|	|	ADV
cana-1019	185	21	𝑥_𝑖	𝑥_𝑖	NUM
cana-1019	185	22	)	)	PUNCT
cana-1019	185	23	∝	∝	PROPN
cana-1019	185	24	𝑃(𝑦_𝑖	𝑃(𝑦_𝑖	PROPN
cana-1019	185	25	)	)	PUNCT
cana-1019	185	26	𝛱_{𝑗	𝛱_{𝑗	NUM
cana-1019	185	27	=	=	SYM
cana-1019	185	28	1}^{𝑑	1}^{𝑑	X
cana-1019	185	29	}	}	PUNCT
cana-1019	185	30	𝑃(𝑥	𝑃(𝑥	PRON
cana-1019	185	31	{	{	PUNCT
cana-1019	185	32	𝑖𝑗}|	𝑖𝑗}|	NUM
cana-1019	185	33	𝑦𝑖,𝜃{𝑦𝑖	𝑦𝑖,𝜃{𝑦𝑖	NOUN
cana-1019	185	34	}	}	PUNCT
cana-1019	185	35	)	)	PUNCT
cana-1019	185	36	)	)	PUNCT
cana-1019	186	1	substituting	substitute	VERB
cana-1019	186	2	the	the	DET
cana-1019	186	3	gaussian	gaussian	ADJ
cana-1019	186	4	distribution	distribution	NOUN
cana-1019	186	5	:	:	PUNCT
cana-1019	186	6	𝑃(𝑦_𝑖	𝑃(𝑦_𝑖	ADV
cana-1019	186	7	|	|	ADV
cana-1019	186	8	𝑥_𝑖	𝑥_𝑖	NUM
cana-1019	186	9	)	)	PUNCT
cana-1019	186	10	∝	∝	PROPN
cana-1019	186	11	𝑃(𝑦_𝑖	𝑃(𝑦_𝑖	PROPN
cana-1019	186	12	)	)	PUNCT
cana-1019	186	13	𝛱_{𝑗	𝛱_{𝑗	NUM
cana-1019	186	14	=	=	SYM
cana-1019	186	15	(	(	PUNCT
cana-1019	186	16	1	1	NUM
cana-1019	186	17	𝑠𝑞𝑟𝑡(2𝜋𝜎{𝑦𝑖,𝑗	𝑠𝑞𝑟𝑡(2𝜋𝜎{𝑦𝑖,𝑗	NOUN
cana-1019	186	18	}	}	PUNCT
cana-1019	186	19	2	2	NUM
cana-1019	186	20	)	)	PUNCT
cana-1019	186	21	)	)	PUNCT
cana-1019	186	22	∗	∗	NOUN
cana-1019	186	23	exp	exp	NOUN
cana-1019	186	24	(	(	PUNCT
cana-1019	186	25	−	−	PROPN
cana-1019	186	26	(	(	PUNCT
cana-1019	186	27	𝑥{𝑖𝑗	𝑥{𝑖𝑗	PROPN
cana-1019	186	28	}	}	PUNCT
cana-1019	186	29	−	−	PROPN
cana-1019	186	30	𝜇{𝑦𝑖,𝑗	𝜇{𝑦𝑖,𝑗	NOUN
cana-1019	186	31	}	}	PUNCT
cana-1019	186	32	)	)	PUNCT
cana-1019	186	33	2	2	NUM
cana-1019	186	34	(	(	PUNCT
cana-1019	186	35	2𝜎{𝑦𝑖,𝑗	2𝜎{𝑦𝑖,𝑗	NOUN
cana-1019	186	36	}	}	SYM
cana-1019	186	37	2	2	NUM
cana-1019	186	38	)	)	PUNCT
cana-1019	186	39	)	)	PUNCT
cana-1019	186	40	5	5	X
cana-1019	186	41	.	.	X
cana-1019	186	42	prediction	prediction	NOUN
cana-1019	186	43	:	:	PUNCT
cana-1019	186	44	classify	classify	VERB
cana-1019	186	45	a	a	DET
cana-1019	186	46	new	new	ADJ
cana-1019	186	47	sample	sample	NOUN
cana-1019	186	48	x_i	x_i	X
cana-1019	186	49	by	by	ADP
cana-1019	186	50	selecting	select	VERB
cana-1019	186	51	the	the	DET
cana-1019	186	52	class	class	NOUN
cana-1019	186	53	y_i	y_i	PROPN
cana-1019	186	54	that	that	PRON
cana-1019	186	55	maximizes	maximize	VERB
cana-1019	186	56	the	the	DET
cana-1019	186	57	posterior	posterior	ADJ
cana-1019	186	58	probability	probability	NOUN
cana-1019	186	59	p(y_i	p(y_i	PROPN
cana-1019	186	60	|	|	ADV
cana-1019	186	61	x_i	x_i	X
cana-1019	186	62	):	):	PUNCT
cana-1019	186	63	𝑦_𝑖	𝑦_𝑖	NUM
cana-1019	186	64	∗	∗	NOUN
cana-1019	186	65	=	=	SYM
cana-1019	186	66	𝑎𝑟𝑔𝑚𝑎𝑥{𝑦𝑖}𝑃(𝑦𝑖)𝛱{𝑗=1	𝑎𝑟𝑔𝑚𝑎𝑥{𝑦𝑖}𝑃(𝑦𝑖)𝛱{𝑗=1	NOUN
cana-1019	186	67	}	}	PUNCT
cana-1019	186	68	𝑃(𝑥_{𝑖𝑗	𝑃(𝑥_{𝑖𝑗	NUM
cana-1019	186	69	}	}	PUNCT
cana-1019	186	70	|	|	ADV
cana-1019	186	71	𝑦_𝑖	𝑦_𝑖	NUM
cana-1019	186	72	,	,	PUNCT
cana-1019	186	73	𝜃_{𝑦_𝑖	𝜃_{𝑦_𝑖	NOUN
cana-1019	186	74	}	}	PUNCT
cana-1019	186	75	)	)	PUNCT
cana-1019	186	76	d.	d.	PROPN
cana-1019	186	77	decision	decision	NOUN
cana-1019	186	78	tree	tree	NOUN
cana-1019	186	79	:	:	PUNCT
cana-1019	186	80	decision	decision	NOUN
cana-1019	186	81	trees	tree	NOUN
cana-1019	186	82	use	use	VERB
cana-1019	186	83	feature	feature	NOUN
cana-1019	186	84	levels	level	NOUN
cana-1019	186	85	to	to	PART
cana-1019	186	86	repeatedly	repeatedly	ADV
cana-1019	186	87	divide	divide	VERB
cana-1019	186	88	data	datum	NOUN
cana-1019	186	89	into	into	ADP
cana-1019	186	90	groups	group	NOUN
cana-1019	186	91	,	,	PUNCT
cana-1019	186	92	making	make	VERB
cana-1019	186	93	a	a	DET
cana-1019	186	94	structure	structure	NOUN
cana-1019	186	95	that	that	PRON
cana-1019	186	96	looks	look	VERB
cana-1019	186	97	like	like	ADP
cana-1019	186	98	a	a	DET
cana-1019	186	99	tree	tree	NOUN
cana-1019	186	100	.	.	PUNCT
cana-1019	187	1	they	they	PRON
cana-1019	187	2	are	be	AUX
cana-1019	187	3	easy	easy	ADJ
cana-1019	187	4	to	to	PART
cana-1019	187	5	understand	understand	VERB
cana-1019	187	6	and	and	CCONJ
cana-1019	187	7	use	use	VERB
cana-1019	187	8	,	,	PUNCT
cana-1019	187	9	and	and	CCONJ
cana-1019	187	10	they	they	PRON
cana-1019	187	11	can	can	AUX
cana-1019	187	12	capture	capture	VERB
cana-1019	187	13	complex	complex	ADJ
cana-1019	187	14	relationships	relationship	NOUN
cana-1019	187	15	between	between	ADP
cana-1019	187	16	traits	trait	NOUN
cana-1019	187	17	.	.	PUNCT
cana-1019	188	1	however	however	ADV
cana-1019	188	2	,	,	PUNCT
cana-1019	188	3	they	they	PRON
cana-1019	188	4	tend	tend	VERB
cana-1019	188	5	to	to	PART
cana-1019	188	6	overfit	overfit	VERB
cana-1019	188	7	without	without	ADP
cana-1019	188	8	being	be	AUX
cana-1019	188	9	pruned	prune	VERB
cana-1019	188	10	.	.	PUNCT
cana-1019	189	1	decision	decision	NOUN
cana-1019	189	2	tree	tree	NOUN
cana-1019	189	3	model	model	NOUN
cana-1019	189	4	for	for	ADP
cana-1019	189	5	mood	mood	NOUN
cana-1019	189	6	analysis	analysis	NOUN
cana-1019	189	7	:	:	PUNCT
cana-1019	189	8	1	1	X
cana-1019	189	9	.	.	X
cana-1019	189	10	tree	tree	NOUN
cana-1019	189	11	construction	construction	NOUN
cana-1019	189	12	:	:	PUNCT
cana-1019	189	13	decision	decision	NOUN
cana-1019	189	14	trees	tree	NOUN
cana-1019	189	15	recursively	recursively	ADV
cana-1019	189	16	partition	partition	VERB
cana-1019	189	17	the	the	DET
cana-1019	189	18	feature	feature	NOUN
cana-1019	189	19	space	space	NOUN
cana-1019	189	20	into	into	ADP
cana-1019	189	21	disjoint	disjoint	ADJ
cana-1019	189	22	regions	region	NOUN
cana-1019	189	23	by	by	ADP
cana-1019	189	24	selecting	select	VERB
cana-1019	189	25	feature	feature	NOUN
cana-1019	189	26	thresholds	threshold	NOUN
cana-1019	189	27	that	that	PRON
cana-1019	189	28	maximize	maximize	VERB
cana-1019	189	29	information	information	NOUN
cana-1019	189	30	gain	gain	NOUN
cana-1019	189	31	or	or	CCONJ
cana-1019	189	32	minimize	minimize	VERB
cana-1019	189	33	impurity	impurity	NOUN
cana-1019	189	34	measures	measure	NOUN
cana-1019	189	35	.	.	PUNCT
cana-1019	190	1	2	2	X
cana-1019	190	2	.	.	NOUN
cana-1019	190	3	splitting	splitting	NOUN
cana-1019	190	4	criterion	criterion	NOUN
cana-1019	190	5	:	:	PUNCT
cana-1019	190	6	at	at	ADP
cana-1019	190	7	each	each	DET
cana-1019	190	8	node	node	NOUN
cana-1019	190	9	,	,	PUNCT
cana-1019	190	10	choose	choose	VERB
cana-1019	190	11	the	the	DET
cana-1019	190	12	split	split	NOUN
cana-1019	190	13	that	that	PRON
cana-1019	190	14	best	good	ADJ
cana-1019	190	15	separates	separate	VERB
cana-1019	190	16	the	the	DET
cana-1019	190	17	data	datum	NOUN
cana-1019	190	18	based	base	VERB
cana-1019	190	19	on	on	ADP
cana-1019	190	20	a	a	DET
cana-1019	190	21	chosen	choose	VERB
cana-1019	190	22	criterion	criterion	NOUN
cana-1019	190	23	(	(	PUNCT
cana-1019	190	24	e.g.	e.g.	ADV
cana-1019	190	25	,	,	PUNCT
cana-1019	190	26	gini	gini	NOUN
cana-1019	190	27	impurity	impurity	NOUN
cana-1019	190	28	,	,	PUNCT
cana-1019	190	29	entropy	entropy	NOUN
cana-1019	190	30	,	,	PUNCT
cana-1019	190	31	or	or	CCONJ
cana-1019	190	32	misclassification	misclassification	NOUN
cana-1019	190	33	error	error	NOUN
cana-1019	190	34	)	)	PUNCT
cana-1019	190	35	.	.	PUNCT
cana-1019	191	1	communications	communication	NOUN
cana-1019	191	2	on	on	ADP
cana-1019	191	3	applied	apply	VERB
cana-1019	191	4	nonlinear	nonlinear	ADJ
cana-1019	191	5	analysis	analysis	NOUN
cana-1019	191	6	issn	issn	NOUN
cana-1019	191	7	:	:	PUNCT
cana-1019	191	8	1074	1074	NUM
cana-1019	191	9	-	-	PUNCT
cana-1019	191	10	133x	133x	NUM
cana-1019	191	11	vol	vol	NOUN
cana-1019	191	12	31	31	NUM
cana-1019	191	13	no	no	NOUN
cana-1019	191	14	.	.	PUNCT
cana-1019	192	1	5s	5s	NUM
cana-1019	192	2	(	(	PUNCT
cana-1019	192	3	2024	2024	NUM
cana-1019	192	4	)	)	PUNCT
cana-1019	192	5	245	245	NUM
cana-1019	192	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1019	192	7	gini	gini	NOUN
cana-1019	192	8	impurity	impurity	NOUN
cana-1019	192	9	:	:	PUNCT
cana-1019	192	10	𝐺𝑖𝑛𝑖(𝐷	𝐺𝑖𝑛𝑖(𝐷	PROPN
cana-1019	192	11	)	)	PUNCT
cana-1019	192	12	=	=	SYM
cana-1019	192	13	1	1	NUM
cana-1019	192	14	−	−	NOUN
cana-1019	192	15	𝛴{𝑖=1	𝛴{𝑖=1	NUM
cana-1019	192	16	}	}	PUNCT
cana-1019	192	17	{	{	PUNCT
cana-1019	192	18	𝑘}(𝑝𝑖)2	𝑘}(𝑝𝑖)2	ADJ
cana-1019	192	19	where	where	SCONJ
cana-1019	192	20	p_i	p_i	PROPN
cana-1019	192	21	is	be	AUX
cana-1019	192	22	the	the	DET
cana-1019	192	23	probability	probability	NOUN
cana-1019	192	24	of	of	ADP
cana-1019	192	25	class	class	NOUN
cana-1019	192	26	i	i	PROPN
cana-1019	192	27	in	in	ADP
cana-1019	192	28	node	node	PROPN
cana-1019	192	29	d.	d.	PROPN
cana-1019	192	30	entropy	entropy	PROPN
cana-1019	192	31	:	:	PUNCT
cana-1019	192	32	𝐸𝑛𝑡𝑟𝑜𝑝𝑦(𝐷	𝐸𝑛𝑡𝑟𝑜𝑝𝑦(𝐷	NOUN
cana-1019	192	33	)	)	PUNCT
cana-1019	192	34	=	=	SYM
cana-1019	193	1	−	−	PROPN
cana-1019	193	2	𝛴	𝛴	PROPN
cana-1019	193	3	{	{	PUNCT
cana-1019	193	4	𝑖=1	𝑖=1	PROPN
cana-1019	193	5	}	}	PUNCT
cana-1019	193	6	𝑖	𝑖	NOUN
cana-1019	193	7	{	{	PUNCT
cana-1019	193	8	𝑘}𝑝	𝑘}𝑝	PROPN
cana-1019	193	9	𝑙𝑜𝑔2(𝑝𝑖	𝑙𝑜𝑔2(𝑝𝑖	PROPN
cana-1019	193	10	)	)	PUNCT
cana-1019	193	11	where	where	SCONJ
cana-1019	193	12	p_i	p_i	PROPN
cana-1019	193	13	is	be	AUX
cana-1019	193	14	the	the	DET
cana-1019	193	15	probability	probability	NOUN
cana-1019	193	16	of	of	ADP
cana-1019	193	17	class	class	NOUN
cana-1019	193	18	i	i	PROPN
cana-1019	193	19	in	in	ADP
cana-1019	193	20	node	node	PROPN
cana-1019	193	21	d.	d.	PROPN
cana-1019	193	22	misclassification	misclassification	NOUN
cana-1019	193	23	error	error	NOUN
cana-1019	193	24	:	:	PUNCT
cana-1019	193	25	𝑀𝑖𝑠𝑐𝑙𝑎𝑠𝑠𝑖𝑓𝑖𝑐𝑎𝑡𝑖𝑜𝑛𝐸𝑟𝑟𝑜𝑟(𝐷	𝑀𝑖𝑠𝑐𝑙𝑎𝑠𝑠𝑖𝑓𝑖𝑐𝑎𝑡𝑖𝑜𝑛𝐸𝑟𝑟𝑜𝑟(𝐷	NOUN
cana-1019	193	26	)	)	PUNCT
cana-1019	193	27	=	=	SYM
cana-1019	193	28	1	1	NUM
cana-1019	193	29	−	−	NOUN
cana-1019	193	30	𝑚𝑎𝑥(𝑝_𝑖	𝑚𝑎𝑥(𝑝_𝑖	NOUN
cana-1019	193	31	)	)	PUNCT
cana-1019	193	32	where	where	SCONJ
cana-1019	193	33	p_i	p_i	PROPN
cana-1019	193	34	is	be	AUX
cana-1019	193	35	the	the	DET
cana-1019	193	36	maximum	maximum	ADJ
cana-1019	193	37	probability	probability	NOUN
cana-1019	193	38	of	of	ADP
cana-1019	193	39	class	class	NOUN
cana-1019	193	40	i	i	PROPN
cana-1019	193	41	in	in	ADP
cana-1019	193	42	node	node	PROPN
cana-1019	193	43	d.	d.	PROPN
cana-1019	193	44	3	3	NUM
cana-1019	193	45	.	.	PUNCT
cana-1019	193	46	recursive	recursive	ADJ
cana-1019	193	47	splitting	splitting	NOUN
cana-1019	193	48	:	:	PUNCT
cana-1019	193	49	recursively	recursively	ADV
cana-1019	193	50	split	split	VERB
cana-1019	193	51	the	the	DET
cana-1019	193	52	data	datum	NOUN
cana-1019	193	53	until	until	SCONJ
cana-1019	193	54	a	a	DET
cana-1019	193	55	stopping	stopping	NOUN
cana-1019	193	56	criterion	criterion	NOUN
cana-1019	193	57	is	be	AUX
cana-1019	193	58	met	meet	VERB
cana-1019	193	59	,	,	PUNCT
cana-1019	193	60	such	such	ADJ
cana-1019	193	61	as	as	ADP
cana-1019	193	62	maximum	maximum	ADJ
cana-1019	193	63	tree	tree	NOUN
cana-1019	193	64	depth	depth	NOUN
cana-1019	193	65	,	,	PUNCT
cana-1019	193	66	minimum	minimum	ADJ
cana-1019	193	67	samples	sample	NOUN
cana-1019	193	68	per	per	ADP
cana-1019	193	69	leaf	leaf	NOUN
cana-1019	193	70	,	,	PUNCT
cana-1019	193	71	or	or	CCONJ
cana-1019	193	72	no	no	DET
cana-1019	193	73	further	further	ADJ
cana-1019	193	74	gain	gain	NOUN
cana-1019	193	75	in	in	ADP
cana-1019	193	76	impurity	impurity	NOUN
cana-1019	193	77	reduction	reduction	NOUN
cana-1019	193	78	.	.	PUNCT
cana-1019	194	1	4	4	X
cana-1019	194	2	.	.	X
cana-1019	194	3	prediction	prediction	NOUN
cana-1019	194	4	:	:	PUNCT
cana-1019	194	5	assign	assign	VERB
cana-1019	194	6	the	the	DET
cana-1019	194	7	majority	majority	NOUN
cana-1019	194	8	class	class	NOUN
cana-1019	194	9	of	of	ADP
cana-1019	194	10	training	training	NOUN
cana-1019	194	11	samples	sample	NOUN
cana-1019	194	12	in	in	ADP
cana-1019	194	13	each	each	DET
cana-1019	194	14	leaf	leaf	NOUN
cana-1019	194	15	node	node	NOUN
cana-1019	194	16	as	as	ADP
cana-1019	194	17	the	the	DET
cana-1019	194	18	predicted	predict	VERB
cana-1019	194	19	class	class	NOUN
cana-1019	194	20	for	for	ADP
cana-1019	194	21	new	new	ADJ
cana-1019	194	22	instances	instance	NOUN
cana-1019	194	23	falling	fall	VERB
cana-1019	194	24	into	into	ADP
cana-1019	194	25	that	that	DET
cana-1019	194	26	leaf	leaf	NOUN
cana-1019	194	27	.	.	PUNCT
cana-1019	195	1	e.	e.	PROPN
cana-1019	195	2	random	random	PROPN
cana-1019	195	3	forest	forest	NOUN
cana-1019	195	4	:	:	PUNCT
cana-1019	195	5	a	a	DET
cana-1019	195	6	random	random	ADJ
cana-1019	195	7	forest	forest	NOUN
cana-1019	195	8	is	be	AUX
cana-1019	195	9	a	a	DET
cana-1019	195	10	group	group	NOUN
cana-1019	195	11	of	of	ADP
cana-1019	195	12	decision	decision	NOUN
cana-1019	195	13	trees	tree	NOUN
cana-1019	195	14	,	,	PUNCT
cana-1019	195	15	and	and	CCONJ
cana-1019	195	16	each	each	DET
cana-1019	195	17	tree	tree	NOUN
cana-1019	195	18	is	be	AUX
cana-1019	195	19	trained	train	VERB
cana-1019	195	20	on	on	ADP
cana-1019	195	21	a	a	DET
cana-1019	195	22	different	different	ADJ
cana-1019	195	23	set	set	NOUN
cana-1019	195	24	of	of	ADP
cana-1019	195	25	data	datum	NOUN
cana-1019	195	26	and	and	CCONJ
cana-1019	195	27	traits	trait	NOUN
cana-1019	195	28	.	.	PUNCT
cana-1019	196	1	by	by	ADP
cana-1019	196	2	averaging	average	VERB
cana-1019	196	3	results	result	NOUN
cana-1019	196	4	across	across	ADP
cana-1019	196	5	multiple	multiple	ADJ
cana-1019	196	6	trees	tree	NOUN
cana-1019	196	7	,	,	PUNCT
cana-1019	196	8	it	it	PRON
cana-1019	196	9	cuts	cut	VERB
cana-1019	196	10	down	down	ADP
cana-1019	196	11	on	on	ADP
cana-1019	196	12	overfitting	overfitte	VERB
cana-1019	196	13	and	and	CCONJ
cana-1019	196	14	boosts	boost	VERB
cana-1019	196	15	accuracy	accuracy	NOUN
cana-1019	196	16	,	,	PUNCT
cana-1019	196	17	making	make	VERB
cana-1019	196	18	it	it	PRON
cana-1019	196	19	suitable	suitable	ADJ
cana-1019	196	20	for	for	ADP
cana-1019	196	21	a	a	DET
cana-1019	196	22	wide	wide	ADJ
cana-1019	196	23	range	range	NOUN
cana-1019	196	24	of	of	ADP
cana-1019	196	25	classification	classification	NOUN
cana-1019	196	26	tasks	task	NOUN
cana-1019	196	27	.	.	PUNCT
cana-1019	197	1	random	random	ADJ
cana-1019	197	2	forest	forest	NOUN
cana-1019	197	3	model	model	NOUN
cana-1019	197	4	for	for	ADP
cana-1019	197	5	mood	mood	NOUN
cana-1019	197	6	analysis	analysis	NOUN
cana-1019	197	7	1	1	NUM
cana-1019	197	8	.	.	X
cana-1019	197	9	bootstrap	bootstrap	NOUN
cana-1019	197	10	sampling	sampling	NOUN
cana-1019	197	11	:	:	PUNCT
cana-1019	197	12	randomly	randomly	ADV
cana-1019	197	13	select	select	VERB
cana-1019	197	14	n	n	PRON
cana-1019	197	15	samples	sample	NOUN
cana-1019	197	16	with	with	ADP
cana-1019	197	17	replacement	replacement	NOUN
cana-1019	197	18	from	from	ADP
cana-1019	197	19	the	the	DET
cana-1019	197	20	original	original	ADJ
cana-1019	197	21	dataset	dataset	NOUN
cana-1019	197	22	to	to	PART
cana-1019	197	23	create	create	VERB
cana-1019	197	24	multiple	multiple	ADJ
cana-1019	197	25	bootstrap	bootstrap	NOUN
cana-1019	197	26	samples	sample	NOUN
cana-1019	197	27	(	(	PUNCT
cana-1019	197	28	also	also	ADV
cana-1019	197	29	known	know	VERB
cana-1019	197	30	as	as	ADP
cana-1019	197	31	bagging	bagging	NOUN
cana-1019	197	32	)	)	PUNCT
cana-1019	197	33	.	.	PUNCT
cana-1019	198	1	2	2	X
cana-1019	198	2	.	.	X
cana-1019	198	3	tree	tree	NOUN
cana-1019	198	4	construction	construction	NOUN
cana-1019	198	5	:	:	PUNCT
cana-1019	198	6	build	build	VERB
cana-1019	198	7	a	a	DET
cana-1019	198	8	decision	decision	NOUN
cana-1019	198	9	tree	tree	NOUN
cana-1019	198	10	for	for	ADP
cana-1019	198	11	each	each	DET
cana-1019	198	12	bootstrap	bootstrap	NOUN
cana-1019	198	13	sample	sample	NOUN
cana-1019	198	14	:	:	PUNCT
cana-1019	198	15	select	select	VERB
cana-1019	198	16	a	a	DET
cana-1019	198	17	random	random	ADJ
cana-1019	198	18	subset	subset	NOUN
cana-1019	198	19	of	of	ADP
cana-1019	198	20	features	feature	NOUN
cana-1019	198	21	at	at	ADP
cana-1019	198	22	each	each	DET
cana-1019	198	23	node	node	NOUN
cana-1019	198	24	.	.	PUNCT
cana-1019	199	1	split	split	VERB
cana-1019	199	2	nodes	node	NOUN
cana-1019	199	3	based	base	VERB
cana-1019	199	4	on	on	ADP
cana-1019	199	5	the	the	DET
cana-1019	199	6	best	good	ADJ
cana-1019	199	7	split	split	NOUN
cana-1019	199	8	according	accord	VERB
cana-1019	199	9	to	to	ADP
cana-1019	199	10	a	a	DET
cana-1019	199	11	criterion	criterion	NOUN
cana-1019	199	12	(	(	PUNCT
cana-1019	199	13	e.g.	e.g.	ADV
cana-1019	199	14	,	,	PUNCT
cana-1019	199	15	gini	gini	NOUN
cana-1019	199	16	impurity	impurity	NOUN
cana-1019	199	17	or	or	CCONJ
cana-1019	199	18	entropy	entropy	NOUN
cana-1019	199	19	)	)	PUNCT
cana-1019	199	20	.	.	PUNCT
cana-1019	200	1	3	3	X
cana-1019	200	2	.	.	X
cana-1019	200	3	ensemble	ensemble	ADJ
cana-1019	200	4	learning	learning	NOUN
cana-1019	200	5	:	:	PUNCT
cana-1019	200	6	aggregate	aggregate	ADJ
cana-1019	200	7	predictions	prediction	NOUN
cana-1019	200	8	from	from	ADP
cana-1019	200	9	all	all	DET
cana-1019	200	10	decision	decision	NOUN
cana-1019	200	11	trees	tree	NOUN
cana-1019	200	12	to	to	PART
cana-1019	200	13	make	make	VERB
cana-1019	200	14	final	final	ADJ
cana-1019	200	15	predictions	prediction	NOUN
cana-1019	200	16	:	:	PUNCT
cana-1019	200	17	for	for	ADP
cana-1019	200	18	classification	classification	NOUN
cana-1019	200	19	:	:	PUNCT
cana-1019	200	20	use	use	VERB
cana-1019	200	21	majority	majority	NOUN
cana-1019	200	22	voting	voting	NOUN
cana-1019	200	23	among	among	ADP
cana-1019	200	24	all	all	DET
cana-1019	200	25	decision	decision	NOUN
cana-1019	200	26	trees	tree	NOUN
cana-1019	200	27	.	.	PUNCT
cana-1019	201	1	for	for	ADP
cana-1019	201	2	regression	regression	NOUN
cana-1019	201	3	:	:	PUNCT
cana-1019	201	4	use	use	VERB
cana-1019	201	5	the	the	DET
cana-1019	201	6	average	average	NOUN
cana-1019	201	7	of	of	ADP
cana-1019	201	8	predictions	prediction	NOUN
cana-1019	201	9	from	from	ADP
cana-1019	201	10	all	all	DET
cana-1019	201	11	decision	decision	NOUN
cana-1019	201	12	trees	tree	NOUN
cana-1019	201	13	.	.	PUNCT
cana-1019	202	1	communications	communication	NOUN
cana-1019	202	2	on	on	ADP
cana-1019	202	3	applied	apply	VERB
cana-1019	202	4	nonlinear	nonlinear	ADJ
cana-1019	202	5	analysis	analysis	NOUN
cana-1019	202	6	issn	issn	NOUN
cana-1019	202	7	:	:	PUNCT
cana-1019	202	8	1074	1074	NUM
cana-1019	202	9	-	-	PUNCT
cana-1019	202	10	133x	133x	NUM
cana-1019	202	11	vol	vol	NOUN
cana-1019	202	12	31	31	NUM
cana-1019	202	13	no	no	NOUN
cana-1019	202	14	.	.	PUNCT
cana-1019	203	1	5s	5s	NUM
cana-1019	203	2	(	(	PUNCT
cana-1019	203	3	2024	2024	NUM
cana-1019	203	4	)	)	PUNCT
cana-1019	203	5	246	246	NUM
cana-1019	203	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1019	203	7	4	4	X
cana-1019	203	8	.	.	X
cana-1019	203	9	random	random	ADJ
cana-1019	203	10	forest	forest	NOUN
cana-1019	203	11	prediction	prediction	NOUN
cana-1019	203	12	:	:	PUNCT
cana-1019	203	13	given	give	VERB
cana-1019	203	14	a	a	DET
cana-1019	203	15	new	new	ADJ
cana-1019	203	16	sample	sample	NOUN
cana-1019	203	17	x_i	x_i	X
cana-1019	203	18	,	,	PUNCT
cana-1019	203	19	predict	predict	VERB
cana-1019	203	20	its	its	PRON
cana-1019	203	21	class	class	NOUN
cana-1019	203	22	y_i	y_i	NUM
cana-1019	203	23	by	by	ADP
cana-1019	203	24	aggregating	aggregate	VERB
cana-1019	203	25	predictions	prediction	NOUN
cana-1019	203	26	from	from	ADP
cana-1019	203	27	all	all	DET
cana-1019	203	28	decision	decision	NOUN
cana-1019	203	29	trees	tree	NOUN
cana-1019	203	30	:	:	PUNCT
cana-1019	203	31	𝑦𝑖	𝑦𝑖	NUM
cana-1019	203	32	∗	∗	NOUN
cana-1019	203	33	=	=	SYM
cana-1019	203	34	𝑚𝑜𝑑𝑒	𝑚𝑜𝑑𝑒	PROPN
cana-1019	203	35	(	(	PUNCT
cana-1019	203	36	{	{	PUNCT
cana-1019	203	37	𝑇1(𝑥𝑖	𝑇1(𝑥𝑖	PROPN
cana-1019	203	38	)	)	PUNCT
cana-1019	203	39	,	,	PUNCT
cana-1019	203	40	𝑇2(𝑥𝑖	𝑇2(𝑥𝑖	PROPN
cana-1019	203	41	)	)	PUNCT
cana-1019	203	42	,	,	PUNCT
cana-1019	203	43	…	…	PUNCT
cana-1019	203	44	,	,	PUNCT
cana-1019	203	45	𝑇𝑛(𝑥𝑖	𝑇𝑛(𝑥𝑖	PROPN
cana-1019	203	46	)	)	PUNCT
cana-1019	203	47	}	}	PUNCT
cana-1019	203	48	)	)	PUNCT
cana-1019	203	49	where	where	SCONJ
cana-1019	203	50	t_j(x_i	t_j(x_i	X
cana-1019	203	51	)	)	PUNCT
cana-1019	203	52	is	be	AUX
cana-1019	203	53	the	the	DET
cana-1019	203	54	prediction	prediction	NOUN
cana-1019	203	55	of	of	ADP
cana-1019	203	56	the	the	DET
cana-1019	203	57	j	j	PROPN
cana-1019	203	58	-	-	PUNCT
cana-1019	203	59	th	th	VERB
cana-1019	203	60	decision	decision	NOUN
cana-1019	203	61	tree	tree	NOUN
cana-1019	203	62	for	for	ADP
cana-1019	203	63	sample	sample	NOUN
cana-1019	203	64	x_i	x_i	X
cana-1019	203	65	.	.	PUNCT
cana-1019	204	1	5	5	NUM
cana-1019	204	2	.	.	X
cana-1019	204	3	out	out	ADP
cana-1019	204	4	-	-	PUNCT
cana-1019	204	5	of	of	ADP
cana-1019	204	6	-	-	PUNCT
cana-1019	204	7	bag	bag	NOUN
cana-1019	204	8	error	error	NOUN
cana-1019	204	9	:	:	PUNCT
cana-1019	204	10	evaluate	evaluate	VERB
cana-1019	204	11	model	model	NOUN
cana-1019	204	12	performance	performance	NOUN
cana-1019	204	13	using	use	VERB
cana-1019	204	14	out	out	ADV
cana-1019	204	15	-	-	PUNCT
cana-1019	204	16	of	of	ADP
cana-1019	204	17	-	-	PUNCT
cana-1019	204	18	bag	bag	NOUN
cana-1019	204	19	(	(	PUNCT
cana-1019	204	20	oob	oob	NOUN
cana-1019	204	21	)	)	PUNCT
cana-1019	204	22	samples	sample	NOUN
cana-1019	204	23	:	:	PUNCT
cana-1019	204	24	for	for	ADP
cana-1019	204	25	miss	miss	ADJ
cana-1019	204	26	classification	classification	NOUN
cana-1019	204	27	.	.	PUNCT
cana-1019	205	1	𝑂𝑂𝐵	𝑂𝑂𝐵	PROPN
cana-1019	205	2	𝐸𝑟𝑟𝑜𝑟	𝐸𝑟𝑟𝑜𝑟	PROPN
cana-1019	205	3	=	=	PROPN
cana-1019	205	4	𝑛1∑𝑖	𝑛1∑𝑖	X
cana-1019	205	5	=	=	SYM
cana-1019	205	6	1𝑛𝐼(𝑦𝑖	1𝑛𝐼(𝑦𝑖	NUM
cana-1019	205	7	=	=	SYM
cana-1019	205	8	𝑦𝑖	𝑦𝑖	PROPN
cana-1019	205	9	)	)	PUNCT
cana-1019	205	10	for	for	ADP
cana-1019	205	11	regression	regression	NOUN
cana-1019	205	12	error	error	NOUN
cana-1019	205	13	:	:	PUNCT
cana-1019	206	1	𝑂𝑂𝐵	𝑂𝑂𝐵	PROPN
cana-1019	206	2	𝐸𝑟𝑟𝑜𝑟	𝐸𝑟𝑟𝑜𝑟	PROPN
cana-1019	206	3	=	=	PROPN
cana-1019	206	4	𝑛1∑𝑖	𝑛1∑𝑖	X
cana-1019	206	5	=	=	SYM
cana-1019	206	6	1𝑛(𝑦𝑖	1𝑛(𝑦𝑖	NUM
cana-1019	206	7	−	−	PROPN
cana-1019	206	8	𝑦𝑖)2	𝑦𝑖)2	PROPN
cana-1019	206	9	f.	f.	PROPN
cana-1019	206	10	xgb	xgb	PROPN
cana-1019	207	1	classifier	classifier	PROPN
cana-1019	207	2	:	:	PUNCT
cana-1019	207	3	the	the	DET
cana-1019	207	4	xgboost	xgboost	PROPN
cana-1019	207	5	classifier	classifier	PROPN
cana-1019	207	6	is	be	AUX
cana-1019	207	7	the	the	DET
cana-1019	207	8	best	good	ADJ
cana-1019	207	9	way	way	NOUN
cana-1019	207	10	to	to	PART
cana-1019	207	11	use	use	VERB
cana-1019	207	12	gradient	gradient	NOUN
cana-1019	207	13	boosting	boost	VERB
cana-1019	207	14	to	to	PART
cana-1019	207	15	improve	improve	VERB
cana-1019	207	16	model	model	NOUN
cana-1019	207	17	performance	performance	NOUN
cana-1019	207	18	through	through	ADP
cana-1019	207	19	sequential	sequential	ADJ
cana-1019	207	20	ensemble	ensemble	ADJ
cana-1019	207	21	learning	learning	NOUN
cana-1019	207	22	.	.	PUNCT
cana-1019	208	1	it	it	PRON
cana-1019	208	2	makes	make	VERB
cana-1019	208	3	predictions	prediction	NOUN
cana-1019	208	4	more	more	ADV
cana-1019	208	5	accurate	accurate	ADJ
cana-1019	208	6	by	by	ADP
cana-1019	208	7	reducing	reduce	VERB
cana-1019	208	8	the	the	DET
cana-1019	208	9	number	number	NOUN
cana-1019	208	10	of	of	ADP
cana-1019	208	11	loss	loss	NOUN
cana-1019	208	12	and	and	CCONJ
cana-1019	208	13	regularization	regularization	NOUN
cana-1019	208	14	terms	term	NOUN
cana-1019	208	15	.	.	PUNCT
cana-1019	209	1	in	in	ADP
cana-1019	209	2	competitions	competition	NOUN
cana-1019	209	3	,	,	PUNCT
cana-1019	209	4	it	it	PRON
cana-1019	209	5	often	often	ADV
cana-1019	209	6	gets	get	VERB
cana-1019	209	7	the	the	DET
cana-1019	209	8	best	good	ADJ
cana-1019	209	9	results	result	NOUN
cana-1019	209	10	possible	possible	ADJ
cana-1019	209	11	.	.	PUNCT
cana-1019	210	1	xgboost	xgboost	PROPN
cana-1019	210	2	classifier	classifier	NOUN
cana-1019	210	3	:	:	PUNCT
cana-1019	210	4	algorithm	algorithm	PROPN
cana-1019	210	5	1	1	NUM
cana-1019	210	6	.	.	PUNCT
cana-1019	210	7	initialize	initialize	NOUN
cana-1019	210	8	model	model	NOUN
cana-1019	210	9	:	:	PUNCT
cana-1019	210	10	start	start	VERB
cana-1019	210	11	with	with	ADP
cana-1019	210	12	initial	initial	ADJ
cana-1019	210	13	predictions	prediction	NOUN
cana-1019	210	14	�	�	NOUN
cana-1019	210	15	̂	̂	NOUN
cana-1019	210	16	�	�	NOUN
cana-1019	210	17	_𝑖	_𝑖	NOUN
cana-1019	210	18	=	=	PUNCT
cana-1019	210	19	0	0	NUM
cana-1019	210	20	for	for	ADP
cana-1019	210	21	all	all	DET
cana-1019	210	22	samples	sample	NOUN
cana-1019	210	23	.	.	PUNCT
cana-1019	211	1	2	2	X
cana-1019	211	2	.	.	X
cana-1019	211	3	compute	compute	NOUN
cana-1019	211	4	gradient	gradient	NOUN
cana-1019	211	5	and	and	CCONJ
cana-1019	211	6	hessian	hessian	NOUN
cana-1019	211	7	:	:	PUNCT
cana-1019	211	8	compute	compute	VERB
cana-1019	211	9	the	the	DET
cana-1019	211	10	gradient	gradient	NOUN
cana-1019	211	11	𝑔_𝑖	𝑔_𝑖	PUNCT
cana-1019	211	12	and	and	CCONJ
cana-1019	211	13	the	the	DET
cana-1019	211	14	second	second	ADJ
cana-1019	211	15	derivative	derivative	ADJ
cana-1019	211	16	(	(	PUNCT
cana-1019	211	17	hessian	hessian	NOUN
cana-1019	211	18	)	)	PUNCT
cana-1019	212	1	ℎ_𝑖	ℎ_𝑖	ADP
cana-1019	212	2	of	of	ADP
cana-1019	212	3	the	the	DET
cana-1019	212	4	loss	loss	NOUN
cana-1019	212	5	function	function	NOUN
cana-1019	212	6	with	with	ADP
cana-1019	212	7	respect	respect	NOUN
cana-1019	212	8	to	to	ADP
cana-1019	212	9	the	the	DET
cana-1019	212	10	predicted	predict	VERB
cana-1019	212	11	values	value	NOUN
cana-1019	212	12	�	�	NOUN
cana-1019	212	13	̂	̂	NOUN
cana-1019	212	14	�	�	NOUN
cana-1019	212	15	_𝑖	_𝑖	NOUN
cana-1019	212	16	:	:	PUNCT
cana-1019	212	17	𝑔𝑖	𝑔𝑖	NOUN
cana-1019	212	18	=	=	SYM
cana-1019	212	19	𝜕𝐿(𝑦𝑖	𝜕𝐿(𝑦𝑖	ADJ
cana-1019	212	20	,	,	PUNCT
cana-1019	212	21	�	�	NOUN
cana-1019	212	22	̂	̂	VERB
cana-1019	212	23	�	�	NOUN
cana-1019	212	24	𝑖	𝑖	SYM
cana-1019	212	25	)	)	PUNCT
cana-1019	212	26	𝜕	𝜕	PROPN
cana-1019	212	27	�	�	PROPN
cana-1019	212	28	̂	̂	NOUN
cana-1019	212	29	�	�	NOUN
cana-1019	212	30	𝑖	𝑖	SYM
cana-1019	212	31	ℎ𝑖	ℎ𝑖	NOUN
cana-1019	212	32	=	=	PUNCT
cana-1019	212	33	𝜕2𝐿(𝑦𝑖	𝜕2𝐿(𝑦𝑖	PROPN
cana-1019	212	34	,	,	PUNCT
cana-1019	212	35	�	�	PROPN
cana-1019	212	36	̂	̂	VERB
cana-1019	212	37	�	�	NOUN
cana-1019	212	38	𝑖	𝑖	SYM
cana-1019	212	39	)	)	PUNCT
cana-1019	212	40	𝜕(	𝜕(	PROPN
cana-1019	212	41	�	�	PROPN
cana-1019	212	42	̂	̂	SYM
cana-1019	212	43	�	�	NOUN
cana-1019	212	44	𝑖)2	𝑖)2	NOUN
cana-1019	212	45	3	3	NUM
cana-1019	212	46	.	.	PUNCT
cana-1019	212	47	build	build	VERB
cana-1019	212	48	a	a	DET
cana-1019	212	49	decision	decision	NOUN
cana-1019	212	50	tree	tree	NOUN
cana-1019	212	51	:	:	PUNCT
cana-1019	212	52	fit	fit	VERB
cana-1019	212	53	a	a	DET
cana-1019	212	54	regression	regression	NOUN
cana-1019	212	55	tree	tree	NOUN
cana-1019	212	56	to	to	ADP
cana-1019	212	57	the	the	DET
cana-1019	212	58	gradient	gradient	NOUN
cana-1019	212	59	𝑔_𝑖	𝑔_𝑖	PUNCT
cana-1019	212	60	as	as	ADP
cana-1019	212	61	targets	target	NOUN
cana-1019	212	62	:	:	PUNCT
cana-1019	212	63	split	split	VERB
cana-1019	212	64	nodes	node	NOUN
cana-1019	212	65	to	to	PART
cana-1019	212	66	minimize	minimize	VERB
cana-1019	212	67	the	the	DET
cana-1019	212	68	loss	loss	NOUN
cana-1019	212	69	function	function	NOUN
cana-1019	212	70	within	within	ADP
cana-1019	212	71	each	each	DET
cana-1019	212	72	leaf	leaf	NOUN
cana-1019	212	73	.	.	PUNCT
cana-1019	213	1	4	4	X
cana-1019	213	2	.	.	X
cana-1019	213	3	update	update	NOUN
cana-1019	213	4	predictions	prediction	NOUN
cana-1019	213	5	:	:	PUNCT
cana-1019	213	6	update	update	NOUN
cana-1019	213	7	predictions	prediction	NOUN
cana-1019	213	8	�	�	NOUN
cana-1019	213	9	̂	̂	NOUN
cana-1019	213	10	�	�	NOUN
cana-1019	213	11	_𝑖	_𝑖	NOUN
cana-1019	213	12	using	use	VERB
cana-1019	213	13	the	the	DET
cana-1019	213	14	fitted	fit	VERB
cana-1019	213	15	tree	tree	NOUN
cana-1019	213	16	:	:	PUNCT
cana-1019	213	17	�	�	PROPN
cana-1019	213	18	̂	̂	VERB
cana-1019	213	19	�	�	NOUN
cana-1019	213	20	𝑖	𝑖	SYM
cana-1019	213	21	𝑡	𝑡	PROPN
cana-1019	213	22	=	=	SYM
cana-1019	213	23	�	�	PROPN
cana-1019	213	24	̂	̂	NOUN
cana-1019	213	25	�	�	PROPN
cana-1019	213	26	𝑖	𝑖	SYM
cana-1019	213	27	𝑡−1	𝑡−1	PROPN
cana-1019	213	28	+	+	CCONJ
cana-1019	213	29	𝜂	𝜂	PROPN
cana-1019	213	30	⋅	⋅	PROPN
cana-1019	213	31	𝑡𝑟𝑒𝑒𝑡(𝑥𝑖	𝑡𝑟𝑒𝑒𝑡(𝑥𝑖	VERB
cana-1019	213	32	)	)	PUNCT
cana-1019	213	33	communications	communication	NOUN
cana-1019	213	34	on	on	ADP
cana-1019	213	35	applied	apply	VERB
cana-1019	213	36	nonlinear	nonlinear	ADJ
cana-1019	213	37	analysis	analysis	NOUN
cana-1019	213	38	issn	issn	NOUN
cana-1019	213	39	:	:	PUNCT
cana-1019	213	40	1074	1074	NUM
cana-1019	213	41	-	-	PUNCT
cana-1019	213	42	133x	133x	NUM
cana-1019	213	43	vol	vol	NOUN
cana-1019	213	44	31	31	NUM
cana-1019	213	45	no	no	NOUN
cana-1019	213	46	.	.	PUNCT
cana-1019	214	1	5s	5s	NUM
cana-1019	214	2	(	(	PUNCT
cana-1019	214	3	2024	2024	NUM
cana-1019	214	4	)	)	PUNCT
cana-1019	214	5	247	247	NUM
cana-1019	214	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1019	214	7	where	where	SCONJ
cana-1019	214	8	𝜂	𝜂	NOUN
cana-1019	214	9	is	be	AUX
cana-1019	214	10	the	the	DET
cana-1019	214	11	learning	learning	NOUN
cana-1019	214	12	rate	rate	NOUN
cana-1019	214	13	and	and	CCONJ
cana-1019	214	14	𝑡𝑟𝑒𝑒_𝑡(𝑥_𝑖	𝑡𝑟𝑒𝑒_𝑡(𝑥_𝑖	NUM
cana-1019	214	15	)	)	PUNCT
cana-1019	214	16	is	be	AUX
cana-1019	214	17	the	the	DET
cana-1019	214	18	prediction	prediction	NOUN
cana-1019	214	19	of	of	ADP
cana-1019	214	20	the	the	DET
cana-1019	214	21	𝑡-th	𝑡-th	PROPN
cana-1019	214	22	tree	tree	NOUN
cana-1019	214	23	for	for	ADP
cana-1019	214	24	sample	sample	NOUN
cana-1019	214	25	𝑥_𝑖.	𝑥_𝑖.	PROPN
cana-1019	214	26	5	5	NUM
cana-1019	214	27	.	.	PUNCT
cana-1019	215	1	regularization	regularization	NOUN
cana-1019	215	2	:	:	PUNCT
cana-1019	215	3	add	add	VERB
cana-1019	215	4	regularization	regularization	NOUN
cana-1019	215	5	terms	term	NOUN
cana-1019	215	6	to	to	PART
cana-1019	215	7	prevent	prevent	VERB
cana-1019	215	8	overfitting	overfitting	NOUN
cana-1019	215	9	:	:	PUNCT
cana-1019	215	10	penalize	penalize	VERB
cana-1019	215	11	large	large	ADJ
cana-1019	215	12	trees	tree	NOUN
cana-1019	215	13	using	use	VERB
cana-1019	215	14	regularization	regularization	NOUN
cana-1019	215	15	parameters	parameter	NOUN
cana-1019	215	16	like	like	ADP
cana-1019	215	17	max_depth	max_depth	NOUN
cana-1019	215	18	,	,	PUNCT
cana-1019	215	19	min_child_weight	min_child_weight	VERB
cana-1019	215	20	,	,	PUNCT
cana-1019	215	21	and	and	CCONJ
cana-1019	215	22	gamma	gamma	PROPN
cana-1019	215	23	.	.	PUNCT
cana-1019	216	1	6	6	X
cana-1019	216	2	.	.	X
cana-1019	216	3	repeat	repeat	NOUN
cana-1019	216	4	:	:	PUNCT
cana-1019	216	5	iterate	iterate	VERB
cana-1019	216	6	steps	step	NOUN
cana-1019	216	7	2	2	NUM
cana-1019	216	8	-	-	SYM
cana-1019	216	9	5	5	NUM
cana-1019	216	10	until	until	SCONJ
cana-1019	216	11	a	a	DET
cana-1019	216	12	predefined	predefine	VERB
cana-1019	216	13	number	number	NOUN
cana-1019	216	14	of	of	ADP
cana-1019	216	15	trees	tree	NOUN
cana-1019	216	16	(	(	PUNCT
cana-1019	216	17	iterations	iteration	NOUN
cana-1019	216	18	)	)	PUNCT
cana-1019	216	19	is	be	AUX
cana-1019	216	20	reached	reach	VERB
cana-1019	216	21	or	or	CCONJ
cana-1019	216	22	the	the	DET
cana-1019	216	23	loss	loss	NOUN
cana-1019	216	24	function	function	NOUN
cana-1019	216	25	converges	converge	VERB
cana-1019	216	26	.	.	PUNCT
cana-1019	217	1	g.	g.	PROPN
cana-1019	217	2	svm	svm	PROPN
cana-1019	217	3	linear	linear	PROPN
cana-1019	217	4	:	:	PUNCT
cana-1019	217	5	it	it	PRON
cana-1019	217	6	finds	find	VERB
cana-1019	217	7	the	the	DET
cana-1019	217	8	best	good	ADJ
cana-1019	217	9	hyperplane	hyperplane	NOUN
cana-1019	217	10	that	that	PRON
cana-1019	217	11	divides	divide	VERB
cana-1019	217	12	classes	class	NOUN
cana-1019	217	13	in	in	ADP
cana-1019	217	14	a	a	DET
cana-1019	217	15	collection	collection	NOUN
cana-1019	217	16	that	that	PRON
cana-1019	217	17	can	can	AUX
cana-1019	217	18	be	be	AUX
cana-1019	217	19	separated	separate	VERB
cana-1019	217	20	linearly	linearly	ADV
cana-1019	217	21	.	.	PUNCT
cana-1019	218	1	it	it	PRON
cana-1019	218	2	makes	make	VERB
cana-1019	218	3	the	the	DET
cana-1019	218	4	difference	difference	NOUN
cana-1019	218	5	between	between	ADP
cana-1019	218	6	classes	class	NOUN
cana-1019	218	7	as	as	ADV
cana-1019	218	8	big	big	ADJ
cana-1019	218	9	as	as	ADP
cana-1019	218	10	possible	possible	ADJ
cana-1019	218	11	,	,	PUNCT
cana-1019	218	12	which	which	PRON
cana-1019	218	13	makes	make	VERB
cana-1019	218	14	it	it	PRON
cana-1019	218	15	good	good	ADJ
cana-1019	218	16	for	for	ADP
cana-1019	218	17	binary	binary	ADJ
cana-1019	218	18	classification	classification	NOUN
cana-1019	218	19	jobs	job	NOUN
cana-1019	218	20	with	with	ADP
cana-1019	218	21	lots	lot	NOUN
cana-1019	218	22	of	of	ADP
cana-1019	218	23	variables	variable	NOUN
cana-1019	218	24	.	.	PUNCT
cana-1019	219	1	svm	svm	PROPN
cana-1019	219	2	linear	linear	PROPN
cana-1019	219	3	model	model	PROPN
cana-1019	219	4	1	1	NUM
cana-1019	219	5	.	.	PUNCT
cana-1019	219	6	objective	objective	ADJ
cana-1019	219	7	function	function	NOUN
cana-1019	219	8	:	:	PUNCT
cana-1019	219	9	minimize	minimize	VERB
cana-1019	219	10	the	the	DET
cana-1019	219	11	objective	objective	ADJ
cana-1019	219	12	function	function	NOUN
cana-1019	219	13	to	to	PART
cana-1019	219	14	find	find	VERB
cana-1019	219	15	the	the	DET
cana-1019	219	16	optimal	optimal	ADJ
cana-1019	219	17	hyperplane	hyperplane	NOUN
cana-1019	219	18	:	:	PUNCT
cana-1019	219	19	min	min	NOUN
cana-1019	219	20	1	1	NUM
cana-1019	219	21	2	2	NUM
cana-1019	219	22	||𝑤||	||𝑤||	X
cana-1019	219	23	2	2	NUM
cana-1019	219	24	subject	subject	NOUN
cana-1019	219	25	to	to	ADP
cana-1019	219	26	:	:	PUNCT
cana-1019	219	27	𝑦𝑖(𝑤𝑇𝑥𝑖	𝑦𝑖(𝑤𝑇𝑥𝑖	NOUN
cana-1019	219	28	+	+	CCONJ
cana-1019	219	29	𝑏	𝑏	NOUN
cana-1019	219	30	)	)	PUNCT
cana-1019	219	31	≥	≥	NOUN
cana-1019	219	32	1	1	NUM
cana-1019	219	33	𝑓𝑜𝑟	𝑓𝑜𝑟	NOUN
cana-1019	219	34	𝑎𝑙𝑙	𝑎𝑙𝑙	X
cana-1019	219	35	𝑖	𝑖	SYM
cana-1019	219	36	=	=	SYM
cana-1019	219	37	1	1	NUM
cana-1019	219	38	,	,	PUNCT
cana-1019	219	39	…	…	PUNCT
cana-1019	219	40	,	,	PUNCT
cana-1019	219	41	𝑛	𝑛	PRON
cana-1019	219	42	2	2	NUM
cana-1019	219	43	.	.	PUNCT
cana-1019	219	44	lagrangian	lagrangian	ADJ
cana-1019	219	45	formulation	formulation	NOUN
cana-1019	219	46	:	:	PUNCT
cana-1019	219	47	formulate	formulate	VERB
cana-1019	219	48	the	the	DET
cana-1019	219	49	lagrangian	lagrangian	NOUN
cana-1019	219	50	with	with	ADP
cana-1019	219	51	lagrange	lagrange	PROPN
cana-1019	219	52	multipliers	multiplier	NOUN
cana-1019	219	53	𝛼_𝑖	𝛼_𝑖	PROPN
cana-1019	219	54	≥	≥	NOUN
cana-1019	219	55	0	0	NUM
cana-1019	219	56	:	:	PUNCT
cana-1019	219	57	𝐿(𝑤	𝐿(𝑤	NUM
cana-1019	219	58	,	,	PUNCT
cana-1019	219	59	𝑏	𝑏	NOUN
cana-1019	219	60	,	,	PUNCT
cana-1019	219	61	𝛼	𝛼	NOUN
cana-1019	219	62	)	)	PUNCT
cana-1019	219	63	=	=	SYM
cana-1019	219	64	1	1	NUM
cana-1019	219	65	2	2	NUM
cana-1019	219	66	||𝑤||	||𝑤||	NOUN
cana-1019	219	67	2	2	NUM
cana-1019	219	68	−	−	PROPN
cana-1019	219	69	𝛴	𝛴	PROPN
cana-1019	219	70	{	{	PUNCT
cana-1019	219	71	𝑖=1	𝑖=1	PROPN
cana-1019	219	72	}	}	PUNCT
cana-1019	219	73	𝑖	𝑖	NOUN
cana-1019	219	74	{	{	PUNCT
cana-1019	219	75	𝑛}𝛼	𝑛}𝛼	PROPN
cana-1019	219	76	[	[	PUNCT
cana-1019	219	77	𝑦𝑖(𝑤𝑇𝑥𝑖+	𝑦𝑖(𝑤𝑇𝑥𝑖+	PROPN
cana-1019	219	78	𝑏)−	𝑏)−	PROPN
cana-1019	219	79	1	1	NUM
cana-1019	219	80	]	]	SYM
cana-1019	219	81	3	3	X
cana-1019	219	82	.	.	PUNCT
cana-1019	219	83	dual	dual	ADJ
cana-1019	219	84	problem	problem	NOUN
cana-1019	219	85	:	:	PUNCT
cana-1019	219	86	maximize	maximize	VERB
cana-1019	219	87	the	the	DET
cana-1019	219	88	dual	dual	ADJ
cana-1019	219	89	function	function	NOUN
cana-1019	219	90	to	to	PART
cana-1019	219	91	find	find	VERB
cana-1019	219	92	𝛼	𝛼	PRON
cana-1019	219	93	that	that	PRON
cana-1019	219	94	maximizes	maximize	VERB
cana-1019	219	95	the	the	DET
cana-1019	219	96	margin	margin	NOUN
cana-1019	219	97	:	:	PUNCT
cana-1019	219	98	max	max	PROPN
cana-1019	219	99	𝛴	𝛴	PROPN
cana-1019	219	100	{	{	PUNCT
cana-1019	219	101	𝑖=1	𝑖=1	PROPN
cana-1019	219	102	}	}	PUNCT
cana-1019	219	103	𝑖	𝑖	NOUN
cana-1019	219	104	{	{	PUNCT
cana-1019	219	105	𝑛}𝛼	𝑛}𝛼	VERB
cana-1019	219	106	−	−	PROPN
cana-1019	219	107	1	1	NUM
cana-1019	219	108	2	2	NUM
cana-1019	219	109	𝛴	𝛴	PROPN
cana-1019	219	110	{	{	PUNCT
cana-1019	219	111	𝑖,𝑗=1	𝑖,𝑗=1	ADV
cana-1019	219	112	}	}	PUNCT
cana-1019	219	113	𝑖	𝑖	NOUN
cana-1019	219	114	{	{	PUNCT
cana-1019	219	115	𝑛}𝛼	𝑛}𝛼	VERB
cana-1019	219	116	𝛼𝑗𝑦𝑖𝑦𝑗𝑥𝑖	𝛼𝑗𝑦𝑖𝑦𝑗𝑥𝑖	PROPN
cana-1019	219	117	𝑇𝑥𝑗	𝑇𝑥𝑗	PROPN
cana-1019	219	118	subject	subject	NOUN
cana-1019	219	119	to	to	ADP
cana-1019	219	120	:	:	PUNCT
cana-1019	219	121	𝛴	𝛴	PROPN
cana-1019	219	122	{	{	PUNCT
cana-1019	219	123	𝑖=1	𝑖=1	PROPN
cana-1019	219	124	}	}	PUNCT
cana-1019	219	125	𝑖	𝑖	NOUN
cana-1019	219	126	{	{	PUNCT
cana-1019	219	127	𝑛}𝛼	𝑛}𝛼	NOUN
cana-1019	219	128	𝑦𝑖	𝑦𝑖	X
cana-1019	219	129	=	=	SYM
cana-1019	219	130	0	0	NUM
cana-1019	219	131	𝛼𝑖	𝛼𝑖	NOUN
cana-1019	219	132	≥	≥	NOUN
cana-1019	219	133	0	0	NUM
cana-1019	220	1	𝑓𝑜𝑟	𝑓𝑜𝑟	ADV
cana-1019	220	2	𝑎𝑙𝑙	𝑎𝑙𝑙	X
cana-1019	220	3	𝑖	𝑖	SYM
cana-1019	220	4	=	=	SYM
cana-1019	220	5	1	1	NUM
cana-1019	220	6	,	,	PUNCT
cana-1019	220	7	…	…	PUNCT
cana-1019	220	8	,	,	PUNCT
cana-1019	220	9	𝑛	𝑛	PRON
cana-1019	220	10	4	4	NUM
cana-1019	220	11	.	.	PUNCT
cana-1019	220	12	calculate	calculate	NOUN
cana-1019	220	13	𝐰	𝐰	PROPN
cana-1019	220	14	and	and	CCONJ
cana-1019	220	15	𝑏	𝑏	NOUN
cana-1019	220	16	:	:	PUNCT
cana-1019	220	17	compute	compute	NOUN
cana-1019	220	18	𝐰	𝐰	NOUN
cana-1019	220	19	and	and	CCONJ
cana-1019	220	20	𝑏	𝑏	NOUN
cana-1019	220	21	using	use	VERB
cana-1019	220	22	the	the	DET
cana-1019	220	23	optimal	optimal	ADJ
cana-1019	220	24	𝛼	𝛼	NOUN
cana-1019	220	25	:	:	PUNCT
cana-1019	220	26	communications	communication	NOUN
cana-1019	220	27	on	on	ADP
cana-1019	220	28	applied	apply	VERB
cana-1019	220	29	nonlinear	nonlinear	ADJ
cana-1019	220	30	analysis	analysis	NOUN
cana-1019	220	31	issn	issn	NOUN
cana-1019	220	32	:	:	PUNCT
cana-1019	220	33	1074	1074	NUM
cana-1019	220	34	-	-	PUNCT
cana-1019	220	35	133x	133x	NUM
cana-1019	220	36	vol	vol	NOUN
cana-1019	220	37	31	31	NUM
cana-1019	220	38	no	no	NOUN
cana-1019	220	39	.	.	PUNCT
cana-1019	221	1	5s	5s	NUM
cana-1019	221	2	(	(	PUNCT
cana-1019	221	3	2024	2024	NUM
cana-1019	221	4	)	)	PUNCT
cana-1019	221	5	248	248	NUM
cana-1019	221	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1019	221	7	𝑤	𝑤	SYM
cana-1019	221	8	=	=	SYM
cana-1019	221	9	𝛴	𝛴	PROPN
cana-1019	221	10	{	{	PUNCT
cana-1019	221	11	𝑖=1	𝑖=1	PROPN
cana-1019	221	12	}	}	PUNCT
cana-1019	221	13	𝑖	𝑖	NOUN
cana-1019	221	14	{	{	PUNCT
cana-1019	221	15	𝑛}𝛼	𝑛}𝛼	X
cana-1019	221	16	𝑦𝑖𝑥𝑖	𝑦𝑖𝑥𝑖	VERB
cana-1019	221	17	𝑏	𝑏	NOUN
cana-1019	221	18	=	=	SYM
cana-1019	221	19	𝑦𝑖	𝑦𝑖	PROPN
cana-1019	221	20	−	−	PROPN
cana-1019	221	21	𝑤𝑇𝑥𝑖𝑓𝑜𝑟	𝑤𝑇𝑥𝑖𝑓𝑜𝑟	PROPN
cana-1019	221	22	𝑎𝑛𝑦	𝑎𝑛𝑦	VERB
cana-1019	221	23	𝑖	𝑖	SYM
cana-1019	221	24	𝑠𝑢𝑐ℎ	𝑠𝑢𝑐ℎ	NOUN
cana-1019	221	25	𝑡ℎ𝑎𝑡	𝑡ℎ𝑎𝑡	ADJ
cana-1019	221	26	0	0	PUNCT
cana-1019	221	27	<	<	X
cana-1019	221	28	𝛼𝑖	𝛼𝑖	ADP
cana-1019	221	29	<	<	X
cana-1019	221	30	𝐶	𝐶	PROPN
cana-1019	221	31	h.	h.	PROPN
cana-1019	221	32	knn	knn	PROPN
cana-1019	221	33	:	:	PUNCT
cana-1019	221	34	in	in	ADP
cana-1019	221	35	the	the	DET
cana-1019	221	36	feature	feature	NOUN
cana-1019	221	37	space	space	NOUN
cana-1019	221	38	,	,	PUNCT
cana-1019	221	39	the	the	DET
cana-1019	221	40	k	k	NOUN
cana-1019	221	41	-	-	PUNCT
cana-1019	221	42	nearest	near	ADJ
cana-1019	221	43	neighbors	neighbor	NOUN
cana-1019	221	44	(	(	PUNCT
cana-1019	221	45	knn	knn	PROPN
cana-1019	221	46	)	)	PUNCT
cana-1019	221	47	method	method	NOUN
cana-1019	221	48	sorts	sort	VERB
cana-1019	221	49	a	a	DET
cana-1019	221	50	sample	sample	NOUN
cana-1019	221	51	into	into	ADP
cana-1019	221	52	a	a	DET
cana-1019	221	53	category	category	NOUN
cana-1019	221	54	based	base	VERB
cana-1019	221	55	on	on	ADP
cana-1019	221	56	the	the	DET
cana-1019	221	57	category	category	NOUN
cana-1019	221	58	that	that	PRON
cana-1019	221	59	it	it	PRON
cana-1019	221	60	’s	’	VERB
cana-1019	221	61	k	k	PROPN
cana-1019	221	62	nearest	near	ADJ
cana-1019	221	63	neighbors	neighbor	NOUN
cana-1019	221	64	belong	belong	VERB
cana-1019	221	65	to	to	ADP
cana-1019	221	66	.	.	PUNCT
cana-1019	222	1	it	it	PRON
cana-1019	222	2	works	work	VERB
cana-1019	222	3	well	well	ADV
cana-1019	222	4	and	and	CCONJ
cana-1019	222	5	is	be	AUX
cana-1019	222	6	easy	easy	ADJ
cana-1019	222	7	to	to	PART
cana-1019	222	8	use	use	VERB
cana-1019	222	9	for	for	ADP
cana-1019	222	10	small	small	ADJ
cana-1019	222	11	to	to	PART
cana-1019	222	12	medium	medium	ADJ
cana-1019	222	13	-	-	PUNCT
cana-1019	222	14	sized	sized	ADJ
cana-1019	222	15	datasets	dataset	NOUN
cana-1019	222	16	,	,	PUNCT
cana-1019	222	17	but	but	CCONJ
cana-1019	222	18	because	because	SCONJ
cana-1019	222	19	it	it	PRON
cana-1019	222	20	learns	learn	VERB
cana-1019	222	21	slowly	slowly	ADV
cana-1019	222	22	,	,	PUNCT
cana-1019	222	23	it	it	PRON
cana-1019	222	24	can	can	AUX
cana-1019	222	25	be	be	AUX
cana-1019	222	26	hard	hard	ADJ
cana-1019	222	27	to	to	PART
cana-1019	222	28	run	run	VERB
cana-1019	222	29	on	on	ADP
cana-1019	222	30	big	big	ADJ
cana-1019	222	31	datasets	dataset	NOUN
cana-1019	222	32	.	.	PUNCT
cana-1019	223	1	the	the	DET
cana-1019	223	2	result	result	NOUN
cana-1019	223	3	snapshot	snapshot	NOUN
cana-1019	223	4	of	of	ADP
cana-1019	223	5	mood	mood	NOUN
cana-1019	223	6	classification	classification	NOUN
cana-1019	223	7	using	use	VERB
cana-1019	223	8	knn	knn	PROPN
cana-1019	223	9	illustrate	illustrate	VERB
cana-1019	223	10	in	in	ADP
cana-1019	223	11	figure	figure	NOUN
cana-1019	223	12	4	4	NUM
cana-1019	223	13	.	.	PUNCT
cana-1019	223	14	figure	figure	VERB
cana-1019	223	15	4	4	NUM
cana-1019	223	16	:	:	PUNCT
cana-1019	223	17	music	music	NOUN
cana-1019	223	18	recommendation	recommendation	NOUN
cana-1019	223	19	based	base	VERB
cana-1019	223	20	on	on	ADP
cana-1019	223	21	mood	mood	NOUN
cana-1019	223	22	using	use	VERB
cana-1019	223	23	knn	knn	PROPN
cana-1019	223	24	k	k	PROPN
cana-1019	223	25	-	-	PUNCT
cana-1019	223	26	nearest	near	ADJ
cana-1019	223	27	neighbors	neighbor	NOUN
cana-1019	223	28	(	(	PUNCT
cana-1019	223	29	knn	knn	PROPN
cana-1019	223	30	)	)	PUNCT
cana-1019	223	31	step	step	VERB
cana-1019	223	32	wise	wise	ADJ
cana-1019	223	33	model	model	NOUN
cana-1019	223	34	1	1	NUM
cana-1019	223	35	.	.	PUNCT
cana-1019	223	36	training	training	NOUN
cana-1019	223	37	phase	phase	NOUN
cana-1019	223	38	:	:	PUNCT
cana-1019	223	39	store	store	VERB
cana-1019	223	40	all	all	DET
cana-1019	223	41	training	training	NOUN
cana-1019	223	42	samples	sample	NOUN
cana-1019	223	43	{	{	PUNCT
cana-1019	223	44	𝑥_𝑖	𝑥_𝑖	NUM
cana-1019	223	45	,	,	PUNCT
cana-1019	223	46	𝑦_𝑖	𝑦_𝑖	PRON
cana-1019	223	47	}	}	PUNCT
cana-1019	223	48	𝑓𝑜𝑟	𝑓𝑜𝑟	ADV
cana-1019	223	49	𝑖	𝑖	NOUN
cana-1019	223	50	=	=	NOUN
cana-1019	223	51	1	1	NUM
cana-1019	223	52	,	,	PUNCT
cana-1019	223	53	.	.	PUNCT
cana-1019	223	54	.	.	PUNCT
cana-1019	223	55	.	.	PUNCT
cana-1019	224	1	,	,	PUNCT
cana-1019	224	2	𝑛.	𝑛.	NOUN
cana-1019	224	3	−	−	NOUN
cana-1019	224	4	𝑥_𝑖	𝑥_𝑖	SYM
cana-1019	224	5	∈	∈	NOUN
cana-1019	224	6	ℝ^𝑑	ℝ^𝑑	NOUN
cana-1019	224	7	represents	represent	VERB
cana-1019	224	8	the	the	DET
cana-1019	224	9	feature	feature	NOUN
cana-1019	224	10	vector	vector	NOUN
cana-1019	224	11	of	of	ADP
cana-1019	224	12	the	the	DET
cana-1019	224	13	𝑖-th	𝑖-th	PROPN
cana-1019	224	14	sample	sample	NOUN
cana-1019	224	15	.	.	PUNCT
cana-1019	225	1	−	−	NOUN
cana-1019	225	2	𝑦_𝑖	𝑦_𝑖	PUNCT
cana-1019	225	3	∈	∈	PROPN
cana-1019	225	4	{	{	PUNCT
cana-1019	225	5	1	1	NUM
cana-1019	225	6	,	,	PUNCT
cana-1019	225	7	.	.	PUNCT
cana-1019	225	8	.	.	PUNCT
cana-1019	225	9	.	.	PUNCT
cana-1019	226	1	,	,	PUNCT
cana-1019	226	2	𝐾	𝐾	NOUN
cana-1019	226	3	}	}	PUNCT
cana-1019	226	4	denotes	denote	VERB
cana-1019	226	5	the	the	DET
cana-1019	226	6	class	class	NOUN
cana-1019	226	7	label	label	NOUN
cana-1019	226	8	of	of	ADP
cana-1019	226	9	the	the	DET
cana-1019	226	10	𝑖-th	𝑖-th	PROPN
cana-1019	226	11	sample	sample	NOUN
cana-1019	226	12	.	.	PUNCT
cana-1019	227	1	2	2	X
cana-1019	227	2	.	.	NOUN
cana-1019	227	3	prediction	prediction	NOUN
cana-1019	227	4	phase	phase	NOUN
cana-1019	227	5	:	:	PUNCT
cana-1019	227	6	given	give	VERB
cana-1019	227	7	a	a	DET
cana-1019	227	8	new	new	ADJ
cana-1019	227	9	sample	sample	NOUN
cana-1019	227	10	𝐱_test	𝐱_test	ADV
cana-1019	227	11	,	,	PUNCT
cana-1019	227	12	find	find	VERB
cana-1019	227	13	its	its	PRON
cana-1019	227	14	𝐾	𝐾	PROPN
cana-1019	227	15	nearest	near	ADJ
cana-1019	227	16	neighbors	neighbor	NOUN
cana-1019	227	17	in	in	ADP
cana-1019	227	18	the	the	DET
cana-1019	227	19	training	training	NOUN
cana-1019	227	20	set	set	NOUN
cana-1019	227	21	based	base	VERB
cana-1019	227	22	on	on	ADP
cana-1019	227	23	a	a	DET
cana-1019	227	24	distance	distance	NOUN
cana-1019	227	25	metric	metric	NOUN
cana-1019	227	26	(	(	PUNCT
cana-1019	227	27	e.g.	e.g.	ADV
cana-1019	227	28	,	,	PUNCT
cana-1019	227	29	euclidean	euclidean	ADJ
cana-1019	227	30	distance	distance	NOUN
cana-1019	227	31	):	):	PUNCT
cana-1019	227	32	𝐷(𝑥𝑖	𝐷(𝑥𝑖	VERB
cana-1019	227	33	,	,	PUNCT
cana-1019	227	34	𝑥𝑡𝑒𝑠𝑡	𝑥𝑡𝑒𝑠𝑡	NOUN
cana-1019	227	35	)	)	PUNCT
cana-1019	227	36	=	=	SYM
cana-1019	227	37	√𝛴{𝑗=1	√𝛴{𝑗=1	NOUN
cana-1019	227	38	}	}	PUNCT
cana-1019	227	39	{	{	PUNCT
cana-1019	227	40	𝑑}(𝑥{𝑖𝑗	𝑑}(𝑥{𝑖𝑗	NOUN
cana-1019	227	41	}	}	PUNCT
cana-1019	227	42	–	–	PUNCT
cana-1019	227	43	𝑥{𝑡𝑒𝑠𝑡,𝑗	𝑥{𝑡𝑒𝑠𝑡,𝑗	NOUN
cana-1019	227	44	}	}	PUNCT
cana-1019	227	45	)	)	PUNCT
cana-1019	227	46	2	2	NUM
cana-1019	227	47	3	3	NUM
cana-1019	227	48	.	.	PUNCT
cana-1019	227	49	voting	voting	NOUN
cana-1019	227	50	mechanism	mechanism	NOUN
cana-1019	227	51	:	:	PUNCT
cana-1019	227	52	count	count	VERB
cana-1019	227	53	the	the	DET
cana-1019	227	54	occurrences	occurrence	NOUN
cana-1019	227	55	of	of	ADP
cana-1019	227	56	each	each	DET
cana-1019	227	57	class	class	NOUN
cana-1019	227	58	among	among	ADP
cana-1019	227	59	the	the	DET
cana-1019	227	60	𝐾	𝐾	PROPN
cana-1019	227	61	nearest	near	ADJ
cana-1019	227	62	neighbors	neighbor	NOUN
cana-1019	227	63	.	.	PUNCT
cana-1019	228	1	assign	assign	VERB
cana-1019	228	2	𝐱_test	𝐱_test	ADV
cana-1019	228	3	to	to	ADP
cana-1019	228	4	the	the	DET
cana-1019	228	5	class	class	NOUN
cana-1019	228	6	that	that	PRON
cana-1019	228	7	is	be	AUX
cana-1019	228	8	most	most	ADV
cana-1019	228	9	common	common	ADJ
cana-1019	228	10	among	among	ADP
cana-1019	228	11	its	its	PRON
cana-1019	228	12	𝐾	𝐾	PROPN
cana-1019	228	13	nearest	near	ADJ
cana-1019	228	14	neighbors	neighbor	NOUN
cana-1019	228	15	.	.	PUNCT
cana-1019	229	1	communications	communication	NOUN
cana-1019	229	2	on	on	ADP
cana-1019	229	3	applied	apply	VERB
cana-1019	229	4	nonlinear	nonlinear	ADJ
cana-1019	229	5	analysis	analysis	NOUN
cana-1019	229	6	issn	issn	NOUN
cana-1019	229	7	:	:	PUNCT
cana-1019	229	8	1074	1074	NUM
cana-1019	229	9	-	-	PUNCT
cana-1019	229	10	133x	133x	NUM
cana-1019	229	11	vol	vol	NOUN
cana-1019	229	12	31	31	NUM
cana-1019	229	13	no	no	NOUN
cana-1019	229	14	.	.	PUNCT
cana-1019	230	1	5s	5s	NUM
cana-1019	230	2	(	(	PUNCT
cana-1019	230	3	2024	2024	NUM
cana-1019	230	4	)	)	PUNCT
cana-1019	230	5	249	249	NUM
cana-1019	230	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1019	230	7	4	4	NUM
cana-1019	230	8	.	.	NOUN
cana-1019	230	9	distance	distance	NOUN
cana-1019	230	10	weighting	weighting	NOUN
cana-1019	230	11	:	:	PUNCT
cana-1019	230	12	weight	weight	NOUN
cana-1019	230	13	the	the	DET
cana-1019	230	14	contribution	contribution	NOUN
cana-1019	230	15	of	of	ADP
cana-1019	230	16	each	each	DET
cana-1019	230	17	neighbor	neighbor	NOUN
cana-1019	230	18	to	to	ADP
cana-1019	230	19	the	the	DET
cana-1019	230	20	prediction	prediction	NOUN
cana-1019	230	21	by	by	ADP
cana-1019	230	22	the	the	DET
cana-1019	230	23	inverse	inverse	NOUN
cana-1019	230	24	of	of	ADP
cana-1019	230	25	their	their	PRON
cana-1019	230	26	distance	distance	NOUN
cana-1019	230	27	:	:	PUNCT
cana-1019	230	28	𝑤𝑖	𝑤𝑖	ADP
cana-1019	230	29	=	=	SYM
cana-1019	230	30	1	1	NUM
cana-1019	230	31	𝐷(𝑥𝑖	𝐷(𝑥𝑖	VERB
cana-1019	230	32	,	,	PUNCT
cana-1019	230	33	𝑥𝑡𝑒𝑠𝑡)2	𝑥𝑡𝑒𝑠𝑡)2	PROPN
cana-1019	230	34	adjust	adjust	VERB
cana-1019	230	35	the	the	DET
cana-1019	230	36	voting	voting	NOUN
cana-1019	230	37	mechanism	mechanism	NOUN
cana-1019	230	38	to	to	PART
cana-1019	230	39	consider	consider	VERB
cana-1019	230	40	the	the	DET
cana-1019	230	41	weighted	weighted	ADJ
cana-1019	230	42	sum	sum	NOUN
cana-1019	230	43	of	of	ADP
cana-1019	230	44	class	class	NOUN
cana-1019	230	45	labels	label	NOUN
cana-1019	230	46	.	.	PUNCT
cana-1019	231	1	c.	c.	PROPN
cana-1019	231	2	hyper	hyper	PROPN
cana-1019	231	3	parameters	parameter	NOUN
cana-1019	231	4	fine	fine	ADV
cana-1019	231	5	-	-	PUNCT
cana-1019	231	6	tuning	tuning	NOUN
cana-1019	231	7	on	on	ADP
cana-1019	231	8	best	good	ADJ
cana-1019	231	9	performing	perform	VERB
cana-1019	231	10	algorithms	algorithm	NOUN
cana-1019	231	11	a.	a.	NOUN
cana-1019	231	12	random	random	ADJ
cana-1019	231	13	forest	forest	NOUN
cana-1019	231	14	with	with	ADP
cana-1019	231	15	fine	fine	ADV
cana-1019	231	16	-	-	PUNCT
cana-1019	231	17	tuning	tune	VERB
cana-1019	231	18	random	random	ADJ
cana-1019	231	19	forest	forest	NOUN
cana-1019	231	20	may	may	AUX
cana-1019	231	21	be	be	AUX
cana-1019	231	22	a	a	DET
cana-1019	231	23	capable	capable	ADJ
cana-1019	231	24	gathering	gathering	NOUN
cana-1019	231	25	learning	learn	VERB
cana-1019	231	26	strategy	strategy	NOUN
cana-1019	231	27	that	that	PRON
cana-1019	231	28	combines	combine	VERB
cana-1019	231	29	different	different	ADJ
cana-1019	231	30	choice	choice	NOUN
cana-1019	231	31	trees	tree	NOUN
cana-1019	231	32	to	to	PART
cana-1019	231	33	move	move	VERB
cana-1019	231	34	forward	forward	ADV
cana-1019	231	35	prescient	prescient	ADJ
cana-1019	231	36	execution	execution	NOUN
cana-1019	231	37	and	and	CCONJ
cana-1019	231	38	diminish	diminish	VERB
cana-1019	231	39	overfitting	overfitting	NOUN
cana-1019	231	40	.	.	PUNCT
cana-1019	232	1	fine	fine	ADJ
cana-1019	232	2	-	-	PUNCT
cana-1019	232	3	tuning	tuning	NOUN
cana-1019	232	4	includes	include	VERB
cana-1019	232	5	optimizing	optimize	VERB
cana-1019	232	6	different	different	ADJ
cana-1019	232	7	parameters	parameter	NOUN
cana-1019	232	8	to	to	PART
cana-1019	232	9	upgrade	upgrade	VERB
cana-1019	232	10	show	show	VERB
cana-1019	232	11	exactness	exactness	NOUN
cana-1019	232	12	and	and	CCONJ
cana-1019	232	13	generalization	generalization	NOUN
cana-1019	232	14	.	.	PUNCT
cana-1019	233	1	parameters	parameter	NOUN
cana-1019	233	2	to	to	PART
cana-1019	233	3	tune	tune	NOUN
cana-1019	233	4	:	:	PUNCT
cana-1019	233	5	•	•	NUM
cana-1019	233	6	number	number	NOUN
cana-1019	233	7	of	of	ADP
cana-1019	233	8	trees	tree	NOUN
cana-1019	233	9	(	(	PUNCT
cana-1019	233	10	n_estimators	n_estimator	NOUN
cana-1019	233	11	):	):	PUNCT
cana-1019	233	12	determines	determine	VERB
cana-1019	233	13	the	the	DET
cana-1019	233	14	number	number	NOUN
cana-1019	233	15	of	of	ADP
cana-1019	233	16	decision	decision	NOUN
cana-1019	233	17	trees	tree	NOUN
cana-1019	233	18	in	in	ADP
cana-1019	233	19	the	the	DET
cana-1019	233	20	forest	forest	NOUN
cana-1019	233	21	.	.	PUNCT
cana-1019	234	1	increasing	increase	VERB
cana-1019	234	2	n_estimators	n_estimator	NOUN
cana-1019	234	3	can	can	AUX
cana-1019	234	4	improve	improve	VERB
cana-1019	234	5	model	model	NOUN
cana-1019	234	6	performance	performance	NOUN
cana-1019	234	7	until	until	ADP
cana-1019	234	8	a	a	DET
cana-1019	234	9	certain	certain	ADJ
cana-1019	234	10	point	point	NOUN
cana-1019	234	11	,	,	PUNCT
cana-1019	234	12	beyond	beyond	ADP
cana-1019	234	13	which	which	PRON
cana-1019	234	14	it	it	PRON
cana-1019	234	15	may	may	AUX
cana-1019	234	16	lead	lead	VERB
cana-1019	234	17	to	to	ADP
cana-1019	234	18	overfitting	overfitte	VERB
cana-1019	234	19	.	.	PUNCT
cana-1019	235	1	•	•	NUM
cana-1019	235	2	tree	tree	NOUN
cana-1019	235	3	depth	depth	NOUN
cana-1019	235	4	(	(	PUNCT
cana-1019	235	5	max_depth	max_depth	NOUN
cana-1019	235	6	):	):	PUNCT
cana-1019	235	7	controls	control	VERB
cana-1019	235	8	the	the	DET
cana-1019	235	9	maximum	maximum	ADJ
cana-1019	235	10	depth	depth	NOUN
cana-1019	235	11	of	of	ADP
cana-1019	235	12	each	each	DET
cana-1019	235	13	decision	decision	NOUN
cana-1019	235	14	tree	tree	NOUN
cana-1019	235	15	.	.	PUNCT
cana-1019	236	1	deeper	deep	ADJ
cana-1019	236	2	trees	tree	NOUN
cana-1019	236	3	can	can	AUX
cana-1019	236	4	capture	capture	VERB
cana-1019	236	5	more	more	ADJ
cana-1019	236	6	complex	complex	ADJ
cana-1019	236	7	relationships	relationship	NOUN
cana-1019	236	8	in	in	ADP
cana-1019	236	9	the	the	DET
cana-1019	236	10	data	datum	NOUN
cana-1019	236	11	but	but	CCONJ
cana-1019	236	12	may	may	AUX
cana-1019	236	13	also	also	ADV
cana-1019	236	14	overfit	overfit	VERB
cana-1019	236	15	.	.	PUNCT
cana-1019	237	1	•	•	NUM
cana-1019	237	2	minimum	minimum	NOUN
cana-1019	237	3	samples	sample	NOUN
cana-1019	237	4	per	per	ADP
cana-1019	237	5	leaf	leaf	NOUN
cana-1019	237	6	(	(	PUNCT
cana-1019	237	7	min_samples_leaf	min_samples_leaf	NOUN
cana-1019	237	8	):	):	PUNCT
cana-1019	237	9	specifies	specify	VERB
cana-1019	237	10	the	the	DET
cana-1019	237	11	minimum	minimum	ADJ
cana-1019	237	12	number	number	NOUN
cana-1019	237	13	of	of	ADP
cana-1019	237	14	samples	sample	NOUN
cana-1019	237	15	required	require	VERB
cana-1019	237	16	to	to	PART
cana-1019	237	17	be	be	AUX
cana-1019	237	18	at	at	ADP
cana-1019	237	19	a	a	DET
cana-1019	237	20	leaf	leaf	NOUN
cana-1019	237	21	node	node	NOUN
cana-1019	237	22	.	.	PUNCT
cana-1019	238	1	increasing	increase	VERB
cana-1019	238	2	min_samples_leaf	min_samples_leaf	NOUN
cana-1019	238	3	can	can	AUX
cana-1019	238	4	prevent	prevent	VERB
cana-1019	238	5	overfitting	overfitte	VERB
cana-1019	238	6	by	by	ADP
cana-1019	238	7	ensuring	ensure	VERB
cana-1019	238	8	that	that	SCONJ
cana-1019	238	9	each	each	DET
cana-1019	238	10	leaf	leaf	NOUN
cana-1019	238	11	node	node	NOUN
cana-1019	238	12	has	have	VERB
cana-1019	238	13	sufficient	sufficient	ADJ
cana-1019	238	14	samples	sample	NOUN
cana-1019	238	15	.	.	PUNCT
cana-1019	239	1	•	•	NUM
cana-1019	239	2	feature	feature	NOUN
cana-1019	239	3	subset	subset	NOUN
cana-1019	239	4	size	size	NOUN
cana-1019	239	5	(	(	PUNCT
cana-1019	239	6	max_features	max_feature	NOUN
cana-1019	239	7	):	):	PUNCT
cana-1019	239	8	determines	determine	VERB
cana-1019	239	9	the	the	DET
cana-1019	239	10	number	number	NOUN
cana-1019	239	11	of	of	ADP
cana-1019	239	12	features	feature	NOUN
cana-1019	239	13	to	to	PART
cana-1019	239	14	consider	consider	VERB
cana-1019	239	15	when	when	SCONJ
cana-1019	239	16	looking	look	VERB
cana-1019	239	17	for	for	ADP
cana-1019	239	18	the	the	DET
cana-1019	239	19	best	good	ADJ
cana-1019	239	20	split	split	NOUN
cana-1019	239	21	.	.	PUNCT
cana-1019	240	1	smaller	small	ADJ
cana-1019	240	2	max_features	max_feature	NOUN
cana-1019	240	3	can	can	AUX
cana-1019	240	4	reduce	reduce	VERB
cana-1019	240	5	overfitting	overfitte	VERB
cana-1019	240	6	.	.	PUNCT
cana-1019	241	1	•	•	NUM
cana-1019	241	2	bootstrap	bootstrap	NOUN
cana-1019	241	3	sampling	sampling	NOUN
cana-1019	241	4	(	(	PUNCT
cana-1019	241	5	bootstrap	bootstrap	NOUN
cana-1019	241	6	):	):	PUNCT
cana-1019	241	7	specifies	specifie	NOUN
cana-1019	241	8	whether	whether	SCONJ
cana-1019	241	9	samples	sample	NOUN
cana-1019	241	10	are	be	AUX
cana-1019	241	11	drawn	draw	VERB
cana-1019	241	12	with	with	ADP
cana-1019	241	13	or	or	CCONJ
cana-1019	241	14	without	without	ADP
cana-1019	241	15	replacement	replacement	NOUN
cana-1019	241	16	.	.	PUNCT
cana-1019	242	1	setting	set	VERB
cana-1019	242	2	bootstrap	bootstrap	NOUN
cana-1019	242	3	=	=	ADJ
cana-1019	242	4	true	true	ADJ
cana-1019	242	5	enables	enable	VERB
cana-1019	242	6	bagging	bagging	NOUN
cana-1019	242	7	,	,	PUNCT
cana-1019	242	8	which	which	PRON
cana-1019	242	9	generally	generally	ADV
cana-1019	242	10	improves	improve	VERB
cana-1019	242	11	model	model	NOUN
cana-1019	242	12	performance	performance	NOUN
cana-1019	242	13	.	.	PUNCT
cana-1019	243	1	fine	fine	ADJ
cana-1019	243	2	-	-	PUNCT
cana-1019	243	3	tuning	tune	VERB
cana-1019	243	4	strategy	strategy	NOUN
cana-1019	243	5	:	:	PUNCT
cana-1019	243	6	•	•	NUM
cana-1019	243	7	grid	grid	NOUN
cana-1019	243	8	search	search	NOUN
cana-1019	243	9	or	or	CCONJ
cana-1019	243	10	random	random	ADJ
cana-1019	243	11	search	search	NOUN
cana-1019	243	12	:	:	PUNCT
cana-1019	243	13	perform	perform	VERB
cana-1019	243	14	grid	grid	NOUN
cana-1019	243	15	search	search	NOUN
cana-1019	243	16	over	over	ADP
cana-1019	243	17	a	a	DET
cana-1019	243	18	predefined	predefine	VERB
cana-1019	243	19	set	set	NOUN
cana-1019	243	20	of	of	ADP
cana-1019	243	21	hyperparameters	hyperparameter	NOUN
cana-1019	243	22	or	or	CCONJ
cana-1019	243	23	random	random	ADJ
cana-1019	243	24	search	search	NOUN
cana-1019	243	25	across	across	ADP
cana-1019	243	26	a	a	DET
cana-1019	243	27	specified	specified	ADJ
cana-1019	243	28	range	range	NOUN
cana-1019	243	29	to	to	PART
cana-1019	243	30	find	find	VERB
cana-1019	243	31	the	the	DET
cana-1019	243	32	optimal	optimal	ADJ
cana-1019	243	33	combination	combination	NOUN
cana-1019	243	34	.	.	PUNCT
cana-1019	244	1	•	•	NUM
cana-1019	244	2	cross	cross	NOUN
cana-1019	244	3	-	-	NOUN
cana-1019	244	4	validation	validation	ADJ
cana-1019	244	5	:	:	PUNCT
cana-1019	244	6	use	use	VERB
cana-1019	244	7	cross	cross	NOUN
cana-1019	244	8	-	-	NOUN
cana-1019	244	9	validation	validation	NOUN
cana-1019	244	10	to	to	PART
cana-1019	244	11	evaluate	evaluate	VERB
cana-1019	244	12	each	each	DET
cana-1019	244	13	combination	combination	NOUN
cana-1019	244	14	of	of	ADP
cana-1019	244	15	hyperparameters	hyperparameter	NOUN
cana-1019	244	16	.	.	PUNCT
cana-1019	245	1	this	this	PRON
cana-1019	245	2	helps	help	VERB
cana-1019	245	3	in	in	ADP
cana-1019	245	4	selecting	select	VERB
cana-1019	245	5	the	the	DET
cana-1019	245	6	set	set	NOUN
cana-1019	245	7	that	that	PRON
cana-1019	245	8	provides	provide	VERB
cana-1019	245	9	the	the	DET
cana-1019	245	10	best	good	ADJ
cana-1019	245	11	generalization	generalization	NOUN
cana-1019	245	12	performance	performance	NOUN
cana-1019	245	13	.	.	PUNCT
cana-1019	246	1	b.	b.	PROPN
cana-1019	247	1	xgboost	xgboost	X
cana-1019	248	1	with	with	ADP
cana-1019	248	2	fine	fine	ADV
cana-1019	248	3	-	-	PUNCT
cana-1019	248	4	tuning	tuning	NOUN
cana-1019	248	5	xgboost	xgboost	NOUN
cana-1019	248	6	is	be	AUX
cana-1019	248	7	an	an	DET
cana-1019	248	8	advanced	advanced	ADJ
cana-1019	248	9	implementation	implementation	NOUN
cana-1019	248	10	of	of	ADP
cana-1019	248	11	gradient	gradient	NOUN
cana-1019	248	12	boosting	boost	VERB
cana-1019	248	13	that	that	PRON
cana-1019	248	14	offers	offer	VERB
cana-1019	248	15	better	well	ADJ
cana-1019	248	16	performance	performance	NOUN
cana-1019	248	17	and	and	CCONJ
cana-1019	248	18	efficiency	efficiency	NOUN
cana-1019	248	19	over	over	ADP
cana-1019	248	20	traditional	traditional	ADJ
cana-1019	248	21	gradient	gradient	ADJ
cana-1019	248	22	boosting	boost	VERB
cana-1019	248	23	methods	method	NOUN
cana-1019	248	24	.	.	PUNCT
cana-1019	249	1	fine	fine	ADJ
cana-1019	249	2	-	-	PUNCT
cana-1019	249	3	tuning	tuning	NOUN
cana-1019	249	4	xgboost	xgboost	ADV
cana-1019	249	5	involves	involve	VERB
cana-1019	249	6	optimizing	optimize	VERB
cana-1019	249	7	various	various	ADJ
cana-1019	249	8	parameters	parameter	NOUN
cana-1019	249	9	to	to	PART
cana-1019	249	10	achieve	achieve	VERB
cana-1019	249	11	optimal	optimal	ADJ
cana-1019	249	12	performance	performance	NOUN
cana-1019	249	13	.	.	PUNCT
cana-1019	250	1	parameters	parameter	NOUN
cana-1019	250	2	to	to	PART
cana-1019	250	3	tune	tune	NOUN
cana-1019	250	4	:	:	PUNCT
cana-1019	250	5	•	•	ADV
cana-1019	250	6	learning	learn	VERB
cana-1019	250	7	rate	rate	NOUN
cana-1019	250	8	(	(	PUNCT
cana-1019	250	9	eta	eta	ADV
cana-1019	250	10	or	or	CCONJ
cana-1019	250	11	learning_rate	learning_rate	NUM
cana-1019	250	12	):	):	PUNCT
cana-1019	250	13	controls	control	VERB
cana-1019	250	14	the	the	DET
cana-1019	250	15	step	step	NOUN
cana-1019	250	16	size	size	NOUN
cana-1019	250	17	at	at	ADP
cana-1019	250	18	each	each	DET
cana-1019	250	19	iteration	iteration	NOUN
cana-1019	250	20	while	while	SCONJ
cana-1019	250	21	moving	move	VERB
cana-1019	250	22	toward	toward	ADP
cana-1019	250	23	a	a	DET
cana-1019	250	24	minimum	minimum	NOUN
cana-1019	250	25	of	of	ADP
cana-1019	250	26	the	the	DET
cana-1019	250	27	loss	loss	NOUN
cana-1019	250	28	function	function	NOUN
cana-1019	250	29	.	.	PUNCT
cana-1019	251	1	communications	communication	NOUN
cana-1019	251	2	on	on	ADP
cana-1019	251	3	applied	apply	VERB
cana-1019	251	4	nonlinear	nonlinear	ADJ
cana-1019	251	5	analysis	analysis	NOUN
cana-1019	251	6	issn	issn	NOUN
cana-1019	251	7	:	:	PUNCT
cana-1019	251	8	1074	1074	NUM
cana-1019	251	9	-	-	PUNCT
cana-1019	251	10	133x	133x	NUM
cana-1019	251	11	vol	vol	NOUN
cana-1019	251	12	31	31	NUM
cana-1019	251	13	no	no	NOUN
cana-1019	251	14	.	.	PUNCT
cana-1019	252	1	5s	5s	NUM
cana-1019	252	2	(	(	PUNCT
cana-1019	252	3	2024	2024	NUM
cana-1019	252	4	)	)	PUNCT
cana-1019	252	5	250	250	NUM
cana-1019	252	6	https://internationalpubls.com	https://internationalpubls.com	NOUN
cana-1019	252	7	•	•	NOUN
cana-1019	252	8	number	number	NOUN
cana-1019	252	9	of	of	ADP
cana-1019	252	10	trees	tree	NOUN
cana-1019	252	11	(	(	PUNCT
cana-1019	252	12	n_estimators	n_estimator	NOUN
cana-1019	252	13	):	):	PUNCT
cana-1019	252	14	specifies	specifie	NOUN
cana-1019	252	15	the	the	DET
cana-1019	252	16	number	number	NOUN
cana-1019	252	17	of	of	ADP
cana-1019	252	18	boosting	boost	VERB
cana-1019	252	19	rounds	round	NOUN
cana-1019	252	20	or	or	CCONJ
cana-1019	252	21	trees	tree	NOUN
cana-1019	252	22	to	to	PART
cana-1019	252	23	build	build	VERB
cana-1019	252	24	.	.	PUNCT
cana-1019	253	1	•	•	NUM
cana-1019	253	2	maximum	maximum	ADJ
cana-1019	253	3	tree	tree	NOUN
cana-1019	253	4	depth	depth	NOUN
cana-1019	253	5	(	(	PUNCT
cana-1019	253	6	max_depth	max_depth	NOUN
cana-1019	253	7	):	):	PUNCT
cana-1019	253	8	limits	limit	VERB
cana-1019	253	9	the	the	DET
cana-1019	253	10	depth	depth	NOUN
cana-1019	253	11	of	of	ADP
cana-1019	253	12	each	each	DET
cana-1019	253	13	tree	tree	NOUN
cana-1019	253	14	.	.	PUNCT
cana-1019	254	1	deeper	deep	ADJ
cana-1019	254	2	trees	tree	NOUN
cana-1019	254	3	can	can	AUX
cana-1019	254	4	model	model	VERB
cana-1019	254	5	more	more	ADJ
cana-1019	254	6	complex	complex	ADJ
cana-1019	254	7	relationships	relationship	NOUN
cana-1019	254	8	but	but	CCONJ
cana-1019	254	9	may	may	AUX
cana-1019	254	10	lead	lead	VERB
cana-1019	254	11	to	to	ADP
cana-1019	254	12	overfitting	overfitte	VERB
cana-1019	254	13	.	.	PUNCT
cana-1019	255	1	•	•	NUM
cana-1019	255	2	subsample	subsample	ADJ
cana-1019	255	3	ratio	ratio	NOUN
cana-1019	255	4	(	(	PUNCT
cana-1019	255	5	subsample	subsample	ADV
cana-1019	255	6	):	):	PUNCT
cana-1019	255	7	specifies	specify	VERB
cana-1019	255	8	the	the	DET
cana-1019	255	9	fraction	fraction	NOUN
cana-1019	255	10	of	of	ADP
cana-1019	255	11	samples	sample	NOUN
cana-1019	255	12	to	to	PART
cana-1019	255	13	be	be	AUX
cana-1019	255	14	used	use	VERB
cana-1019	255	15	for	for	ADP
cana-1019	255	16	training	train	VERB
cana-1019	255	17	each	each	DET
cana-1019	255	18	tree	tree	NOUN
cana-1019	255	19	.	.	PUNCT
cana-1019	256	1	lower	low	ADJ
cana-1019	256	2	values	value	NOUN
cana-1019	256	3	prevent	prevent	VERB
cana-1019	256	4	overfitting	overfitting	NOUN
cana-1019	256	5	but	but	CCONJ
cana-1019	256	6	may	may	AUX
cana-1019	256	7	increase	increase	VERB
cana-1019	256	8	bias	bias	NOUN
cana-1019	256	9	.	.	PUNCT
cana-1019	257	1	•	•	NUM
cana-1019	257	2	column	column	NOUN
cana-1019	257	3	subsampling	subsample	VERB
cana-1019	257	4	(	(	PUNCT
cana-1019	257	5	colsample_bytree	colsample_bytree	ADJ
cana-1019	257	6	):	):	PUNCT
cana-1019	257	7	specifies	specify	VERB
cana-1019	257	8	the	the	DET
cana-1019	257	9	fraction	fraction	NOUN
cana-1019	257	10	of	of	ADP
cana-1019	257	11	features	feature	NOUN
cana-1019	257	12	to	to	PART
cana-1019	257	13	be	be	AUX
cana-1019	257	14	randomly	randomly	ADV
cana-1019	257	15	sampled	sample	VERB
cana-1019	257	16	for	for	ADP
cana-1019	257	17	each	each	DET
cana-1019	257	18	tree	tree	NOUN
cana-1019	257	19	.	.	PUNCT
cana-1019	258	1	fine	fine	ADJ
cana-1019	258	2	-	-	PUNCT
cana-1019	258	3	tuning	tune	VERB
cana-1019	258	4	strategy	strategy	NOUN
cana-1019	258	5	:	:	PUNCT
cana-1019	258	6	•	•	NUM
cana-1019	258	7	grid	grid	NOUN
cana-1019	258	8	search	search	NOUN
cana-1019	258	9	or	or	CCONJ
cana-1019	258	10	random	random	ADJ
cana-1019	258	11	search	search	NOUN
cana-1019	258	12	:	:	PUNCT
cana-1019	258	13	search	search	NOUN
cana-1019	258	14	over	over	ADP
cana-1019	258	15	a	a	DET
cana-1019	258	16	grid	grid	NOUN
cana-1019	258	17	of	of	ADP
cana-1019	258	18	hyperparameters	hyperparameter	NOUN
cana-1019	258	19	or	or	CCONJ
cana-1019	258	20	randomly	randomly	ADJ
cana-1019	258	21	sample	sample	NOUN
cana-1019	258	22	from	from	ADP
cana-1019	258	23	a	a	DET
cana-1019	258	24	distribution	distribution	NOUN
cana-1019	258	25	of	of	ADP
cana-1019	258	26	hyperparameters	hyperparameter	NOUN
cana-1019	258	27	.	.	PUNCT
cana-1019	259	1	•	•	NUM
cana-1019	259	2	early	early	ADJ
cana-1019	259	3	stopping	stopping	NOUN
cana-1019	259	4	:	:	PUNCT
cana-1019	259	5	use	use	VERB
cana-1019	259	6	early	early	ADV
cana-1019	259	7	stopping	stop	VERB
cana-1019	259	8	to	to	PART
cana-1019	259	9	halt	halt	VERB
cana-1019	259	10	the	the	DET
cana-1019	259	11	training	training	NOUN
cana-1019	259	12	process	process	NOUN
cana-1019	259	13	when	when	SCONJ
cana-1019	259	14	model	model	NOUN
cana-1019	259	15	performance	performance	NOUN
cana-1019	259	16	stops	stop	VERB
cana-1019	259	17	improving	improve	VERB
cana-1019	259	18	on	on	ADP
cana-1019	259	19	a	a	DET
cana-1019	259	20	validation	validation	NOUN
cana-1019	259	21	dataset	dataset	NOUN
cana-1019	259	22	.	.	PUNCT
cana-1019	260	1	c.	c.	PROPN
cana-1019	260	2	svm	svm	PROPN
cana-1019	260	3	with	with	ADP
cana-1019	260	4	fine	fine	ADV
cana-1019	260	5	-	-	PUNCT
cana-1019	260	6	tuning	tune	VERB
cana-1019	260	7	support	support	NOUN
cana-1019	260	8	vector	vector	NOUN
cana-1019	260	9	machines	machine	NOUN
cana-1019	260	10	(	(	PUNCT
cana-1019	260	11	svms	svms	NOUN
cana-1019	260	12	)	)	PUNCT
cana-1019	260	13	are	be	AUX
cana-1019	260	14	powerful	powerful	ADJ
cana-1019	260	15	supervised	supervised	ADJ
cana-1019	260	16	learning	learning	NOUN
cana-1019	260	17	models	model	NOUN
cana-1019	260	18	used	use	VERB
cana-1019	260	19	for	for	ADP
cana-1019	260	20	classification	classification	NOUN
cana-1019	260	21	and	and	CCONJ
cana-1019	260	22	regression	regression	NOUN
cana-1019	260	23	tasks	task	NOUN
cana-1019	260	24	.	.	PUNCT
cana-1019	261	1	fine	fine	ADJ
cana-1019	261	2	-	-	PUNCT
cana-1019	261	3	tuning	tune	VERB
cana-1019	261	4	svm	svm	NOUN
cana-1019	261	5	involves	involve	VERB
cana-1019	261	6	optimizing	optimize	VERB
cana-1019	261	7	parameters	parameter	NOUN
cana-1019	261	8	that	that	PRON
cana-1019	261	9	influence	influence	VERB
cana-1019	261	10	the	the	DET
cana-1019	261	11	decision	decision	NOUN
cana-1019	261	12	boundary	boundary	NOUN
cana-1019	261	13	and	and	CCONJ
cana-1019	261	14	regularization	regularization	NOUN
cana-1019	261	15	.	.	PUNCT
cana-1019	262	1	parameters	parameter	NOUN
cana-1019	262	2	to	to	PART
cana-1019	262	3	tune	tune	NOUN
cana-1019	262	4	:	:	PUNCT
cana-1019	262	5	•	•	NUM
cana-1019	262	6	kernel	kernel	PROPN
cana-1019	262	7	choice	choice	NOUN
cana-1019	262	8	and	and	CCONJ
cana-1019	262	9	parameters	parameter	NOUN
cana-1019	262	10	(	(	PUNCT
cana-1019	262	11	kernel	kernel	PROPN
cana-1019	262	12	,	,	PUNCT
cana-1019	262	13	c	c	X
cana-1019	262	14	,	,	PUNCT
cana-1019	262	15	gamma	gamma	NOUN
cana-1019	262	16	):	):	PUNCT
cana-1019	262	17	select	select	VERB
cana-1019	262	18	the	the	DET
cana-1019	262	19	kernel	kernel	PROPN
cana-1019	262	20	type	type	NOUN
cana-1019	262	21	(	(	PUNCT
cana-1019	262	22	linear	linear	ADJ
cana-1019	262	23	,	,	PUNCT
cana-1019	262	24	polynomial	polynomial	ADJ
cana-1019	262	25	,	,	PUNCT
cana-1019	262	26	radial	radial	ADJ
cana-1019	262	27	basis	basis	NOUN
cana-1019	262	28	function	function	NOUN
cana-1019	262	29	)	)	PUNCT
cana-1019	262	30	and	and	CCONJ
cana-1019	262	31	tune	tune	NOUN
cana-1019	262	32	associated	associated	ADJ
cana-1019	262	33	parameters	parameter	NOUN
cana-1019	262	34	.	.	PUNCT
cana-1019	263	1	•	•	NUM
cana-1019	263	2	c	c	X
cana-1019	263	3	:	:	PUNCT
cana-1019	263	4	penalty	penalty	NOUN
cana-1019	263	5	parameter	parameter	NOUN
cana-1019	263	6	for	for	ADP
cana-1019	263	7	the	the	DET
cana-1019	263	8	error	error	NOUN
cana-1019	263	9	term	term	NOUN
cana-1019	263	10	.	.	PUNCT
cana-1019	264	1	controls	control	VERB
cana-1019	264	2	the	the	DET
cana-1019	264	3	trade	trade	NOUN
cana-1019	264	4	-	-	PUNCT
cana-1019	264	5	off	off	NOUN
cana-1019	264	6	between	between	ADP
cana-1019	264	7	maximizing	maximize	VERB
cana-1019	264	8	the	the	DET
cana-1019	264	9	margin	margin	NOUN
cana-1019	264	10	and	and	CCONJ
cana-1019	264	11	minimizing	minimize	VERB
cana-1019	264	12	classification	classification	NOUN
cana-1019	264	13	error	error	NOUN
cana-1019	264	14	.	.	PUNCT
cana-1019	265	1	•	•	NUM
cana-1019	265	2	gamma	gamma	PROPN
cana-1019	265	3	:	:	PUNCT
cana-1019	265	4	kernel	kernel	PROPN
cana-1019	265	5	coefficient	coefficient	NOUN
cana-1019	265	6	for	for	ADP
cana-1019	265	7	‘	'	PUNCT
cana-1019	265	8	rbf	rbf	PROPN
cana-1019	265	9	’	'	PUNCT
cana-1019	265	10	,	,	PUNCT
cana-1019	265	11	‘	'	PUNCT
cana-1019	265	12	poly	poly	ADJ
cana-1019	265	13	’	'	PUNCT
cana-1019	265	14	,	,	PUNCT
cana-1019	265	15	and	and	CCONJ
cana-1019	265	16	‘	'	PUNCT
cana-1019	265	17	sigmoid	sigmoid	NOUN
cana-1019	265	18	’	'	PUNCT
cana-1019	265	19	.	.	PUNCT
cana-1019	266	1	higher	high	ADJ
cana-1019	266	2	values	value	NOUN
cana-1019	266	3	lead	lead	VERB
cana-1019	266	4	to	to	ADP
cana-1019	266	5	tighter	tight	ADJ
cana-1019	266	6	decision	decision	NOUN
cana-1019	266	7	boundaries	boundary	NOUN
cana-1019	266	8	,	,	PUNCT
cana-1019	266	9	potentially	potentially	ADV
cana-1019	266	10	overfitting	overfitte	VERB
cana-1019	266	11	the	the	DET
cana-1019	266	12	training	training	NOUN
cana-1019	266	13	data	datum	NOUN
cana-1019	266	14	.	.	PUNCT
cana-1019	267	1	•	•	NUM
cana-1019	267	2	regularization	regularization	NOUN
cana-1019	267	3	(	(	PUNCT
cana-1019	267	4	c	c	NOUN
cana-1019	267	5	):	):	PUNCT
cana-1019	267	6	controls	control	VERB
cana-1019	267	7	the	the	DET
cana-1019	267	8	trade	trade	NOUN
cana-1019	267	9	-	-	PUNCT
cana-1019	267	10	off	off	NOUN
cana-1019	267	11	between	between	ADP
cana-1019	267	12	a	a	DET
cana-1019	267	13	larger	large	ADJ
cana-1019	267	14	margin	margin	NOUN
cana-1019	267	15	and	and	CCONJ
cana-1019	267	16	higher	high	ADJ
cana-1019	267	17	training	training	NOUN
cana-1019	267	18	error	error	NOUN
cana-1019	267	19	.	.	PUNCT
cana-1019	268	1	higher	high	ADJ
cana-1019	268	2	values	value	NOUN
cana-1019	268	3	of	of	ADP
cana-1019	268	4	c	c	PROPN
cana-1019	268	5	allow	allow	VERB
cana-1019	268	6	more	more	ADJ
cana-1019	268	7	training	training	NOUN
cana-1019	268	8	points	point	NOUN
cana-1019	268	9	to	to	PART
cana-1019	268	10	be	be	AUX
cana-1019	268	11	correctly	correctly	ADV
cana-1019	268	12	classified	classify	VERB
cana-1019	268	13	at	at	ADP
cana-1019	268	14	the	the	DET
cana-1019	268	15	cost	cost	NOUN
cana-1019	268	16	of	of	ADP
cana-1019	268	17	a	a	DET
cana-1019	268	18	smaller	small	ADJ
cana-1019	268	19	margin	margin	NOUN
cana-1019	268	20	.	.	PUNCT
cana-1019	269	1	•	•	ADJ
cana-1019	269	2	kernel	kernel	PROPN
cana-1019	269	3	parameters	parameter	NOUN
cana-1019	269	4	(	(	PUNCT
cana-1019	269	5	gamma	gamma	NOUN
cana-1019	269	6	):	):	PUNCT
cana-1019	269	7	influence	influence	NOUN
cana-1019	269	8	the	the	DET
cana-1019	269	9	decision	decision	NOUN
cana-1019	269	10	boundary	boundary	NOUN
cana-1019	269	11	's	's	PART
cana-1019	269	12	flexibility	flexibility	NOUN
cana-1019	269	13	.	.	PUNCT
cana-1019	270	1	larger	large	ADJ
cana-1019	270	2	values	value	NOUN
cana-1019	270	3	of	of	ADP
cana-1019	270	4	gamma	gamma	NOUN
cana-1019	270	5	can	can	AUX
cana-1019	270	6	lead	lead	VERB
cana-1019	270	7	to	to	ADP
cana-1019	270	8	overfitting	overfitte	VERB
cana-1019	270	9	.	.	PUNCT
cana-1019	271	1	fine	fine	ADJ
cana-1019	271	2	-	-	PUNCT
cana-1019	271	3	tuning	tune	VERB
cana-1019	271	4	strategy	strategy	NOUN
cana-1019	271	5	:	:	PUNCT
cana-1019	271	6	•	•	NUM
cana-1019	271	7	grid	grid	NOUN
cana-1019	271	8	search	search	NOUN
cana-1019	271	9	or	or	CCONJ
cana-1019	271	10	random	random	ADJ
cana-1019	271	11	search	search	NOUN
cana-1019	271	12	:	:	PUNCT
cana-1019	271	13	explore	explore	VERB
cana-1019	271	14	a	a	DET
cana-1019	271	15	grid	grid	NOUN
cana-1019	271	16	of	of	ADP
cana-1019	271	17	hyperparameters	hyperparameter	NOUN
cana-1019	271	18	to	to	PART
cana-1019	271	19	find	find	VERB
cana-1019	271	20	the	the	DET
cana-1019	271	21	combination	combination	NOUN
cana-1019	271	22	that	that	PRON
cana-1019	271	23	maximizes	maximize	VERB
cana-1019	271	24	model	model	NOUN
cana-1019	271	25	performance	performance	NOUN
cana-1019	271	26	.	.	PUNCT
cana-1019	272	1	•	•	NUM
cana-1019	272	2	cross	cross	NOUN
cana-1019	272	3	-	-	NOUN
cana-1019	272	4	validation	validation	ADJ
cana-1019	272	5	:	:	PUNCT
cana-1019	272	6	use	use	VERB
cana-1019	272	7	cross	cross	NOUN
cana-1019	272	8	-	-	NOUN
cana-1019	272	9	validation	validation	NOUN
cana-1019	272	10	to	to	PART
cana-1019	272	11	evaluate	evaluate	VERB
cana-1019	272	12	each	each	DET
cana-1019	272	13	combination	combination	NOUN
cana-1019	272	14	of	of	ADP
cana-1019	272	15	hyperparameters	hyperparameter	NOUN
cana-1019	272	16	and	and	CCONJ
cana-1019	272	17	select	select	VERB
cana-1019	272	18	the	the	DET
cana-1019	272	19	one	one	NOUN
cana-1019	272	20	with	with	ADP
cana-1019	272	21	the	the	DET
cana-1019	272	22	best	good	ADJ
cana-1019	272	23	average	average	ADJ
cana-1019	272	24	performance	performance	NOUN
cana-1019	272	25	across	across	ADP
cana-1019	272	26	all	all	DET
cana-1019	272	27	folds	fold	NOUN
cana-1019	272	28	.	.	PUNCT
cana-1019	273	1	communications	communication	NOUN
cana-1019	273	2	on	on	ADP
cana-1019	273	3	applied	apply	VERB
cana-1019	273	4	nonlinear	nonlinear	ADJ
cana-1019	273	5	analysis	analysis	NOUN
cana-1019	273	6	issn	issn	NOUN
cana-1019	273	7	:	:	PUNCT
cana-1019	273	8	1074	1074	NUM
cana-1019	273	9	-	-	PUNCT
cana-1019	273	10	133x	133x	NUM
cana-1019	273	11	vol	vol	NOUN
cana-1019	273	12	31	31	NUM
cana-1019	273	13	no	no	NOUN
cana-1019	273	14	.	.	PUNCT
cana-1019	274	1	5s	5s	NUM
cana-1019	274	2	(	(	PUNCT
cana-1019	274	3	2024	2024	NUM
cana-1019	274	4	)	)	PUNCT
cana-1019	274	5	251	251	NUM
cana-1019	274	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1019	274	7	5	5	NUM
cana-1019	274	8	.	.	NOUN
cana-1019	274	9	result	result	NOUN
cana-1019	274	10	and	and	CCONJ
cana-1019	274	11	discussion	discussion	NOUN
cana-1019	274	12	a.	a.	NOUN
cana-1019	274	13	result	result	NOUN
cana-1019	274	14	for	for	ADP
cana-1019	274	15	machine	machine	NOUN
cana-1019	274	16	learning	learn	VERB
cana-1019	274	17	algorithms	algorithm	NOUN
cana-1019	274	18	table	table	NOUN
cana-1019	274	19	3	3	NUM
cana-1019	274	20	shows	show	VERB
cana-1019	274	21	how	how	SCONJ
cana-1019	274	22	well	well	ADV
cana-1019	274	23	different	different	ADJ
cana-1019	274	24	machine	machine	NOUN
cana-1019	274	25	learning	learn	VERB
cana-1019	274	26	classification	classification	NOUN
cana-1019	274	27	methods	method	NOUN
cana-1019	274	28	worked	work	VERB
cana-1019	274	29	with	with	ADP
cana-1019	274	30	a	a	DET
cana-1019	274	31	dataset	dataset	NOUN
cana-1019	274	32	for	for	ADP
cana-1019	274	33	mood	mood	NOUN
cana-1019	274	34	analysis	analysis	NOUN
cana-1019	274	35	.	.	PUNCT
cana-1019	275	1	for	for	ADP
cana-1019	275	2	each	each	DET
cana-1019	275	3	algorithm	algorithm	NOUN
cana-1019	275	4	,	,	PUNCT
cana-1019	275	5	the	the	DET
cana-1019	275	6	table	table	NOUN
cana-1019	275	7	shows	show	VERB
cana-1019	275	8	its	its	PRON
cana-1019	275	9	accuracy	accuracy	NOUN
cana-1019	275	10	,	,	PUNCT
cana-1019	275	11	precision	precision	NOUN
cana-1019	275	12	,	,	PUNCT
cana-1019	275	13	recall	recall	NOUN
cana-1019	275	14	,	,	PUNCT
cana-1019	275	15	and	and	CCONJ
cana-1019	275	16	f1	f1	PROPN
cana-1019	275	17	score	score	NOUN
cana-1019	275	18	.	.	PUNCT
cana-1019	276	1	these	these	DET
cana-1019	276	2	measurements	measurement	NOUN
cana-1019	276	3	are	be	AUX
cana-1019	276	4	very	very	ADV
cana-1019	276	5	important	important	ADJ
cana-1019	276	6	for	for	ADP
cana-1019	276	7	figuring	figure	VERB
cana-1019	276	8	out	out	ADP
cana-1019	276	9	how	how	SCONJ
cana-1019	276	10	well	well	ADV
cana-1019	276	11	each	each	DET
cana-1019	276	12	program	program	NOUN
cana-1019	276	13	sorts	sort	VERB
cana-1019	276	14	the	the	DET
cana-1019	276	15	emotional	emotional	ADJ
cana-1019	276	16	tone	tone	NOUN
cana-1019	276	17	of	of	ADP
cana-1019	276	18	the	the	DET
cana-1019	276	19	data	datum	NOUN
cana-1019	276	20	.	.	PUNCT
cana-1019	277	1	it	it	PRON
cana-1019	277	2	has	have	VERB
cana-1019	277	3	an	an	DET
cana-1019	277	4	f1	f1	ADJ
cana-1019	277	5	score	score	NOUN
cana-1019	277	6	of	of	ADP
cana-1019	277	7	0.541512	0.541512	NUM
cana-1019	277	8	,	,	PUNCT
cana-1019	277	9	an	an	DET
cana-1019	277	10	accuracy	accuracy	NOUN
cana-1019	277	11	of	of	ADP
cana-1019	277	12	0.568345	0.568345	NUM
cana-1019	277	13	,	,	PUNCT
cana-1019	277	14	a	a	DET
cana-1019	277	15	precision	precision	NOUN
cana-1019	277	16	of	of	ADP
cana-1019	277	17	0.548112	0.548112	NUM
cana-1019	277	18	,	,	PUNCT
cana-1019	277	19	and	and	CCONJ
cana-1019	277	20	a	a	DET
cana-1019	277	21	memory	memory	NOUN
cana-1019	277	22	of	of	ADP
cana-1019	277	23	0.568345	0.568345	NUM
cana-1019	277	24	to	to	PART
cana-1019	277	25	be	be	AUX
cana-1019	277	26	exact	exact	ADJ
cana-1019	277	27	.	.	PUNCT
cana-1019	278	1	these	these	DET
cana-1019	278	2	results	result	NOUN
cana-1019	278	3	show	show	VERB
cana-1019	278	4	that	that	SCONJ
cana-1019	278	5	logistic	logistic	ADJ
cana-1019	278	6	regression	regression	NOUN
cana-1019	278	7	does	do	VERB
cana-1019	278	8	a	a	DET
cana-1019	278	9	decent	decent	ADJ
cana-1019	278	10	job	job	NOUN
cana-1019	278	11	,	,	PUNCT
cana-1019	278	12	but	but	CCONJ
cana-1019	278	13	it	it	PRON
cana-1019	278	14	could	could	AUX
cana-1019	278	15	do	do	VERB
cana-1019	278	16	better	well	ADV
cana-1019	278	17	,	,	PUNCT
cana-1019	278	18	especially	especially	ADV
cana-1019	278	19	when	when	SCONJ
cana-1019	278	20	it	it	PRON
cana-1019	278	21	comes	come	VERB
cana-1019	278	22	to	to	ADP
cana-1019	278	23	finding	find	VERB
cana-1019	278	24	the	the	DET
cana-1019	278	25	right	right	ADJ
cana-1019	278	26	balance	balance	NOUN
cana-1019	278	27	between	between	ADP
cana-1019	278	28	accuracy	accuracy	NOUN
cana-1019	278	29	and	and	CCONJ
cana-1019	278	30	memory	memory	NOUN
cana-1019	278	31	to	to	PART
cana-1019	278	32	raise	raise	VERB
cana-1019	278	33	the	the	DET
cana-1019	278	34	f1	f1	NOUN
cana-1019	278	35	score	score	NOUN
cana-1019	278	36	.	.	PUNCT
cana-1019	279	1	table	table	NOUN
cana-1019	279	2	3	3	NUM
cana-1019	279	3	:	:	PUNCT
cana-1019	279	4	performance	performance	NOUN
cana-1019	279	5	metrics	metric	NOUN
cana-1019	279	6	for	for	ADP
cana-1019	279	7	ml	ml	NOUN
cana-1019	279	8	classification	classification	NOUN
cana-1019	279	9	algorithms	algorithm	NOUN
cana-1019	279	10	for	for	ADP
cana-1019	279	11	mood	mood	NOUN
cana-1019	279	12	analysis	analysis	NOUN
cana-1019	279	13	dataset	dataset	VERB
cana-1019	279	14	algorithm	algorithm	NOUN
cana-1019	279	15	accuracy	accuracy	NOUN
cana-1019	279	16	precision	precision	NOUN
cana-1019	279	17	recall	recall	NOUN
cana-1019	279	18	f1	f1	PROPN
cana-1019	279	19	score	score	NOUN
cana-1019	279	20	logistic	logistic	ADJ
cana-1019	279	21	regression	regression	NOUN
cana-1019	279	22	0.568345	0.568345	NUM
cana-1019	279	23	0.548112	0.548112	NUM
cana-1019	279	24	0.568345	0.568345	NUM
cana-1019	279	25	0.541512	0.541512	NUM
cana-1019	279	26	sgd	sgd	PROPN
cana-1019	279	27	classifier	classifier	NOUN
cana-1019	279	28	0.266187	0.266187	NUM
cana-1019	279	29	0.070856	0.070856	NUM
cana-1019	279	30	0.266187	0.266187	NUM
cana-1019	279	31	0.111920	0.111920	NUM
cana-1019	279	32	gaussian	gaussian	ADJ
cana-1019	279	33	naive	naive	ADJ
cana-1019	279	34	bayes	bayes	NOUN
cana-1019	279	35	0.661871	0.661871	NUM
cana-1019	280	1	0.664540	0.664540	NUM
cana-1019	280	2	0.661871	0.661871	NUM
cana-1019	280	3	0.649197	0.649197	NUM
cana-1019	280	4	decision	decision	NOUN
cana-1019	280	5	tree	tree	NOUN
cana-1019	280	6	0.748201	0.748201	NUM
cana-1019	280	7	0.762372	0.762372	NUM
cana-1019	280	8	0.748201	0.748201	NUM
cana-1019	280	9	0.745440	0.745440	NUM
cana-1019	280	10	random	random	ADJ
cana-1019	280	11	forest	forest	NOUN
cana-1019	280	12	0.856115	0.856115	NUM
cana-1019	280	13	0.861950	0.861950	NUM
cana-1019	280	14	0.856115	0.856115	NUM
cana-1019	280	15	0.855389	0.855389	NUM
cana-1019	280	16	xgb	xgb	NOUN
cana-1019	280	17	classifier	classifier	NOUN
cana-1019	280	18	0.827338	0.827338	NUM
cana-1019	280	19	0.827626	0.827626	NUM
cana-1019	280	20	0.827338	0.827338	NUM
cana-1019	280	21	0.826412	0.826412	NUM
cana-1019	280	22	svm	svm	NOUN
cana-1019	280	23	linear	linear	PROPN
cana-1019	280	24	0.460432	0.460432	NUM
cana-1019	280	25	0.628598	0.628598	NUM
cana-1019	280	26	0.460432	0.460432	NUM
cana-1019	280	27	0.414862	0.414862	NUM
cana-1019	280	28	knn	knn	PROPN
cana-1019	280	29	0.309353	0.309353	NUM
cana-1019	280	30	0.292099	0.292099	NUM
cana-1019	280	31	0.309353	0.309353	NUM
cana-1019	280	32	0.294645	0.294645	NUM
cana-1019	280	33	with	with	ADP
cana-1019	280	34	an	an	DET
cana-1019	280	35	accuracy	accuracy	NOUN
cana-1019	280	36	of	of	ADP
cana-1019	280	37	0.266187	0.266187	NUM
cana-1019	280	38	and	and	CCONJ
cana-1019	280	39	a	a	DET
cana-1019	280	40	very	very	ADV
cana-1019	280	41	low	low	ADJ
cana-1019	280	42	precision	precision	NOUN
cana-1019	280	43	of	of	ADP
cana-1019	280	44	0.070856	0.070856	NUM
cana-1019	280	45	,	,	PUNCT
cana-1019	280	46	the	the	DET
cana-1019	280	47	sgd	sgd	NOUN
cana-1019	280	48	(	(	PUNCT
cana-1019	280	49	stochastic	stochastic	ADJ
cana-1019	280	50	gradient	gradient	ADJ
cana-1019	280	51	descent	descent	NOUN
cana-1019	280	52	)	)	PUNCT
cana-1019	280	53	classifier	classifier	NOUN
cana-1019	280	54	does	do	AUX
cana-1019	280	55	a	a	DET
cana-1019	280	56	lot	lot	NOUN
cana-1019	280	57	worse	bad	ADJ
cana-1019	280	58	.	.	PUNCT
cana-1019	281	1	at	at	ADP
cana-1019	281	2	0.266187	0.266187	NUM
cana-1019	281	3	and	and	CCONJ
cana-1019	281	4	0.111920	0.111920	NUM
cana-1019	281	5	,	,	PUNCT
cana-1019	281	6	the	the	DET
cana-1019	281	7	memory	memory	NOUN
cana-1019	281	8	and	and	CCONJ
cana-1019	281	9	f1	f1	NOUN
cana-1019	281	10	score	score	NOUN
cana-1019	281	11	are	be	AUX
cana-1019	281	12	also	also	ADV
cana-1019	281	13	very	very	ADV
cana-1019	281	14	low	low	ADJ
cana-1019	281	15	.	.	PUNCT
cana-1019	282	1	this	this	PRON
cana-1019	282	2	shows	show	VERB
cana-1019	282	3	that	that	SCONJ
cana-1019	282	4	sgd	sgd	PROPN
cana-1019	282	5	classifier	classifier	NOUN
cana-1019	282	6	has	have	VERB
cana-1019	282	7	trouble	trouble	NOUN
cana-1019	282	8	with	with	ADP
cana-1019	282	9	the	the	DET
cana-1019	282	10	mood	mood	NOUN
cana-1019	282	11	analysis	analysis	NOUN
cana-1019	282	12	job	job	NOUN
cana-1019	282	13	.	.	PUNCT
cana-1019	283	1	this	this	PRON
cana-1019	283	2	could	could	AUX
cana-1019	283	3	be	be	AUX
cana-1019	283	4	because	because	SCONJ
cana-1019	283	5	it	it	PRON
cana-1019	283	6	is	be	AUX
cana-1019	283	7	sensitive	sensitive	ADJ
cana-1019	283	8	to	to	ADP
cana-1019	283	9	changing	change	VERB
cana-1019	283	10	the	the	DET
cana-1019	283	11	size	size	NOUN
cana-1019	283	12	of	of	ADP
cana-1019	283	13	features	feature	NOUN
cana-1019	283	14	and	and	CCONJ
cana-1019	283	15	the	the	DET
cana-1019	283	16	settings	setting	NOUN
cana-1019	283	17	for	for	ADP
cana-1019	283	18	parameters	parameter	NOUN
cana-1019	283	19	.	.	PUNCT
cana-1019	284	1	with	with	ADP
cana-1019	284	2	a	a	DET
cana-1019	284	3	score	score	NOUN
cana-1019	284	4	of	of	ADP
cana-1019	284	5	0.661871	0.661871	NUM
cana-1019	284	6	,	,	PUNCT
cana-1019	284	7	gaussian	gaussian	ADJ
cana-1019	284	8	naive	naive	ADJ
cana-1019	284	9	bayes	bayes	NOUN
cana-1019	284	10	is	be	AUX
cana-1019	284	11	more	more	ADV
cana-1019	284	12	accurate	accurate	ADJ
cana-1019	284	13	than	than	ADP
cana-1019	284	14	logistic	logistic	ADJ
cana-1019	284	15	regression	regression	NOUN
cana-1019	284	16	and	and	CCONJ
cana-1019	284	17	sgd	sgd	PROPN
cana-1019	284	18	classifier	classifier	NOUN
cana-1019	284	19	.	.	PUNCT
cana-1019	285	1	it	it	PRON
cana-1019	285	2	's	be	AUX
cana-1019	285	3	accurate	accurate	ADJ
cana-1019	285	4	0.664540	0.664540	NUM
cana-1019	285	5	times	time	NOUN
cana-1019	285	6	,	,	PUNCT
cana-1019	285	7	correct	correct	ADJ
cana-1019	285	8	0.661871	0.661871	NUM
cana-1019	285	9	times	time	NOUN
cana-1019	285	10	,	,	PUNCT
cana-1019	285	11	and	and	CCONJ
cana-1019	285	12	has	have	VERB
cana-1019	285	13	an	an	DET
cana-1019	285	14	f1	f1	ADJ
cana-1019	285	15	score	score	NOUN
cana-1019	285	16	of	of	ADP
cana-1019	285	17	0.649197	0.649197	NUM
cana-1019	285	18	.	.	PUNCT
cana-1019	286	1	these	these	DET
cana-1019	286	2	measurements	measurement	NOUN
cana-1019	286	3	show	show	VERB
cana-1019	286	4	that	that	SCONJ
cana-1019	286	5	it	it	PRON
cana-1019	286	6	does	do	VERB
cana-1019	286	7	a	a	DET
cana-1019	286	8	good	good	ADJ
cana-1019	286	9	job	job	NOUN
cana-1019	286	10	of	of	ADP
cana-1019	286	11	dealing	deal	VERB
cana-1019	286	12	with	with	ADP
cana-1019	286	13	the	the	DET
cana-1019	286	14	uncertain	uncertain	ADJ
cana-1019	286	15	nature	nature	NOUN
cana-1019	286	16	of	of	ADP
cana-1019	286	17	the	the	DET
cana-1019	286	18	mood	mood	NOUN
cana-1019	286	19	classification	classification	NOUN
cana-1019	286	20	task	task	NOUN
cana-1019	286	21	,	,	PUNCT
cana-1019	286	22	but	but	CCONJ
cana-1019	286	23	it	it	PRON
cana-1019	286	24	still	still	ADV
cana-1019	286	25	needs	need	VERB
cana-1019	286	26	to	to	PART
cana-1019	286	27	get	get	VERB
cana-1019	286	28	better	well	ADJ
cana-1019	286	29	at	at	ADP
cana-1019	286	30	being	be	AUX
cana-1019	286	31	precise	precise	ADJ
cana-1019	286	32	and	and	CCONJ
cana-1019	286	33	remembering	remember	VERB
cana-1019	286	34	things	thing	NOUN
cana-1019	286	35	.	.	PUNCT
cana-1019	287	1	communications	communication	NOUN
cana-1019	287	2	on	on	ADP
cana-1019	287	3	applied	apply	VERB
cana-1019	287	4	nonlinear	nonlinear	ADJ
cana-1019	287	5	analysis	analysis	NOUN
cana-1019	287	6	issn	issn	NOUN
cana-1019	287	7	:	:	PUNCT
cana-1019	287	8	1074	1074	NUM
cana-1019	287	9	-	-	PUNCT
cana-1019	287	10	133x	133x	NUM
cana-1019	287	11	vol	vol	NOUN
cana-1019	287	12	31	31	NUM
cana-1019	287	13	no	no	NOUN
cana-1019	287	14	.	.	PUNCT
cana-1019	288	1	5s	5s	NUM
cana-1019	288	2	(	(	PUNCT
cana-1019	288	3	2024	2024	NUM
cana-1019	288	4	)	)	PUNCT
cana-1019	288	5	252	252	NUM
cana-1019	288	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1019	288	7	figure	figure	NOUN
cana-1019	288	8	5	5	NUM
cana-1019	288	9	:	:	PUNCT
cana-1019	288	10	representation	representation	NOUN
cana-1019	288	11	of	of	ADP
cana-1019	288	12	performance	performance	NOUN
cana-1019	288	13	comparison	comparison	NOUN
cana-1019	288	14	of	of	ADP
cana-1019	288	15	different	different	ADJ
cana-1019	288	16	ml	ml	NOUN
cana-1019	288	17	model	model	NOUN
cana-1019	288	18	with	with	ADP
cana-1019	288	19	a	a	DET
cana-1019	288	20	score	score	NOUN
cana-1019	288	21	of	of	ADP
cana-1019	288	22	0.748201	0.748201	NUM
cana-1019	288	23	,	,	PUNCT
cana-1019	288	24	the	the	DET
cana-1019	288	25	decision	decision	NOUN
cana-1019	288	26	tree	tree	NOUN
cana-1019	288	27	classification	classification	NOUN
cana-1019	288	28	is	be	AUX
cana-1019	288	29	much	much	ADV
cana-1019	288	30	more	more	ADV
cana-1019	288	31	accurate	accurate	ADJ
cana-1019	288	32	than	than	ADP
cana-1019	288	33	before	before	ADV
cana-1019	288	34	.	.	PUNCT
cana-1019	289	1	they	they	PRON
cana-1019	289	2	got	get	VERB
cana-1019	289	3	a	a	DET
cana-1019	289	4	score	score	NOUN
cana-1019	289	5	of	of	ADP
cana-1019	289	6	0.745440	0.745440	NUM
cana-1019	289	7	for	for	ADP
cana-1019	289	8	f1	f1	NOUN
cana-1019	289	9	and	and	CCONJ
cana-1019	289	10	a	a	DET
cana-1019	289	11	score	score	NOUN
cana-1019	289	12	of	of	ADP
cana-1019	289	13	0.762372	0.762372	NUM
cana-1019	289	14	for	for	ADP
cana-1019	289	15	precision	precision	NOUN
cana-1019	289	16	.	.	PUNCT
cana-1019	290	1	this	this	PRON
cana-1019	290	2	shows	show	VERB
cana-1019	290	3	that	that	SCONJ
cana-1019	290	4	decision	decision	NOUN
cana-1019	290	5	trees	tree	NOUN
cana-1019	290	6	are	be	AUX
cana-1019	290	7	good	good	ADJ
cana-1019	290	8	at	at	ADP
cana-1019	290	9	finding	find	VERB
cana-1019	290	10	the	the	DET
cana-1019	290	11	basic	basic	ADJ
cana-1019	290	12	trends	trend	NOUN
cana-1019	290	13	in	in	ADP
cana-1019	290	14	data	datum	NOUN
cana-1019	290	15	,	,	PUNCT
cana-1019	290	16	but	but	CCONJ
cana-1019	290	17	they	they	PRON
cana-1019	290	18	can	can	AUX
cana-1019	290	19	overfit	overfit	VERB
cana-1019	290	20	,	,	PUNCT
cana-1019	290	21	which	which	PRON
cana-1019	290	22	can	can	AUX
cana-1019	290	23	hurt	hurt	VERB
cana-1019	290	24	their	their	PRON
cana-1019	290	25	performance	performance	NOUN
cana-1019	290	26	on	on	ADP
cana-1019	290	27	data	datum	NOUN
cana-1019	290	28	they	they	PRON
cana-1019	290	29	have	have	AUX
cana-1019	290	30	n't	not	PART
cana-1019	290	31	seen	see	VERB
cana-1019	290	32	before	before	ADV
cana-1019	290	33	.	.	PUNCT
cana-1019	291	1	with	with	ADP
cana-1019	291	2	an	an	DET
cana-1019	291	3	accuracy	accuracy	NOUN
cana-1019	291	4	of	of	ADP
cana-1019	291	5	0.856115	0.856115	NUM
cana-1019	291	6	,	,	PUNCT
cana-1019	291	7	random	random	ADJ
cana-1019	291	8	forest	forest	NOUN
cana-1019	291	9	stands	stand	VERB
cana-1019	291	10	out	out	ADP
cana-1019	291	11	.	.	PUNCT
cana-1019	292	1	it	it	PRON
cana-1019	292	2	also	also	ADV
cana-1019	292	3	has	have	VERB
cana-1019	292	4	a	a	DET
cana-1019	292	5	high	high	ADJ
cana-1019	292	6	f1	f1	NOUN
cana-1019	292	7	score	score	NOUN
cana-1019	292	8	of	of	ADP
cana-1019	292	9	0.855389	0.855389	NUM
cana-1019	292	10	,	,	PUNCT
cana-1019	292	11	an	an	DET
cana-1019	292	12	accuracy	accuracy	NOUN
cana-1019	292	13	of	of	ADP
cana-1019	292	14	0.861950	0.861950	NUM
cana-1019	292	15	,	,	PUNCT
cana-1019	292	16	and	and	CCONJ
cana-1019	292	17	a	a	DET
cana-1019	292	18	memory	memory	NOUN
cana-1019	292	19	of	of	ADP
cana-1019	292	20	0.856115	0.856115	NUM
cana-1019	292	21	.	.	PUNCT
cana-1019	293	1	as	as	SCONJ
cana-1019	293	2	you	you	PRON
cana-1019	293	3	can	can	AUX
cana-1019	293	4	see	see	VERB
cana-1019	293	5	,	,	PUNCT
cana-1019	293	6	these	these	DET
cana-1019	293	7	results	result	NOUN
cana-1019	293	8	show	show	VERB
cana-1019	293	9	that	that	SCONJ
cana-1019	293	10	ensemble	ensemble	ADJ
cana-1019	293	11	methods	method	NOUN
cana-1019	293	12	,	,	PUNCT
cana-1019	293	13	especially	especially	ADV
cana-1019	293	14	random	random	ADJ
cana-1019	293	15	forest	forest	NOUN
cana-1019	293	16	,	,	PUNCT
cana-1019	293	17	can	can	AUX
cana-1019	293	18	handle	handle	VERB
cana-1019	293	19	difficult	difficult	ADJ
cana-1019	293	20	mood	mood	NOUN
cana-1019	293	21	classification	classification	NOUN
cana-1019	293	22	jobs	job	NOUN
cana-1019	293	23	with	with	ADP
cana-1019	293	24	ease	ease	NOUN
cana-1019	293	25	,	,	PUNCT
cana-1019	293	26	comparison	comparison	NOUN
cana-1019	293	27	of	of	ADP
cana-1019	293	28	different	different	ADJ
cana-1019	293	29	model	model	NOUN
cana-1019	293	30	shown	show	VERB
cana-1019	293	31	in	in	ADP
cana-1019	293	32	figure	figure	NOUN
cana-1019	293	33	5	5	NUM
cana-1019	293	34	.	.	PUNCT
cana-1019	293	35	with	with	ADP
cana-1019	293	36	an	an	DET
cana-1019	293	37	accuracy	accuracy	NOUN
cana-1019	293	38	of	of	ADP
cana-1019	293	39	0.827338	0.827338	NUM
cana-1019	293	40	,	,	PUNCT
cana-1019	293	41	the	the	DET
cana-1019	293	42	xgboost	xgboost	PROPN
cana-1019	293	43	(	(	PUNCT
cana-1019	293	44	xgb	xgb	NOUN
cana-1019	293	45	)	)	PUNCT
cana-1019	293	46	classifier	classifier	NOUN
cana-1019	293	47	also	also	ADV
cana-1019	293	48	does	do	VERB
cana-1019	293	49	very	very	ADV
cana-1019	293	50	well	well	ADV
cana-1019	293	51	.	.	PUNCT
cana-1019	294	1	it	it	PRON
cana-1019	294	2	's	be	AUX
cana-1019	294	3	accurate	accurate	ADJ
cana-1019	294	4	0.827626	0.827626	NUM
cana-1019	294	5	times	time	NOUN
cana-1019	294	6	,	,	PUNCT
cana-1019	294	7	accurate	accurate	ADJ
cana-1019	294	8	0.827338	0.827338	NUM
cana-1019	294	9	times	time	NOUN
cana-1019	294	10	,	,	PUNCT
cana-1019	294	11	and	and	CCONJ
cana-1019	294	12	has	have	VERB
cana-1019	294	13	an	an	DET
cana-1019	294	14	f1	f1	ADJ
cana-1019	294	15	score	score	NOUN
cana-1019	294	16	of	of	ADP
cana-1019	294	17	0.826412	0.826412	NUM
cana-1019	294	18	.	.	PUNCT
cana-1019	295	1	the	the	DET
cana-1019	295	2	gradient	gradient	NOUN
cana-1019	295	3	boosting	boost	VERB
cana-1019	295	4	method	method	NOUN
cana-1019	295	5	in	in	ADP
cana-1019	295	6	xgboost	xgboost	ADV
cana-1019	295	7	makes	make	VERB
cana-1019	295	8	it	it	PRON
cana-1019	295	9	better	well	ADJ
cana-1019	295	10	at	at	ADP
cana-1019	295	11	making	make	VERB
cana-1019	295	12	predictions	prediction	NOUN
cana-1019	295	13	,	,	PUNCT
cana-1019	295	14	which	which	PRON
cana-1019	295	15	makes	make	VERB
cana-1019	295	16	it	it	PRON
cana-1019	295	17	very	very	ADV
cana-1019	295	18	good	good	ADJ
cana-1019	295	19	at	at	ADP
cana-1019	295	20	this	this	DET
cana-1019	295	21	classification	classification	NOUN
cana-1019	295	22	problem	problem	NOUN
cana-1019	295	23	.	.	PUNCT
cana-1019	296	1	with	with	ADP
cana-1019	296	2	a	a	DET
cana-1019	296	3	level	level	NOUN
cana-1019	296	4	of	of	ADP
cana-1019	296	5	accuracy	accuracy	NOUN
cana-1019	296	6	of	of	ADP
cana-1019	296	7	0.460432	0.460432	NUM
cana-1019	296	8	,	,	PUNCT
cana-1019	296	9	svm	svm	PROPN
cana-1019	296	10	linear	linear	PROPN
cana-1019	296	11	does	do	VERB
cana-1019	296	12	pretty	pretty	ADV
cana-1019	296	13	well	well	ADV
cana-1019	296	14	.	.	PUNCT
cana-1019	297	1	the	the	DET
cana-1019	297	2	accuracy	accuracy	NOUN
cana-1019	297	3	is	be	AUX
cana-1019	297	4	higher	high	ADJ
cana-1019	297	5	at	at	ADP
cana-1019	297	6	0.628598	0.628598	NUM
cana-1019	297	7	,	,	PUNCT
cana-1019	297	8	but	but	CCONJ
cana-1019	297	9	the	the	DET
cana-1019	297	10	recall	recall	NOUN
cana-1019	297	11	is	be	AUX
cana-1019	297	12	lower	low	ADJ
cana-1019	297	13	at	at	ADP
cana-1019	297	14	0.460432	0.460432	NUM
cana-1019	297	15	.	.	PUNCT
cana-1019	298	1	this	this	PRON
cana-1019	298	2	gives	give	VERB
cana-1019	298	3	it	it	PRON
cana-1019	298	4	an	an	DET
cana-1019	298	5	f1	f1	ADJ
cana-1019	298	6	score	score	NOUN
cana-1019	298	7	of	of	ADP
cana-1019	298	8	0.414862	0.414862	NUM
cana-1019	298	9	.	.	PUNCT
cana-1019	299	1	this	this	DET
cana-1019	299	2	difference	difference	NOUN
cana-1019	299	3	shows	show	VERB
cana-1019	299	4	that	that	SCONJ
cana-1019	299	5	svm	svm	PROPN
cana-1019	299	6	can	can	AUX
cana-1019	299	7	make	make	VERB
cana-1019	299	8	good	good	ADJ
cana-1019	299	9	guesses	guess	NOUN
cana-1019	299	10	,	,	PUNCT
cana-1019	299	11	but	but	CCONJ
cana-1019	299	12	it	it	PRON
cana-1019	299	13	might	might	AUX
cana-1019	299	14	miss	miss	VERB
cana-1019	299	15	a	a	DET
cana-1019	299	16	lot	lot	NOUN
cana-1019	299	17	of	of	ADP
cana-1019	299	18	good	good	ADJ
cana-1019	299	19	examples	example	NOUN
cana-1019	299	20	,	,	PUNCT
cana-1019	299	21	which	which	PRON
cana-1019	299	22	would	would	AUX
cana-1019	299	23	lower	lower	VERB
cana-1019	299	24	its	its	PRON
cana-1019	299	25	total	total	ADJ
cana-1019	299	26	recall	recall	NOUN
cana-1019	299	27	.	.	PUNCT
cana-1019	300	1	k	k	ADJ
cana-1019	300	2	-	-	PUNCT
cana-1019	300	3	nearest	near	ADJ
cana-1019	300	4	neighbors	neighbor	NOUN
cana-1019	300	5	(	(	PUNCT
cana-1019	300	6	knn	knn	PROPN
cana-1019	300	7	)	)	PUNCT
cana-1019	300	8	,	,	PUNCT
cana-1019	300	9	which	which	PRON
cana-1019	300	10	has	have	VERB
cana-1019	300	11	an	an	DET
cana-1019	300	12	accuracy	accuracy	NOUN
cana-1019	300	13	of	of	ADP
cana-1019	300	14	0.309353	0.309353	NUM
cana-1019	300	15	,	,	PUNCT
cana-1019	300	16	is	be	AUX
cana-1019	300	17	one	one	NUM
cana-1019	300	18	of	of	ADP
cana-1019	300	19	the	the	DET
cana-1019	300	20	worst	bad	ADJ
cana-1019	300	21	.	.	PUNCT
cana-1019	301	1	that	that	DET
cana-1019	301	2	number	number	NOUN
cana-1019	301	3	is	be	AUX
cana-1019	301	4	0.292099	0.292099	NUM
cana-1019	301	5	,	,	PUNCT
cana-1019	301	6	the	the	DET
cana-1019	301	7	memory	memory	NOUN
cana-1019	301	8	number	number	NOUN
cana-1019	301	9	is	be	AUX
cana-1019	301	10	0.309353	0.309353	NUM
cana-1019	301	11	,	,	PUNCT
cana-1019	301	12	and	and	CCONJ
cana-1019	301	13	the	the	DET
cana-1019	301	14	f1	f1	NOUN
cana-1019	301	15	score	score	NOUN
cana-1019	301	16	number	number	NOUN
cana-1019	301	17	is	be	AUX
cana-1019	301	18	0.294645	0.294645	NUM
cana-1019	301	19	.	.	PUNCT
cana-1019	302	1	these	these	DET
cana-1019	302	2	measures	measure	NOUN
cana-1019	302	3	show	show	VERB
cana-1019	302	4	that	that	SCONJ
cana-1019	302	5	knn	knn	PROPN
cana-1019	302	6	has	have	VERB
cana-1019	302	7	trouble	trouble	NOUN
cana-1019	302	8	with	with	ADP
cana-1019	302	9	the	the	DET
cana-1019	302	10	mood	mood	NOUN
cana-1019	302	11	analysis	analysis	NOUN
cana-1019	302	12	task	task	NOUN
cana-1019	302	13	.	.	PUNCT
cana-1019	303	1	this	this	PRON
cana-1019	303	2	might	might	AUX
cana-1019	303	3	be	be	AUX
cana-1019	303	4	because	because	SCONJ
cana-1019	303	5	it	it	PRON
cana-1019	303	6	is	be	AUX
cana-1019	303	7	sensitive	sensitive	ADJ
cana-1019	303	8	to	to	ADP
cana-1019	303	9	the	the	DET
cana-1019	303	10	curse	curse	NOUN
cana-1019	303	11	of	of	ADP
cana-1019	303	12	dimensionality	dimensionality	NOUN
cana-1019	303	13	and	and	CCONJ
cana-1019	303	14	needs	need	VERB
cana-1019	303	15	to	to	PART
cana-1019	303	16	choose	choose	VERB
cana-1019	303	17	the	the	DET
cana-1019	303	18	right	right	ADJ
cana-1019	303	19	distance	distance	NOUN
cana-1019	303	20	metric	metric	NOUN
cana-1019	303	21	.	.	PUNCT
cana-1019	304	1	communications	communication	NOUN
cana-1019	304	2	on	on	ADP
cana-1019	304	3	applied	apply	VERB
cana-1019	304	4	nonlinear	nonlinear	ADJ
cana-1019	304	5	analysis	analysis	NOUN
cana-1019	304	6	issn	issn	NOUN
cana-1019	304	7	:	:	PUNCT
cana-1019	304	8	1074	1074	NUM
cana-1019	304	9	-	-	PUNCT
cana-1019	304	10	133x	133x	NUM
cana-1019	304	11	vol	vol	NOUN
cana-1019	304	12	31	31	NUM
cana-1019	304	13	no	no	NOUN
cana-1019	304	14	.	.	PUNCT
cana-1019	305	1	5s	5s	NUM
cana-1019	305	2	(	(	PUNCT
cana-1019	305	3	2024	2024	NUM
cana-1019	305	4	)	)	PUNCT
cana-1019	305	5	253	253	NUM
cana-1019	305	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1019	305	7	figure	figure	NOUN
cana-1019	305	8	6	6	NUM
cana-1019	305	9	:	:	PUNCT
cana-1019	305	10	comparison	comparison	NOUN
cana-1019	305	11	accuracy	accuracy	NOUN
cana-1019	305	12	of	of	ADP
cana-1019	305	13	different	different	ADJ
cana-1019	305	14	model	model	NOUN
cana-1019	305	15	figure	figure	NOUN
cana-1019	305	16	6	6	NUM
cana-1019	305	17	shows	show	VERB
cana-1019	305	18	a	a	DET
cana-1019	305	19	comparison	comparison	NOUN
cana-1019	305	20	of	of	ADP
cana-1019	305	21	how	how	SCONJ
cana-1019	305	22	accurate	accurate	ADJ
cana-1019	305	23	different	different	ADJ
cana-1019	305	24	models	model	NOUN
cana-1019	305	25	are	be	AUX
cana-1019	305	26	,	,	PUNCT
cana-1019	305	27	with	with	ADP
cana-1019	305	28	random	random	ADJ
cana-1019	305	29	forest	forest	NOUN
cana-1019	305	30	and	and	CCONJ
cana-1019	305	31	xgb	xgb	NOUN
cana-1019	305	32	classifier	classifier	NOUN
cana-1019	305	33	coming	come	VERB
cana-1019	305	34	out	out	ADP
cana-1019	305	35	on	on	ADP
cana-1019	305	36	top	top	NOUN
cana-1019	305	37	.	.	PUNCT
cana-1019	306	1	the	the	DET
cana-1019	306	2	confusion	confusion	NOUN
cana-1019	306	3	matrix	matrix	NOUN
cana-1019	306	4	,	,	PUNCT
cana-1019	306	5	shown	show	VERB
cana-1019	306	6	in	in	ADP
cana-1019	306	7	figure	figure	NOUN
cana-1019	306	8	7	7	NUM
cana-1019	306	9	,	,	PUNCT
cana-1019	306	10	gives	give	VERB
cana-1019	306	11	you	you	PRON
cana-1019	306	12	a	a	DET
cana-1019	306	13	lot	lot	NOUN
cana-1019	306	14	of	of	ADP
cana-1019	306	15	information	information	NOUN
cana-1019	306	16	about	about	ADP
cana-1019	306	17	how	how	SCONJ
cana-1019	306	18	well	well	ADV
cana-1019	306	19	each	each	DET
cana-1019	306	20	program	program	NOUN
cana-1019	306	21	does	do	AUX
cana-1019	306	22	at	at	ADP
cana-1019	306	23	classifying	classify	VERB
cana-1019	306	24	.	.	PUNCT
cana-1019	307	1	(	(	PUNCT
cana-1019	307	2	a	a	X
cana-1019	307	3	)	)	PUNCT
cana-1019	307	4	logistic	logistic	ADJ
cana-1019	307	5	regression	regression	NOUN
cana-1019	307	6	(	(	PUNCT
cana-1019	307	7	b	b	NOUN
cana-1019	307	8	)	)	PUNCT
cana-1019	307	9	sgd	sgd	PROPN
cana-1019	307	10	classifier	classifier	NOUN
cana-1019	307	11	communications	communication	NOUN
cana-1019	307	12	on	on	ADP
cana-1019	307	13	applied	apply	VERB
cana-1019	307	14	nonlinear	nonlinear	ADJ
cana-1019	307	15	analysis	analysis	NOUN
cana-1019	307	16	issn	issn	NOUN
cana-1019	307	17	:	:	PUNCT
cana-1019	307	18	1074	1074	NUM
cana-1019	307	19	-	-	PUNCT
cana-1019	307	20	133x	133x	NUM
cana-1019	307	21	vol	vol	NOUN
cana-1019	307	22	31	31	NUM
cana-1019	307	23	no	no	NOUN
cana-1019	307	24	.	.	PUNCT
cana-1019	308	1	5s	5s	NUM
cana-1019	308	2	(	(	PUNCT
cana-1019	308	3	2024	2024	NUM
cana-1019	308	4	)	)	PUNCT
cana-1019	308	5	254	254	NUM
cana-1019	308	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1019	308	7	(	(	PUNCT
cana-1019	308	8	c	c	NOUN
cana-1019	308	9	)	)	PUNCT
cana-1019	308	10	gaussian	gaussian	ADJ
cana-1019	308	11	naïve	naïve	ADJ
cana-1019	308	12	bayes	bayes	NOUN
cana-1019	308	13	(	(	PUNCT
cana-1019	308	14	d	d	X
cana-1019	308	15	)	)	PUNCT
cana-1019	308	16	decision	decision	NOUN
cana-1019	308	17	tree	tree	NOUN
cana-1019	308	18	(	(	PUNCT
cana-1019	308	19	e	e	NOUN
cana-1019	308	20	)	)	PUNCT
cana-1019	308	21	random	random	ADJ
cana-1019	308	22	forest	forest	NOUN
cana-1019	309	1	(	(	PUNCT
cana-1019	309	2	f	f	X
cana-1019	309	3	)	)	PUNCT
cana-1019	309	4	xgb	xgb	PROPN
cana-1019	309	5	classifier	classifier	NOUN
cana-1019	309	6	(	(	PUNCT
cana-1019	309	7	g	g	NOUN
cana-1019	309	8	)	)	PUNCT
cana-1019	309	9	svm	svm	ADJ
cana-1019	309	10	classifier	classifier	NOUN
cana-1019	309	11	(	(	PUNCT
cana-1019	309	12	h	h	NOUN
cana-1019	309	13	)	)	PUNCT
cana-1019	309	14	knn	knn	PROPN
cana-1019	309	15	classifier	classifier	PROPN
cana-1019	309	16	figure	figure	VERB
cana-1019	309	17	7	7	NUM
cana-1019	309	18	:	:	PUNCT
cana-1019	309	19	confusion	confusion	NOUN
cana-1019	309	20	matrix	matrix	NOUN
cana-1019	309	21	of	of	ADP
cana-1019	309	22	machine	machine	NOUN
cana-1019	309	23	learning	learn	VERB
cana-1019	309	24	algorithms	algorithms	NOUN
cana-1019	309	25	communications	communication	NOUN
cana-1019	309	26	on	on	ADP
cana-1019	309	27	applied	apply	VERB
cana-1019	309	28	nonlinear	nonlinear	ADJ
cana-1019	309	29	analysis	analysis	NOUN
cana-1019	309	30	issn	issn	NOUN
cana-1019	309	31	:	:	PUNCT
cana-1019	309	32	1074	1074	NUM
cana-1019	309	33	-	-	PUNCT
cana-1019	309	34	133x	133x	NUM
cana-1019	309	35	vol	vol	NOUN
cana-1019	309	36	31	31	NUM
cana-1019	309	37	no	no	NOUN
cana-1019	309	38	.	.	PUNCT
cana-1019	310	1	5s	5s	NUM
cana-1019	310	2	(	(	PUNCT
cana-1019	310	3	2024	2024	NUM
cana-1019	310	4	)	)	PUNCT
cana-1019	310	5	255	255	NUM
cana-1019	310	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1019	310	7	b.	b.	NOUN
cana-1019	310	8	machine	machine	NOUN
cana-1019	310	9	learning	learn	VERB
cana-1019	310	10	algorithms	algorithm	NOUN
cana-1019	310	11	with	with	ADP
cana-1019	310	12	fine	fine	ADJ
cana-1019	310	13	-	-	PUNCT
cana-1019	310	14	tune	tune	NOUN
cana-1019	310	15	parameters	parameter	NOUN
cana-1019	310	16	random	random	ADJ
cana-1019	310	17	forest	forest	NOUN
cana-1019	310	18	,	,	PUNCT
cana-1019	310	19	xgb	xgb	PROPN
cana-1019	310	20	classifier	classifier	NOUN
cana-1019	310	21	,	,	PUNCT
cana-1019	310	22	and	and	CCONJ
cana-1019	310	23	svm	svm	ADJ
cana-1019	310	24	linear	linear	PROPN
cana-1019	310	25	are	be	AUX
cana-1019	310	26	three	three	NUM
cana-1019	310	27	machine	machine	NOUN
cana-1019	310	28	learning	learning	NOUN
cana-1019	310	29	methods	method	NOUN
cana-1019	310	30	that	that	PRON
cana-1019	310	31	were	be	AUX
cana-1019	310	32	tested	test	VERB
cana-1019	310	33	and	and	CCONJ
cana-1019	310	34	their	their	PRON
cana-1019	310	35	success	success	NOUN
cana-1019	310	36	was	be	AUX
cana-1019	310	37	measured	measure	VERB
cana-1019	310	38	in	in	ADP
cana-1019	310	39	table	table	NOUN
cana-1019	310	40	4	4	NUM
cana-1019	310	41	.	.	NOUN
cana-1019	310	42	accuracy	accuracy	NOUN
cana-1019	310	43	,	,	PUNCT
cana-1019	310	44	precision	precision	NOUN
cana-1019	310	45	,	,	PUNCT
cana-1019	310	46	recall	recall	NOUN
cana-1019	310	47	,	,	PUNCT
cana-1019	310	48	and	and	CCONJ
cana-1019	310	49	f1	f1	NOUN
cana-1019	310	50	score	score	NOUN
cana-1019	310	51	are	be	AUX
cana-1019	310	52	some	some	PRON
cana-1019	310	53	of	of	ADP
cana-1019	310	54	the	the	DET
cana-1019	310	55	measures	measure	NOUN
cana-1019	310	56	that	that	PRON
cana-1019	310	57	give	give	VERB
cana-1019	310	58	a	a	DET
cana-1019	310	59	full	full	ADJ
cana-1019	310	60	picture	picture	NOUN
cana-1019	310	61	of	of	ADP
cana-1019	310	62	how	how	SCONJ
cana-1019	310	63	well	well	ADV
cana-1019	310	64	each	each	DET
cana-1019	310	65	model	model	NOUN
cana-1019	310	66	did	do	AUX
cana-1019	310	67	on	on	ADP
cana-1019	310	68	the	the	DET
cana-1019	310	69	mood	mood	NOUN
cana-1019	310	70	analysis	analysis	NOUN
cana-1019	310	71	dataset	dataset	VERB
cana-1019	310	72	.	.	PUNCT
cana-1019	311	1	table	table	NOUN
cana-1019	311	2	4	4	NUM
cana-1019	311	3	:	:	PUNCT
cana-1019	311	4	result	result	NOUN
cana-1019	311	5	for	for	ADP
cana-1019	311	6	machine	machine	NOUN
cana-1019	311	7	learning	learn	VERB
cana-1019	311	8	algorithms	algorithm	NOUN
cana-1019	311	9	with	with	ADP
cana-1019	311	10	fine	fine	ADJ
cana-1019	311	11	-	-	PUNCT
cana-1019	311	12	tune	tune	NOUN
cana-1019	311	13	parameters	parameter	NOUN
cana-1019	311	14	algorithm	algorithm	NOUN
cana-1019	311	15	accuracy	accuracy	NOUN
cana-1019	311	16	precision	precision	NOUN
cana-1019	311	17	recall	recall	NOUN
cana-1019	311	18	f1	f1	PROPN
cana-1019	311	19	score	score	NOUN
cana-1019	311	20	random	random	ADJ
cana-1019	311	21	forest	forest	NOUN
cana-1019	311	22	0.8619	0.8619	NUM
cana-1019	311	23	0.86195	0.86195	NUM
cana-1019	311	24	0.86	0.86	NUM
cana-1019	311	25	0.861	0.861	NUM
cana-1019	311	26	xgb	xgb	NOUN
cana-1019	311	27	classifier	classifier	VERB
cana-1019	311	28	0.863	0.863	NUM
cana-1019	311	29	0.86	0.86	NUM
cana-1019	311	30	0.863	0.863	NUM
cana-1019	311	31	0.86	0.86	NUM
cana-1019	311	32	svm	svm	NOUN
cana-1019	311	33	linear	linear	PROPN
cana-1019	311	34	0.8545	0.8545	NUM
cana-1019	311	35	0.869	0.869	NUM
cana-1019	311	36	0.460432	0.460432	NUM
cana-1019	311	37	0.8356	0.8356	NUM
cana-1019	311	38	an	an	DET
cana-1019	311	39	accuracy	accuracy	NOUN
cana-1019	311	40	of	of	ADP
cana-1019	311	41	0.8619	0.8619	NUM
cana-1019	311	42	shows	show	VERB
cana-1019	311	43	that	that	SCONJ
cana-1019	311	44	the	the	DET
cana-1019	311	45	random	random	ADJ
cana-1019	311	46	forest	forest	NOUN
cana-1019	311	47	algorithm	algorithm	NOUN
cana-1019	311	48	works	work	VERB
cana-1019	311	49	well	well	ADV
cana-1019	311	50	even	even	ADV
cana-1019	311	51	when	when	SCONJ
cana-1019	311	52	things	thing	NOUN
cana-1019	311	53	go	go	VERB
cana-1019	311	54	wrong	wrong	ADJ
cana-1019	311	55	.	.	PUNCT
cana-1019	312	1	the	the	DET
cana-1019	312	2	f1	f1	PROPN
cana-1019	312	3	score	score	NOUN
cana-1019	312	4	is	be	AUX
cana-1019	312	5	0.861	0.861	NUM
cana-1019	312	6	,	,	PUNCT
cana-1019	312	7	which	which	PRON
cana-1019	312	8	means	mean	VERB
cana-1019	312	9	that	that	SCONJ
cana-1019	312	10	both	both	DET
cana-1019	312	11	precision	precision	NOUN
cana-1019	312	12	and	and	CCONJ
cana-1019	312	13	memory	memory	NOUN
cana-1019	312	14	are	be	AUX
cana-1019	312	15	good	good	ADJ
cana-1019	312	16	,	,	PUNCT
cana-1019	312	17	at	at	ADP
cana-1019	312	18	0.86195	0.86195	NUM
cana-1019	312	19	and	and	CCONJ
cana-1019	312	20	0.86	0.86	NUM
cana-1019	312	21	,	,	PUNCT
cana-1019	312	22	respectively	respectively	ADV
cana-1019	312	23	.	.	PUNCT
cana-1019	313	1	the	the	DET
cana-1019	313	2	model	model	NOUN
cana-1019	313	3	has	have	VERB
cana-1019	313	4	a	a	DET
cana-1019	313	5	good	good	ADJ
cana-1019	313	6	mix	mix	NOUN
cana-1019	313	7	between	between	ADP
cana-1019	313	8	accuracy	accuracy	NOUN
cana-1019	313	9	(	(	PUNCT
cana-1019	313	10	the	the	DET
cana-1019	313	11	number	number	NOUN
cana-1019	313	12	of	of	ADP
cana-1019	313	13	correctly	correctly	ADV
cana-1019	313	14	predicted	predict	VERB
cana-1019	313	15	positive	positive	ADJ
cana-1019	313	16	observations	observation	NOUN
cana-1019	313	17	divided	divide	VERB
cana-1019	313	18	by	by	ADP
cana-1019	313	19	the	the	DET
cana-1019	313	20	total	total	ADJ
cana-1019	313	21	number	number	NOUN
cana-1019	313	22	of	of	ADP
cana-1019	313	23	correctly	correctly	ADV
cana-1019	313	24	predicted	predict	VERB
cana-1019	313	25	positive	positive	ADJ
cana-1019	313	26	observations	observation	NOUN
cana-1019	313	27	)	)	PUNCT
cana-1019	313	28	and	and	CCONJ
cana-1019	313	29	recall	recall	NOUN
cana-1019	313	30	(	(	PUNCT
cana-1019	313	31	the	the	DET
cana-1019	313	32	number	number	NOUN
cana-1019	313	33	of	of	ADP
cana-1019	313	34	correctly	correctly	ADV
cana-1019	313	35	predicted	predict	VERB
cana-1019	313	36	positive	positive	ADJ
cana-1019	313	37	observations	observation	NOUN
cana-1019	313	38	divided	divide	VERB
cana-1019	313	39	by	by	ADP
cana-1019	313	40	all	all	DET
cana-1019	313	41	observations	observation	NOUN
cana-1019	313	42	in	in	ADP
cana-1019	313	43	the	the	DET
cana-1019	313	44	real	real	ADJ
cana-1019	313	45	class	class	NOUN
cana-1019	313	46	)	)	PUNCT
cana-1019	313	47	.	.	PUNCT
cana-1019	314	1	this	this	DET
cana-1019	314	2	model	model	NOUN
cana-1019	314	3	is	be	AUX
cana-1019	314	4	very	very	ADV
cana-1019	314	5	good	good	ADJ
cana-1019	314	6	at	at	ADP
cana-1019	314	7	classifying	classify	VERB
cana-1019	314	8	things	thing	NOUN
cana-1019	314	9	,	,	PUNCT
cana-1019	314	10	as	as	SCONJ
cana-1019	314	11	shown	show	VERB
cana-1019	314	12	by	by	ADP
cana-1019	314	13	its	its	PRON
cana-1019	314	14	high	high	ADJ
cana-1019	314	15	f1	f1	NOUN
cana-1019	314	16	score	score	NOUN
cana-1019	314	17	,	,	PUNCT
cana-1019	314	18	the	the	DET
cana-1019	314	19	figure	figure	NOUN
cana-1019	314	20	8	8	NUM
cana-1019	314	21	illustrate	illustrate	VERB
cana-1019	314	22	the	the	DET
cana-1019	314	23	comparison	comparison	NOUN
cana-1019	314	24	graphs	graph	NOUN
cana-1019	314	25	of	of	ADP
cana-1019	314	26	fine	fine	ADJ
cana-1019	314	27	-	-	PUNCT
cana-1019	314	28	tune	tune	NOUN
cana-1019	314	29	machine	machine	NOUN
cana-1019	314	30	learning	learn	VERB
cana-1019	314	31	algorithms	algorithm	NOUN
cana-1019	314	32	.	.	PUNCT
cana-1019	315	1	figure	figure	VERB
cana-1019	315	2	8	8	NUM
cana-1019	315	3	:	:	PUNCT
cana-1019	315	4	performance	performance	NOUN
cana-1019	315	5	comparison	comparison	NOUN
cana-1019	315	6	graphs	graph	NOUN
cana-1019	315	7	of	of	ADP
cana-1019	315	8	fine	fine	ADJ
cana-1019	315	9	-	-	PUNCT
cana-1019	315	10	tune	tune	NOUN
cana-1019	315	11	machine	machine	NOUN
cana-1019	315	12	learning	learn	VERB
cana-1019	315	13	algorithms	algorithm	NOUN
cana-1019	315	14	this	this	PRON
cana-1019	315	15	makes	make	VERB
cana-1019	315	16	random	random	ADJ
cana-1019	315	17	forest	forest	NOUN
cana-1019	315	18	a	a	DET
cana-1019	315	19	good	good	ADJ
cana-1019	315	20	choice	choice	NOUN
cana-1019	315	21	for	for	ADP
cana-1019	315	22	analyzing	analyze	VERB
cana-1019	315	23	mood	mood	NOUN
cana-1019	315	24	.	.	PUNCT
cana-1019	316	1	at	at	ADP
cana-1019	316	2	0.863	0.863	NUM
cana-1019	316	3	,	,	PUNCT
cana-1019	316	4	the	the	DET
cana-1019	316	5	xgboost	xgboost	X
cana-1019	316	6	(	(	PUNCT
cana-1019	316	7	xgb	xgb	NOUN
cana-1019	316	8	)	)	PUNCT
cana-1019	316	9	classifier	classifier	NOUN
cana-1019	316	10	is	be	AUX
cana-1019	316	11	the	the	DET
cana-1019	316	12	most	most	ADV
cana-1019	316	13	accurate	accurate	ADJ
cana-1019	316	14	of	of	ADP
cana-1019	316	15	the	the	DET
cana-1019	316	16	three	three	NUM
cana-1019	316	17	models	model	NOUN
cana-1019	316	18	.	.	PUNCT
cana-1019	317	1	with	with	ADP
cana-1019	317	2	an	an	DET
cana-1019	317	3	f1	f1	ADJ
cana-1019	317	4	score	score	NOUN
cana-1019	317	5	of	of	ADP
cana-1019	317	6	0.86	0.86	NUM
cana-1019	317	7	,	,	PUNCT
cana-1019	317	8	this	this	DET
cana-1019	317	9	method	method	NOUN
cana-1019	317	10	also	also	ADV
cana-1019	317	11	strikes	strike	VERB
cana-1019	317	12	a	a	DET
cana-1019	317	13	good	good	ADJ
cana-1019	317	14	mix	mix	NOUN
cana-1019	317	15	between	between	ADP
cana-1019	317	16	accuracy	accuracy	NOUN
cana-1019	317	17	(	(	PUNCT
cana-1019	317	18	0.86	0.86	NUM
cana-1019	317	19	)	)	PUNCT
cana-1019	317	20	and	and	CCONJ
cana-1019	317	21	memory	memory	NOUN
cana-1019	317	22	(	(	PUNCT
cana-1019	317	23	0.863	0.863	NUM
cana-1019	317	24	)	)	PUNCT
cana-1019	317	25	.	.	PUNCT
cana-1019	318	1	its	its	PRON
cana-1019	318	2	better	well	ADJ
cana-1019	318	3	success	success	NOUN
cana-1019	318	4	is	be	AUX
cana-1019	318	5	due	due	ADJ
cana-1019	318	6	in	in	ADP
cana-1019	318	7	part	part	NOUN
cana-1019	318	8	to	to	ADP
cana-1019	318	9	its	its	PRON
cana-1019	318	10	ability	ability	NOUN
cana-1019	318	11	to	to	PART
cana-1019	318	12	handle	handle	VERB
cana-1019	318	13	big	big	ADJ
cana-1019	318	14	datasets	dataset	NOUN
cana-1019	318	15	and	and	CCONJ
cana-1019	318	16	its	its	PRON
cana-1019	318	17	resistance	resistance	NOUN
cana-1019	318	18	to	to	ADP
cana-1019	318	19	overfitting	overfitte	VERB
cana-1019	318	20	.	.	PUNCT
cana-1019	319	1	because	because	SCONJ
cana-1019	319	2	recall	recall	NOUN
cana-1019	319	3	is	be	AUX
cana-1019	319	4	a	a	DET
cana-1019	319	5	little	little	ADV
cana-1019	319	6	higher	high	ADJ
cana-1019	319	7	than	than	SCONJ
cana-1019	319	8	communications	communication	NOUN
cana-1019	319	9	on	on	ADP
cana-1019	319	10	applied	apply	VERB
cana-1019	319	11	nonlinear	nonlinear	ADJ
cana-1019	319	12	analysis	analysis	NOUN
cana-1019	319	13	issn	issn	NOUN
cana-1019	319	14	:	:	PUNCT
cana-1019	319	15	1074	1074	NUM
cana-1019	319	16	-	-	PUNCT
cana-1019	319	17	133x	133x	NUM
cana-1019	319	18	vol	vol	NOUN
cana-1019	319	19	31	31	NUM
cana-1019	319	20	no	no	NOUN
cana-1019	319	21	.	.	PUNCT
cana-1019	320	1	5s	5s	NUM
cana-1019	320	2	(	(	PUNCT
cana-1019	320	3	2024	2024	NUM
cana-1019	320	4	)	)	PUNCT
cana-1019	320	5	256	256	NUM
cana-1019	320	6	https://internationalpubls.com	https://internationalpubls.com	NUM
cana-1019	320	7	accuracy	accuracy	NOUN
cana-1019	320	8	,	,	PUNCT
cana-1019	320	9	it	it	PRON
cana-1019	320	10	means	mean	VERB
cana-1019	320	11	that	that	SCONJ
cana-1019	320	12	xgboost	xgboost	PROPN
cana-1019	320	13	is	be	AUX
cana-1019	320	14	a	a	DET
cana-1019	320	15	little	little	ADJ
cana-1019	320	16	better	well	ADJ
cana-1019	320	17	at	at	ADP
cana-1019	320	18	finding	find	VERB
cana-1019	320	19	all	all	DET
cana-1019	320	20	positive	positive	ADJ
cana-1019	320	21	instances	instance	NOUN
cana-1019	320	22	.	.	PUNCT
cana-1019	321	1	this	this	PRON
cana-1019	321	2	is	be	AUX
cana-1019	321	3	important	important	ADJ
cana-1019	321	4	in	in	ADP
cana-1019	321	5	situations	situation	NOUN
cana-1019	321	6	where	where	SCONJ
cana-1019	321	7	losing	lose	VERB
cana-1019	321	8	a	a	DET
cana-1019	321	9	positive	positive	ADJ
cana-1019	321	10	instance	instance	NOUN
cana-1019	321	11	(	(	PUNCT
cana-1019	321	12	like	like	ADP
cana-1019	321	13	a	a	DET
cana-1019	321	14	mood	mood	NOUN
cana-1019	321	15	)	)	PUNCT
cana-1019	321	16	is	be	AUX
cana-1019	321	17	more	more	ADV
cana-1019	321	18	expensive	expensive	ADJ
cana-1019	321	19	than	than	ADP
cana-1019	321	20	wrongly	wrongly	ADV
cana-1019	321	21	naming	name	VERB
cana-1019	321	22	a	a	DET
cana-1019	321	23	negative	negative	ADJ
cana-1019	321	24	instance	instance	NOUN
cana-1019	321	25	as	as	ADP
cana-1019	321	26	positive	positive	ADJ
cana-1019	321	27	.	.	PUNCT
cana-1019	322	1	figure	figure	NOUN
cana-1019	322	2	9	9	NUM
cana-1019	322	3	shows	show	VERB
cana-1019	322	4	confusion	confusion	NOUN
cana-1019	322	5	matrices	matrix	NOUN
cana-1019	322	6	show	show	VERB
cana-1019	322	7	fine	fine	ADV
cana-1019	322	8	-	-	PUNCT
cana-1019	322	9	tuned	tune	VERB
cana-1019	322	10	xgboost	xgboost	ADV
cana-1019	322	11	and	and	CCONJ
cana-1019	322	12	random	random	ADJ
cana-1019	322	13	forest	forest	NOUN
cana-1019	322	14	models	model	NOUN
cana-1019	322	15	excelling	excel	VERB
cana-1019	322	16	in	in	ADP
cana-1019	322	17	mood	mood	NOUN
cana-1019	322	18	classification	classification	NOUN
cana-1019	322	19	,	,	PUNCT
cana-1019	322	20	with	with	ADP
cana-1019	322	21	high	high	ADJ
cana-1019	322	22	accuracy	accuracy	NOUN
cana-1019	322	23	and	and	CCONJ
cana-1019	322	24	minimal	minimal	ADJ
cana-1019	322	25	misclassifications	misclassification	NOUN
cana-1019	322	26	,	,	PUNCT
cana-1019	322	27	highlighting	highlight	VERB
cana-1019	322	28	their	their	PRON
cana-1019	322	29	robust	robust	ADJ
cana-1019	322	30	performance	performance	NOUN
cana-1019	322	31	.	.	PUNCT
cana-1019	323	1	(	(	PUNCT
cana-1019	323	2	a	a	X
cana-1019	323	3	)	)	PUNCT
cana-1019	323	4	xg	xg	NOUN
cana-1019	323	5	boost	boost	NOUN
cana-1019	323	6	(	(	PUNCT
cana-1019	323	7	b	b	NOUN
cana-1019	323	8	)	)	PUNCT
cana-1019	323	9	random	random	ADJ
cana-1019	323	10	forest	forest	NOUN
cana-1019	323	11	(	(	PUNCT
cana-1019	323	12	c	c	X
cana-1019	323	13	)	)	PUNCT
cana-1019	323	14	svm	svm	ADJ
cana-1019	323	15	linear	linear	ADJ
cana-1019	323	16	figure	figure	NOUN
cana-1019	323	17	9	9	NUM
cana-1019	323	18	:	:	PUNCT
cana-1019	323	19	confusion	confusion	NOUN
cana-1019	323	20	matrix	matrix	NOUN
cana-1019	323	21	fine	fine	ADJ
cana-1019	323	22	tuner	tuner	NOUN
cana-1019	323	23	ml	ml	NOUN
cana-1019	323	24	models	model	NOUN
cana-1019	323	25	with	with	ADP
cana-1019	323	26	an	an	DET
cana-1019	323	27	accuracy	accuracy	NOUN
cana-1019	323	28	of	of	ADP
cana-1019	323	29	0.8545	0.8545	NUM
cana-1019	323	30	and	and	CCONJ
cana-1019	323	31	a	a	DET
cana-1019	323	32	precision	precision	NOUN
cana-1019	323	33	of	of	ADP
cana-1019	323	34	0.869	0.869	NUM
cana-1019	323	35	,	,	PUNCT
cana-1019	323	36	the	the	DET
cana-1019	323	37	svm	svm	PROPN
cana-1019	323	38	linear	linear	PROPN
cana-1019	323	39	model	model	NOUN
cana-1019	323	40	is	be	AUX
cana-1019	323	41	the	the	DET
cana-1019	323	42	most	most	ADV
cana-1019	323	43	accurate	accurate	ADJ
cana-1019	323	44	of	of	ADP
cana-1019	323	45	the	the	DET
cana-1019	323	46	three	three	NUM
cana-1019	323	47	methods	method	NOUN
cana-1019	323	48	.	.	PUNCT
cana-1019	324	1	its	its	PRON
cana-1019	324	2	f1	f1	PROPN
cana-1019	324	3	score	score	NOUN
cana-1019	324	4	,	,	PUNCT
cana-1019	324	5	on	on	ADP
cana-1019	324	6	the	the	DET
cana-1019	324	7	other	other	ADJ
cana-1019	324	8	hand	hand	NOUN
cana-1019	324	9	,	,	PUNCT
cana-1019	324	10	drops	drop	VERB
cana-1019	324	11	to	to	ADP
cana-1019	324	12	0.8356	0.8356	NUM
cana-1019	324	13	because	because	SCONJ
cana-1019	324	14	its	its	PRON
cana-1019	324	15	memory	memory	NOUN
cana-1019	324	16	is	be	AUX
cana-1019	324	17	much	much	ADV
cana-1019	324	18	lower	low	ADJ
cana-1019	324	19	at	at	ADP
cana-1019	324	20	0.460432	0.460432	NUM
cana-1019	324	21	.	.	PUNCT
cana-1019	325	1	this	this	DET
cana-1019	325	2	difference	difference	NOUN
cana-1019	325	3	between	between	ADP
cana-1019	325	4	accuracy	accuracy	NOUN
cana-1019	325	5	and	and	CCONJ
cana-1019	325	6	recall	recall	NOUN
cana-1019	325	7	shows	show	VERB
cana-1019	325	8	that	that	SCONJ
cana-1019	325	9	the	the	DET
cana-1019	325	10	svm	svm	PROPN
cana-1019	325	11	linear	linear	PROPN
cana-1019	325	12	model	model	NOUN
cana-1019	325	13	is	be	AUX
cana-1019	325	14	very	very	ADV
cana-1019	325	15	accurate	accurate	ADJ
cana-1019	325	16	when	when	SCONJ
cana-1019	325	17	it	it	PRON
cana-1019	325	18	says	say	VERB
cana-1019	325	19	something	something	PRON
cana-1019	325	20	is	be	AUX
cana-1019	325	21	positive	positive	ADJ
cana-1019	325	22	,	,	PUNCT
cana-1019	325	23	but	but	CCONJ
cana-1019	325	24	it	it	PRON
cana-1019	325	25	misses	miss	VERB
cana-1019	325	26	a	a	DET
cana-1019	325	27	lot	lot	NOUN
cana-1019	325	28	of	of	ADP
cana-1019	325	29	real	real	ADJ
cana-1019	325	30	positive	positive	ADJ
cana-1019	325	31	cases	case	NOUN
cana-1019	325	32	.	.	PUNCT
cana-1019	326	1	this	this	DET
cana-1019	326	2	mismatch	mismatch	NOUN
cana-1019	326	3	makes	make	VERB
cana-1019	326	4	me	i	PRON
cana-1019	326	5	think	think	VERB
cana-1019	326	6	that	that	SCONJ
cana-1019	326	7	the	the	DET
cana-1019	326	8	model	model	NOUN
cana-1019	326	9	might	might	AUX
cana-1019	326	10	be	be	AUX
cana-1019	326	11	too	too	ADV
cana-1019	326	12	cautious	cautious	ADJ
cana-1019	326	13	in	in	ADP
cana-1019	326	14	its	its	PRON
cana-1019	326	15	predictions	prediction	NOUN
cana-1019	326	16	,	,	PUNCT
cana-1019	326	17	choosing	choose	VERB
cana-1019	326	18	to	to	PART
cana-1019	326	19	stay	stay	VERB
cana-1019	326	20	away	away	ADV
cana-1019	326	21	from	from	ADP
cana-1019	326	22	fake	fake	ADJ
cana-1019	326	23	positives	positive	NOUN
cana-1019	326	24	even	even	ADV
cana-1019	326	25	if	if	SCONJ
cana-1019	326	26	it	it	PRON
cana-1019	326	27	means	mean	VERB
cana-1019	326	28	missing	miss	VERB
cana-1019	326	29	true	true	ADJ
cana-1019	326	30	positives	positive	NOUN
cana-1019	326	31	.	.	PUNCT
cana-1019	327	1	communications	communication	NOUN
cana-1019	327	2	on	on	ADP
cana-1019	327	3	applied	apply	VERB
cana-1019	327	4	nonlinear	nonlinear	ADJ
cana-1019	327	5	analysis	analysis	NOUN
cana-1019	327	6	issn	issn	NOUN
cana-1019	327	7	:	:	PUNCT
cana-1019	327	8	1074	1074	NUM
cana-1019	327	9	-	-	PUNCT
cana-1019	327	10	133x	133x	NUM
cana-1019	327	11	vol	vol	NOUN
cana-1019	327	12	31	31	NUM
cana-1019	327	13	no	no	NOUN
cana-1019	327	14	.	.	PUNCT
cana-1019	328	1	5s	5s	NUM
cana-1019	328	2	(	(	PUNCT
cana-1019	328	3	2024	2024	NUM
cana-1019	328	4	)	)	PUNCT
cana-1019	328	5	257	257	NUM
cana-1019	328	6	https://internationalpubls.com	https://internationalpubls.com	X
cana-1019	328	7	6	6	NUM
cana-1019	328	8	.	.	PUNCT
cana-1019	328	9	conclusion	conclusion	NOUN
cana-1019	328	10	this	this	DET
cana-1019	328	11	study	study	NOUN
cana-1019	328	12	shows	show	VERB
cana-1019	328	13	that	that	SCONJ
cana-1019	328	14	fine	fine	ADJ
cana-1019	328	15	-	-	PUNCT
cana-1019	328	16	tuned	tune	VERB
cana-1019	328	17	machine	machine	NOUN
cana-1019	328	18	learning	learning	NOUN
cana-1019	328	19	methods	method	NOUN
cana-1019	328	20	can	can	AUX
cana-1019	328	21	improve	improve	VERB
cana-1019	328	22	how	how	SCONJ
cana-1019	328	23	well	well	ADV
cana-1019	328	24	and	and	CCONJ
cana-1019	328	25	how	how	SCONJ
cana-1019	328	26	accurately	accurately	ADV
cana-1019	328	27	music	music	NOUN
cana-1019	328	28	mood	mood	NOUN
cana-1019	328	29	labeling	labeling	NOUN
cana-1019	328	30	works	work	NOUN
cana-1019	328	31	.	.	PUNCT
cana-1019	329	1	using	use	VERB
cana-1019	329	2	different	different	ADJ
cana-1019	329	3	algorithms	algorithm	NOUN
cana-1019	329	4	like	like	ADP
cana-1019	329	5	random	random	ADJ
cana-1019	329	6	forest	forest	NOUN
cana-1019	329	7	,	,	PUNCT
cana-1019	329	8	xgboost	xgboost	PROPN
cana-1019	329	9	(	(	PUNCT
cana-1019	329	10	xgb	xgb	ADJ
cana-1019	329	11	)	)	PUNCT
cana-1019	329	12	classifier	classifier	NOUN
cana-1019	329	13	,	,	PUNCT
cana-1019	329	14	and	and	CCONJ
cana-1019	329	15	svm	svm	ADJ
cana-1019	329	16	linear	linear	PROPN
cana-1019	329	17	,	,	PUNCT
cana-1019	329	18	we	we	PRON
cana-1019	329	19	saw	see	VERB
cana-1019	329	20	big	big	ADJ
cana-1019	329	21	gains	gain	NOUN
cana-1019	329	22	in	in	ADP
cana-1019	329	23	accuracy	accuracy	NOUN
cana-1019	329	24	,	,	PUNCT
cana-1019	329	25	precision	precision	NOUN
cana-1019	329	26	,	,	PUNCT
cana-1019	329	27	recall	recall	NOUN
cana-1019	329	28	,	,	PUNCT
cana-1019	329	29	and	and	CCONJ
cana-1019	329	30	f1	f1	PROPN
cana-1019	329	31	score	score	NOUN
cana-1019	329	32	,	,	PUNCT
cana-1019	329	33	among	among	ADP
cana-1019	329	34	other	other	ADJ
cana-1019	329	35	performance	performance	NOUN
cana-1019	329	36	measures	measure	NOUN
cana-1019	329	37	.	.	PUNCT
cana-1019	330	1	the	the	DET
cana-1019	330	2	performance	performance	NOUN
cana-1019	330	3	analysis	analysis	NOUN
cana-1019	330	4	showed	show	VERB
cana-1019	330	5	that	that	SCONJ
cana-1019	330	6	random	random	ADJ
cana-1019	330	7	forest	forest	NOUN
cana-1019	330	8	and	and	CCONJ
cana-1019	330	9	xgb	xgb	NUM
cana-1019	330	10	classifier	classifier	PROPN
cana-1019	330	11	did	do	VERB
cana-1019	330	12	a	a	DET
cana-1019	330	13	better	well	ADJ
cana-1019	330	14	job	job	NOUN
cana-1019	330	15	.	.	PUNCT
cana-1019	331	1	random	random	ADJ
cana-1019	331	2	forest	forest	NOUN
cana-1019	331	3	was	be	AUX
cana-1019	331	4	a	a	DET
cana-1019	331	5	good	good	ADJ
cana-1019	331	6	model	model	NOUN
cana-1019	331	7	for	for	ADP
cana-1019	331	8	this	this	DET
cana-1019	331	9	job	job	NOUN
cana-1019	331	10	;	;	PUNCT
cana-1019	331	11	it	it	PRON
cana-1019	331	12	had	have	VERB
cana-1019	331	13	an	an	DET
cana-1019	331	14	accuracy	accuracy	NOUN
cana-1019	331	15	of	of	ADP
cana-1019	331	16	0.8619	0.8619	NUM
cana-1019	331	17	and	and	CCONJ
cana-1019	331	18	a	a	DET
cana-1019	331	19	good	good	ADJ
cana-1019	331	20	mix	mix	NOUN
cana-1019	331	21	between	between	ADP
cana-1019	331	22	precision	precision	NOUN
cana-1019	331	23	and	and	CCONJ
cana-1019	331	24	memory	memory	NOUN
cana-1019	331	25	.	.	PUNCT
cana-1019	332	1	its	its	PRON
cana-1019	332	2	group	group	NOUN
cana-1019	332	3	nature	nature	NOUN
cana-1019	332	4	successfully	successfully	ADV
cana-1019	332	5	lowers	lower	VERB
cana-1019	332	6	overfitting	overfitte	VERB
cana-1019	332	7	and	and	CCONJ
cana-1019	332	8	raises	raise	VERB
cana-1019	332	9	the	the	DET
cana-1019	332	10	level	level	NOUN
cana-1019	332	11	of	of	ADP
cana-1019	332	12	generalization	generalization	NOUN
cana-1019	332	13	.	.	PUNCT
cana-1019	333	1	the	the	DET
cana-1019	333	2	xgb	xgb	PROPN
cana-1019	333	3	classifier	classifier	NOUN
cana-1019	333	4	also	also	ADV
cana-1019	333	5	had	have	VERB
cana-1019	333	6	the	the	DET
cana-1019	333	7	best	good	ADJ
cana-1019	333	8	accuracy	accuracy	NOUN
cana-1019	333	9	,	,	PUNCT
cana-1019	333	10	at	at	ADP
cana-1019	333	11	0.863	0.863	NUM
cana-1019	333	12	,	,	PUNCT
cana-1019	333	13	which	which	PRON
cana-1019	333	14	showed	show	VERB
cana-1019	333	15	how	how	SCONJ
cana-1019	333	16	well	well	ADV
cana-1019	333	17	it	it	PRON
cana-1019	333	18	handled	handle	VERB
cana-1019	333	19	complex	complex	ADJ
cana-1019	333	20	data	datum	NOUN
cana-1019	333	21	structures	structure	NOUN
cana-1019	333	22	and	and	CCONJ
cana-1019	333	23	how	how	SCONJ
cana-1019	333	24	resistant	resistant	ADJ
cana-1019	333	25	it	it	PRON
cana-1019	333	26	was	be	AUX
cana-1019	333	27	to	to	ADP
cana-1019	333	28	overfitting	overfitte	VERB
cana-1019	333	29	thanks	thank	NOUN
cana-1019	333	30	to	to	ADP
cana-1019	333	31	its	its	PRON
cana-1019	333	32	gradient	gradient	NOUN
cana-1019	333	33	boosting	boost	VERB
cana-1019	333	34	method	method	NOUN
cana-1019	333	35	.	.	PUNCT
cana-1019	334	1	the	the	DET
cana-1019	334	2	confusion	confusion	NOUN
cana-1019	334	3	vectors	vector	NOUN
cana-1019	334	4	for	for	ADP
cana-1019	334	5	these	these	DET
cana-1019	334	6	models	model	NOUN
cana-1019	334	7	showed	show	VERB
cana-1019	334	8	that	that	SCONJ
cana-1019	334	9	they	they	PRON
cana-1019	334	10	could	could	AUX
cana-1019	334	11	reduce	reduce	VERB
cana-1019	334	12	the	the	DET
cana-1019	334	13	number	number	NOUN
cana-1019	334	14	of	of	ADP
cana-1019	334	15	both	both	DET
cana-1019	334	16	false	false	ADJ
cana-1019	334	17	positives	positive	NOUN
cana-1019	334	18	and	and	CCONJ
cana-1019	334	19	false	false	ADJ
cana-1019	334	20	negatives	negative	NOUN
cana-1019	334	21	,	,	PUNCT
cana-1019	334	22	which	which	PRON
cana-1019	334	23	added	add	VERB
cana-1019	334	24	to	to	ADP
cana-1019	334	25	the	the	DET
cana-1019	334	26	evidence	evidence	NOUN
cana-1019	334	27	that	that	SCONJ
cana-1019	334	28	they	they	PRON
cana-1019	334	29	are	be	AUX
cana-1019	334	30	reliable	reliable	ADJ
cana-1019	334	31	in	in	ADP
cana-1019	334	32	real	real	ADJ
cana-1019	334	33	-	-	PUNCT
cana-1019	334	34	world	world	NOUN
cana-1019	334	35	situations	situation	NOUN
cana-1019	334	36	.	.	PUNCT
cana-1019	335	1	careful	careful	ADJ
cana-1019	335	2	adjustment	adjustment	NOUN
cana-1019	335	3	of	of	ADP
cana-1019	335	4	hyperparameters	hyperparameter	NOUN
cana-1019	335	5	during	during	ADP
cana-1019	335	6	the	the	DET
cana-1019	335	7	fine	fine	ADV
cana-1019	335	8	-	-	PUNCT
cana-1019	335	9	tuning	tune	VERB
cana-1019	335	10	process	process	NOUN
cana-1019	335	11	was	be	AUX
cana-1019	335	12	a	a	DET
cana-1019	335	13	key	key	ADJ
cana-1019	335	14	part	part	NOUN
cana-1019	335	15	of	of	ADP
cana-1019	335	16	these	these	DET
cana-1019	335	17	results	result	NOUN
cana-1019	335	18	,	,	PUNCT
cana-1019	335	19	making	make	VERB
cana-1019	335	20	sure	sure	ADJ
cana-1019	335	21	that	that	SCONJ
cana-1019	335	22	each	each	DET
cana-1019	335	23	model	model	NOUN
cana-1019	335	24	worked	work	VERB
cana-1019	335	25	at	at	ADP
cana-1019	335	26	its	its	PRON
cana-1019	335	27	best	good	ADJ
cana-1019	335	28	level	level	NOUN
cana-1019	335	29	of	of	ADP
cana-1019	335	30	performance	performance	NOUN
cana-1019	335	31	.	.	PUNCT
cana-1019	336	1	even	even	ADV
cana-1019	336	2	though	though	SCONJ
cana-1019	336	3	svm	svm	ADJ
cana-1019	336	4	linear	linear	PROPN
cana-1019	336	5	was	be	AUX
cana-1019	336	6	very	very	ADV
cana-1019	336	7	accurate	accurate	ADJ
cana-1019	336	8	,	,	PUNCT
cana-1019	336	9	it	it	PRON
cana-1019	336	10	had	have	VERB
cana-1019	336	11	a	a	DET
cana-1019	336	12	lower	low	ADJ
cana-1019	336	13	recall	recall	NOUN
cana-1019	336	14	,	,	PUNCT
cana-1019	336	15	which	which	PRON
cana-1019	336	16	means	mean	VERB
cana-1019	336	17	it	it	PRON
cana-1019	336	18	missed	miss	VERB
cana-1019	336	19	some	some	DET
cana-1019	336	20	true	true	ADJ
cana-1019	336	21	positives	positive	NOUN
cana-1019	336	22	.	.	PUNCT
cana-1019	337	1	this	this	PRON
cana-1019	337	2	means	mean	VERB
cana-1019	337	3	that	that	SCONJ
cana-1019	337	4	svm	svm	PROPN
cana-1019	337	5	can	can	AUX
cana-1019	337	6	make	make	VERB
cana-1019	337	7	good	good	ADJ
cana-1019	337	8	guesses	guess	NOUN
cana-1019	337	9	,	,	PUNCT
cana-1019	337	10	but	but	CCONJ
cana-1019	337	11	it	it	PRON
cana-1019	337	12	needs	need	VERB
cana-1019	337	13	more	more	ADJ
cana-1019	337	14	work	work	NOUN
cana-1019	337	15	to	to	PART
cana-1019	337	16	find	find	VERB
cana-1019	337	17	the	the	DET
cana-1019	337	18	best	good	ADJ
cana-1019	337	19	mix	mix	NOUN
cana-1019	337	20	between	between	ADP
cana-1019	337	21	accuracy	accuracy	NOUN
cana-1019	337	22	and	and	CCONJ
cana-1019	337	23	memory	memory	NOUN
cana-1019	337	24	.	.	PUNCT
cana-1019	338	1	fine	fine	ADJ
cana-1019	338	2	-	-	PUNCT
cana-1019	338	3	tuning	tune	VERB
cana-1019	338	4	machine	machine	NOUN
cana-1019	338	5	learning	learn	VERB
cana-1019	338	6	algorithms	algorithm	NOUN
cana-1019	338	7	makes	make	VERB
cana-1019	338	8	them	they	PRON
cana-1019	338	9	much	much	ADV
cana-1019	338	10	better	well	ADJ
cana-1019	338	11	at	at	ADP
cana-1019	338	12	figuring	figure	VERB
cana-1019	338	13	out	out	ADP
cana-1019	338	14	the	the	DET
cana-1019	338	15	mood	mood	NOUN
cana-1019	338	16	of	of	ADP
cana-1019	338	17	music	music	NOUN
cana-1019	338	18	.	.	PUNCT
cana-1019	339	1	random	random	ADJ
cana-1019	339	2	forest	forest	NOUN
cana-1019	339	3	and	and	CCONJ
cana-1019	339	4	xgb	xgb	NOUN
cana-1019	339	5	classifier	classifier	NOUN
cana-1019	339	6	stand	stand	VERB
cana-1019	339	7	out	out	ADP
cana-1019	339	8	as	as	ADP
cana-1019	339	9	the	the	DET
cana-1019	339	10	best	good	ADJ
cana-1019	339	11	options	option	NOUN
cana-1019	339	12	because	because	SCONJ
cana-1019	339	13	they	they	PRON
cana-1019	339	14	are	be	AUX
cana-1019	339	15	very	very	ADV
cana-1019	339	16	accurate	accurate	ADJ
cana-1019	339	17	and	and	CCONJ
cana-1019	339	18	do	do	VERB
cana-1019	339	19	well	well	ADV
cana-1019	339	20	on	on	ADP
cana-1019	339	21	all	all	DET
cana-1019	339	22	measures	measure	NOUN
cana-1019	339	23	.	.	PUNCT
cana-1019	340	1	these	these	DET
cana-1019	340	2	results	result	NOUN
cana-1019	340	3	show	show	VERB
cana-1019	340	4	how	how	SCONJ
cana-1019	340	5	important	important	ADJ
cana-1019	340	6	model	model	NOUN
cana-1019	340	7	selection	selection	NOUN
cana-1019	340	8	and	and	CCONJ
cana-1019	340	9	hyperparameter	hyperparameter	NOUN
cana-1019	340	10	improvement	improvement	NOUN
cana-1019	340	11	are	be	AUX
cana-1019	340	12	for	for	ADP
cana-1019	340	13	making	make	VERB
cana-1019	340	14	music	music	NOUN
cana-1019	340	15	mood	mood	NOUN
cana-1019	340	16	recognition	recognition	NOUN
cana-1019	340	17	systems	system	NOUN
cana-1019	340	18	that	that	PRON
cana-1019	340	19	work	work	VERB
cana-1019	340	20	well	well	ADV
cana-1019	340	21	and	and	CCONJ
cana-1019	340	22	are	be	AUX
cana-1019	340	23	reliable	reliable	ADJ
cana-1019	340	24	.	.	PUNCT
cana-1019	341	1	more	more	ADJ
cana-1019	341	2	research	research	NOUN
cana-1019	341	3	can	can	AUX
cana-1019	341	4	look	look	VERB
cana-1019	341	5	into	into	ADP
cana-1019	341	6	adding	add	VERB
cana-1019	341	7	more	more	ADJ
cana-1019	341	8	traits	trait	NOUN
cana-1019	341	9	and	and	CCONJ
cana-1019	341	10	using	use	VERB
cana-1019	341	11	these	these	DET
cana-1019	341	12	models	model	NOUN
cana-1019	341	13	in	in	ADP
cana-1019	341	14	real	real	ADJ
cana-1019	341	15	-	-	PUNCT
cana-1019	341	16	life	life	NOUN
cana-1019	341	17	music	music	NOUN
cana-1019	341	18	therapy	therapy	NOUN
cana-1019	341	19	and	and	CCONJ
cana-1019	341	20	guidance	guidance	NOUN
cana-1019	341	21	systems	system	NOUN
cana-1019	341	22	,	,	PUNCT
cana-1019	341	23	which	which	PRON
cana-1019	341	24	could	could	AUX
cana-1019	341	25	make	make	VERB
cana-1019	341	26	them	they	PRON
cana-1019	341	27	more	more	ADV
cana-1019	341	28	useful	useful	ADJ
cana-1019	341	29	and	and	CCONJ
cana-1019	341	30	have	have	VERB
cana-1019	341	31	a	a	DET
cana-1019	341	32	bigger	big	ADJ
cana-1019	341	33	effect	effect	NOUN
cana-1019	341	34	.	.	PUNCT
cana-1019	342	1	reference	reference	NOUN
cana-1019	342	2	[	[	X
cana-1019	342	3	1	1	NUM
cana-1019	342	4	]	]	SYM
cana-1019	342	5	louro	louro	PROPN
cana-1019	342	6	,	,	PUNCT
cana-1019	342	7	p.l	p.l	PROPN
cana-1019	342	8	.	.	PROPN
cana-1019	342	9	;	;	PUNCT
cana-1019	342	10	redinho	redinho	PROPN
cana-1019	342	11	,	,	PUNCT
cana-1019	342	12	h.	h.	PROPN
cana-1019	342	13	;	;	PUNCT
cana-1019	342	14	malheiro	malheiro	PROPN
cana-1019	342	15	,	,	PUNCT
cana-1019	342	16	r.	r.	PROPN
cana-1019	342	17	;	;	PUNCT
cana-1019	342	18	paiva	paiva	PROPN
cana-1019	342	19	,	,	PUNCT
cana-1019	342	20	r.p	r.p	PROPN
cana-1019	342	21	.	.	PROPN
cana-1019	342	22	;	;	PUNCT
cana-1019	342	23	panda	panda	NOUN
cana-1019	342	24	,	,	PUNCT
cana-1019	342	25	r.	r.	PROPN
cana-1019	342	26	a	a	DET
cana-1019	342	27	comparison	comparison	NOUN
cana-1019	342	28	study	study	NOUN
cana-1019	342	29	of	of	ADP
cana-1019	342	30	deep	deep	ADJ
cana-1019	342	31	learning	learning	NOUN
cana-1019	342	32	methodologies	methodology	NOUN
cana-1019	342	33	for	for	ADP
cana-1019	342	34	music	music	NOUN
cana-1019	342	35	emotion	emotion	NOUN
cana-1019	342	36	recognition	recognition	NOUN
cana-1019	342	37	.	.	PUNCT
cana-1019	343	1	sensors	sensor	NOUN
cana-1019	343	2	2024	2024	NUM
cana-1019	343	3	,	,	PUNCT
cana-1019	343	4	24	24	NUM
cana-1019	343	5	,	,	PUNCT
cana-1019	343	6	2201	2201	NUM
cana-1019	343	7	.	.	PUNCT
cana-1019	344	1	https://doi.org/10.3390/s24072201	https://doi.org/10.3390/s24072201	NUM
cana-1019	344	2	[	[	X
cana-1019	344	3	2	2	NUM
cana-1019	344	4	]	]	X
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cana-1019	344	13	.	.	PUNCT
cana-1019	344	14	;	;	PUNCT
cana-1019	344	15	kwak	kwak	PROPN
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cana-1019	344	17	s.-s	s.-s	PROPN
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cana-1019	344	19	;	;	PUNCT
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cana-1019	344	24	;	;	PUNCT
cana-1019	344	25	park	park	NOUN
cana-1019	344	26	,	,	PUNCT
cana-1019	344	27	j.-h	j.-h	PROPN
cana-1019	344	28	.	.	PUNCT
cana-1019	344	29	;	;	PUNCT
cana-1019	344	30	lee	lee	PROPN
cana-1019	344	31	,	,	PUNCT
cana-1019	344	32	j.-d	j.-d	PROPN
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cana-1019	345	10	data	datum	NOUN
cana-1019	345	11	.	.	PUNCT
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cana-1019	346	3	,	,	PUNCT
cana-1019	346	4	23	23	NUM
cana-1019	346	5	,	,	PUNCT
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cana-1019	347	3	3	3	NUM
cana-1019	347	4	]	]	X
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cana-1019	347	8	;	;	PUNCT
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cana-1019	347	10	,	,	PUNCT
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cana-1019	347	20	a	a	DET
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cana-1019	347	23	learning	learning	NOUN
cana-1019	347	24	model	model	NOUN
cana-1019	347	25	for	for	ADP
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cana-1019	347	32	.	.	PUNCT
cana-1019	348	1	appl	appl	PROPN
cana-1019	348	2	.	.	PUNCT
cana-1019	349	1	sci	sci	PROPN
cana-1019	349	2	.	.	PROPN
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cana-1019	349	4	,	,	PUNCT
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cana-1019	349	6	,	,	PUNCT
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cana-1019	349	8	.	.	PUNCT
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cana-1019	350	3	4	4	NUM
cana-1019	350	4	]	]	X
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cana-1019	350	6	-	-	PUNCT
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cana-1019	350	8	,	,	PUNCT
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cana-1019	350	10	;	;	PUNCT
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cana-1019	350	14	;	;	PUNCT
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cana-1019	350	17	t.	t.	PROPN
cana-1019	350	18	;	;	PUNCT
cana-1019	350	19	jakobsen	jakobsen	PROPN
cana-1019	350	20	,	,	PUNCT
cana-1019	350	21	p.	p.	NOUN
cana-1019	350	22	;	;	PUNCT
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cana-1019	350	25	k.j	k.j	PROPN
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cana-1019	350	27	;	;	PUNCT
cana-1019	350	28	tørresen	tørresen	PROPN
cana-1019	350	29	,	,	PUNCT
cana-1019	350	30	j.	j.	PROPN
cana-1019	350	31	mental	mental	PROPN
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cana-1019	350	33	monitoring	monitoring	NOUN
cana-1019	350	34	with	with	ADP
cana-1019	350	35	multimodal	multimodal	ADJ
cana-1019	350	36	sensing	sensing	NOUN
cana-1019	350	37	and	and	CCONJ
cana-1019	350	38	machine	machine	NOUN
cana-1019	350	39	learning	learning	NOUN
cana-1019	350	40	:	:	PUNCT
cana-1019	350	41	a	a	DET
cana-1019	350	42	survey	survey	NOUN
cana-1019	350	43	.	.	PUNCT
cana-1019	351	1	pervasive	pervasive	ADJ
cana-1019	351	2	mob	mob	NOUN
cana-1019	351	3	.	.	PUNCT
cana-1019	352	1	comput	comput	NOUN
cana-1019	352	2	.	.	PUNCT
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cana-1019	353	2	,	,	PUNCT
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cana-1019	354	1	[	[	X
cana-1019	354	2	5	5	NUM
cana-1019	354	3	]	]	X
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cana-1019	354	7	;	;	PUNCT
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cana-1019	354	10	s.s	s.s	PROPN
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cana-1019	354	12	;	;	PUNCT
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cana-1019	354	14	,	,	PUNCT
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cana-1019	354	16	;	;	PUNCT
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cana-1019	354	19	s.	s.	PROPN
cana-1019	354	20	;	;	PUNCT
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cana-1019	354	30	learning	learning	NOUN
cana-1019	354	31	for	for	ADP
cana-1019	354	32	human	human	ADJ
cana-1019	354	33	emotion	emotion	NOUN
cana-1019	354	34	recognition	recognition	NOUN
cana-1019	354	35	using	use	VERB
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cana-1019	355	3	.	.	PUNCT
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cana-1019	356	2	,	,	PUNCT
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cana-1019	357	8	;	;	PUNCT
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cana-1019	357	10	,	,	PUNCT
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cana-1019	357	20	with	with	ADP
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cana-1019	357	22	failure	failure	NOUN
cana-1019	357	23	instances	instance	NOUN
cana-1019	357	24	and	and	CCONJ
cana-1019	357	25	varying	vary	VERB
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cana-1019	357	27	conditions	condition	NOUN
cana-1019	357	28	using	use	VERB
cana-1019	357	29	a	a	DET
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cana-1019	357	32	.	.	PUNCT
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cana-1019	360	2	.	.	PUNCT
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cana-1019	361	2	,	,	PUNCT
cana-1019	361	3	117	117	NUM
cana-1019	361	4	,	,	PUNCT
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cana-1019	362	1	[	[	X
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cana-1019	362	3	]	]	SYM
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cana-1019	362	7	;	;	PUNCT
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cana-1019	362	18	recognition	recognition	NOUN
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cana-1019	366	1	[	[	X
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cana-1019	368	1	(	(	PUNCT
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cana-1019	369	4	:	:	PUNCT
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cana-1019	369	12	-	-	PUNCT
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cana-1019	370	8	in	in	ADP
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cana-1019	370	12	)	)	PUNCT
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cana-1019	371	2	[	[	X
cana-1019	371	3	9	9	NUM
cana-1019	371	4	]	]	X
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cana-1019	371	9	t	t	PROPN
cana-1019	371	10	,	,	PUNCT
cana-1019	371	11	i.	i.	PROPN
cana-1019	371	12	ahmad	ahmad	PROPN
cana-1019	371	13	,	,	PUNCT
cana-1019	371	14	e.	e.	PROPN
cana-1019	371	15	ardhianto	ardhianto	PROPN
cana-1019	371	16	,	,	PUNCT
cana-1019	371	17	d.	d.	PROPN
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cana-1019	371	24	"	"	PUNCT
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cana-1019	371	31	for	for	ADP
cana-1019	371	32	mood	mood	NOUN
cana-1019	371	33	classification	classification	NOUN
cana-1019	371	34	in	in	ADP
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cana-1019	371	36	music	music	NOUN
cana-1019	371	37	,	,	PUNCT
cana-1019	371	38	"	"	PUNCT
cana-1019	371	39	2023	2023	NUM
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cana-1019	371	41	conference	conference	NOUN
cana-1019	371	42	on	on	ADP
cana-1019	371	43	informatics	informatic	NOUN
cana-1019	371	44	,	,	PUNCT
cana-1019	371	45	multimedia	multimedia	NOUN
cana-1019	371	46	,	,	PUNCT
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cana-1019	371	49	informations	information	NOUN
cana-1019	371	50	system	system	NOUN
cana-1019	371	51	(	(	PUNCT
cana-1019	371	52	icimcis	icimcis	ADJ
cana-1019	371	53	)	)	PUNCT
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cana-1019	372	2	-	-	SYM
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cana-1019	372	4	,	,	PUNCT
cana-1019	372	5	doi	doi	NOUN
cana-1019	372	6	:	:	PUNCT
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cana-1019	372	8	/	/	SYM
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cana-1019	372	16	:	:	PUNCT
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cana-1019	372	18	-	-	PUNCT
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cana-1019	372	20	vol	vol	NOUN
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cana-1019	372	22	no	no	NOUN
cana-1019	372	23	.	.	PUNCT
cana-1019	373	1	5s	5s	NUM
cana-1019	373	2	(	(	PUNCT
cana-1019	373	3	2024	2024	NUM
cana-1019	373	4	)	)	PUNCT
cana-1019	373	5	258	258	NUM
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cana-1019	374	1	[	[	X
cana-1019	374	2	10	10	NUM
cana-1019	374	3	]	]	X
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cana-1019	374	6	and	and	CCONJ
cana-1019	374	7	t.	t.	PROPN
cana-1019	374	8	-s	-s	PROPN
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cana-1019	374	12	"	"	PUNCT
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cana-1019	374	14	classification	classification	NOUN
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cana-1019	374	17	and	and	CCONJ
cana-1019	374	18	convnets	convnet	NOUN
cana-1019	374	19	,	,	PUNCT
cana-1019	374	20	"	"	PUNCT
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cana-1019	374	30	applications	application	NOUN
cana-1019	374	31	(	(	PUNCT
cana-1019	374	32	icmla	icmla	NOUN
cana-1019	374	33	)	)	PUNCT
cana-1019	374	34	,	,	PUNCT
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cana-1019	374	37	,	,	PUNCT
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cana-1019	374	39	,	,	PUNCT
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cana-1019	374	41	,	,	PUNCT
cana-1019	374	42	2019	2019	NUM
cana-1019	374	43	,	,	PUNCT
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cana-1019	374	45	.	.	PUNCT
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cana-1019	375	2	-	-	SYM
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cana-1019	375	4	,	,	PUNCT
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cana-1019	376	3	]	]	X
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cana-1019	376	9	,	,	PUNCT
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cana-1019	376	21	,	,	PUNCT
cana-1019	376	22	"	"	PUNCT
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cana-1019	376	29	in	in	ADP
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cana-1019	376	32	,	,	PUNCT
cana-1019	376	33	"	"	PUNCT
cana-1019	376	34	2023	2023	NUM
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cana-1019	376	41	,	,	PUNCT
cana-1019	376	42	cyber	cyber	NOUN
cana-1019	376	43	and	and	CCONJ
cana-1019	376	44	informations	information	NOUN
cana-1019	376	45	system	system	NOUN
cana-1019	376	46	(	(	PUNCT
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cana-1019	376	48	)	)	PUNCT
cana-1019	376	49	,	,	PUNCT
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cana-1019	376	60	-	-	SYM
cana-1019	376	61	673	673	NUM
cana-1019	376	62	,	,	PUNCT
cana-1019	376	63	doi	doi	NOUN
cana-1019	376	64	:	:	PUNCT
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cana-1019	376	66	/	/	SYM
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cana-1019	377	3	]	]	PUNCT
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cana-1019	377	38	"	"	PUNCT
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cana-1019	377	48	(	(	PUNCT
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cana-1019	377	50	)	)	PUNCT
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cana-1019	378	2	-	-	SYM
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cana-1019	380	2	-	-	SYM
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cana-1019	380	6	:	:	PUNCT
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cana-1019	381	28	(	(	PUNCT
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cana-1019	381	35	,	,	PUNCT
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cana-1019	381	37	,	,	PUNCT
cana-1019	381	38	pp	pp	ADJ
cana-1019	381	39	.	.	PUNCT
cana-1019	382	1	1	1	NUM
cana-1019	382	2	-	-	SYM
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cana-1019	382	4	,	,	PUNCT
cana-1019	382	5	doi	doi	NOUN
cana-1019	382	6	:	:	PUNCT
cana-1019	382	7	10.1109	10.1109	NUM
cana-1019	382	8	/	/	SYM
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cana-1019	382	10	.	.	PUNCT
cana-1019	383	1	[	[	X
cana-1019	383	2	15	15	NUM
cana-1019	383	3	]	]	X
cana-1019	383	4	t.	t.	NOUN
cana-1019	383	5	-t	-t	PROPN
cana-1019	383	6	.	.	PUNCT
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cana-1019	384	2	and	and	CCONJ
cana-1019	384	3	k.	k.	PROPN
cana-1019	384	4	shirai	shirai	PROPN
cana-1019	384	5	,	,	PUNCT
cana-1019	384	6	"	"	PUNCT
cana-1019	384	7	machine	machine	NOUN
cana-1019	384	8	learning	learning	NOUN
cana-1019	384	9	approaches	approach	NOUN
cana-1019	384	10	for	for	ADP
cana-1019	384	11	mood	mood	NOUN
cana-1019	384	12	classification	classification	NOUN
cana-1019	384	13	of	of	ADP
cana-1019	384	14	songs	song	NOUN
cana-1019	384	15	toward	toward	ADP
cana-1019	384	16	music	music	NOUN
cana-1019	384	17	search	search	NOUN
cana-1019	384	18	engine	engine	NOUN
cana-1019	384	19	,	,	PUNCT
cana-1019	384	20	"	"	PUNCT
cana-1019	384	21	2009	2009	NUM
cana-1019	384	22	international	international	ADJ
cana-1019	384	23	conference	conference	NOUN
cana-1019	384	24	on	on	ADP
cana-1019	384	25	knowledge	knowledge	NOUN
cana-1019	384	26	and	and	CCONJ
cana-1019	384	27	systems	system	NOUN
cana-1019	384	28	engineering	engineering	NOUN
cana-1019	384	29	,	,	PUNCT
cana-1019	384	30	hanoi	hanoi	PROPN
cana-1019	384	31	,	,	PUNCT
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cana-1019	384	33	,	,	PUNCT
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cana-1019	384	37	.	.	PUNCT
cana-1019	385	1	144	144	NUM
cana-1019	385	2	-	-	SYM
cana-1019	385	3	149	149	NUM
cana-1019	385	4	,	,	PUNCT
cana-1019	385	5	doi	doi	NOUN
cana-1019	385	6	:	:	PUNCT
cana-1019	385	7	10.1109	10.1109	NUM
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cana-1019	385	10	.	.	PUNCT
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cana-1019	386	2	16	16	NUM
cana-1019	386	3	]	]	PUNCT
cana-1019	386	4	a.	a.	NOUN
cana-1019	386	5	ualibekova	ualibekova	NOUN
cana-1019	386	6	and	and	CCONJ
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cana-1019	386	8	shamoi	shamoi	PROPN
cana-1019	386	9	,	,	PUNCT
cana-1019	386	10	"	"	PUNCT
cana-1019	386	11	music	music	NOUN
cana-1019	386	12	emotion	emotion	NOUN
cana-1019	386	13	recognition	recognition	NOUN
cana-1019	386	14	using	use	VERB
cana-1019	386	15	k	k	PROPN
cana-1019	386	16	-	-	PUNCT
cana-1019	386	17	nearest	near	ADJ
cana-1019	386	18	neighbors	neighbor	NOUN
cana-1019	386	19	algorithm	algorithm	NOUN
cana-1019	386	20	,	,	PUNCT
cana-1019	386	21	"	"	PUNCT
cana-1019	386	22	2022	2022	NUM
cana-1019	386	23	international	international	ADJ
cana-1019	386	24	conference	conference	NOUN
cana-1019	386	25	on	on	ADP
cana-1019	386	26	smart	smart	ADJ
cana-1019	386	27	information	information	NOUN
cana-1019	386	28	systems	system	NOUN
cana-1019	386	29	and	and	CCONJ
cana-1019	386	30	technologies	technology	NOUN
cana-1019	386	31	(	(	PUNCT
cana-1019	386	32	sist	sist	NOUN
cana-1019	386	33	)	)	PUNCT
cana-1019	386	34	,	,	PUNCT
cana-1019	386	35	nur	nur	NOUN
cana-1019	386	36	-	-	ADJ
cana-1019	386	37	sultan	sultan	ADJ
cana-1019	386	38	,	,	PUNCT
cana-1019	386	39	kazakhstan	kazakhstan	PROPN
cana-1019	386	40	,	,	PUNCT
cana-1019	386	41	2022	2022	NUM
cana-1019	386	42	,	,	PUNCT
cana-1019	386	43	pp	pp	ADJ
cana-1019	386	44	.	.	PUNCT
cana-1019	387	1	1	1	NUM
cana-1019	387	2	-	-	SYM
cana-1019	387	3	6	6	NUM
cana-1019	387	4	,	,	PUNCT
cana-1019	387	5	doi	doi	NOUN
cana-1019	387	6	:	:	PUNCT
cana-1019	387	7	10.1109	10.1109	NUM
cana-1019	387	8	/	/	SYM
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cana-1019	387	10	.	.	PUNCT
cana-1019	388	1	[	[	X
cana-1019	388	2	17	17	NUM
cana-1019	388	3	]	]	X
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cana-1019	388	5	shete	shete	PROPN
cana-1019	388	6	,	,	PUNCT
cana-1019	388	7	prashant	prashant	PROPN
cana-1019	388	8	khobragade	khobragade	PROPN
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cana-1019	388	10	an	an	DET
cana-1019	388	11	empirical	empirical	ADJ
cana-1019	388	12	analysis	analysis	NOUN
cana-1019	388	13	of	of	ADP
cana-1019	388	14	different	different	ADJ
cana-1019	388	15	data	datum	NOUN
cana-1019	388	16	visualization	visualization	NOUN
cana-1019	388	17	techniques	technique	NOUN
cana-1019	388	18	from	from	ADP
cana-1019	388	19	statistical	statistical	ADJ
cana-1019	388	20	perspective	perspective	NOUN
cana-1019	388	21	.	.	PUNCT
cana-1019	389	1	aip	aip	PROPN
cana-1019	389	2	conf	conf	PROPN
cana-1019	389	3	.	.	PUNCT
cana-1019	390	1	proc	proc	PROPN
cana-1019	390	2	.	.	PUNCT
cana-1019	391	1	29	29	NUM
cana-1019	391	2	september	september	PROPN
cana-1019	391	3	2023	2023	NUM
cana-1019	391	4	;	;	PUNCT
cana-1019	391	5	2839	2839	NUM
cana-1019	391	6	(	(	PUNCT
cana-1019	391	7	1	1	NUM
cana-1019	391	8	):	):	PUNCT
cana-1019	391	9	040017	040017	NUM
cana-1019	391	10	.	.	PUNCT
cana-1019	392	1	[	[	X
cana-1019	392	2	18	18	NUM
cana-1019	392	3	]	]	X
cana-1019	392	4	m	m	PROPN
cana-1019	392	5	t	t	NOUN
cana-1019	392	6	quasim	quasim	NOUN
cana-1019	392	7	,	,	PUNCT
cana-1019	392	8	e	e	PROPN
cana-1019	392	9	h	h	NOUN
cana-1019	392	10	alkhammash	alkhammash	VERB
cana-1019	392	11	,	,	PUNCT
cana-1019	392	12	m	m	VERB
cana-1019	392	13	a	a	DET
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cana-1019	392	15	et	et	PROPN
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cana-1019	392	19	"	"	PUNCT
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cana-1019	392	21	-	-	PUNCT
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cana-1019	392	23	music	music	NOUN
cana-1019	392	24	recommendation	recommendation	NOUN
cana-1019	392	25	and	and	CCONJ
cana-1019	392	26	classification	classification	NOUN
cana-1019	392	27	using	use	VERB
cana-1019	392	28	machine	machine	NOUN
cana-1019	392	29	learning	learn	VERB
cana-1019	392	30	with	with	ADP
cana-1019	392	31	iot	iot	PROPN
cana-1019	392	32	framework[j	framework[j	PROPN
cana-1019	392	33	]	]	PUNCT
cana-1019	392	34	"	"	PUNCT
cana-1019	392	35	,	,	PUNCT
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cana-1019	392	37	computing	computing	NOUN
cana-1019	392	38	,	,	PUNCT
cana-1019	392	39	vol	vol	NOUN
cana-1019	392	40	.	.	PROPN
cana-1019	392	41	25	25	NUM
cana-1019	392	42	,	,	PUNCT
cana-1019	392	43	no	no	INTJ
cana-1019	392	44	.	.	NOUN
cana-1019	392	45	18	18	NUM
cana-1019	392	46	,	,	PUNCT
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cana-1019	392	48	.	.	PUNCT
cana-1019	393	1	12249	12249	NUM
cana-1019	393	2	-	-	SYM
cana-1019	393	3	12260	12260	NUM
cana-1019	393	4	,	,	PUNCT
cana-1019	393	5	2021	2021	NUM
cana-1019	393	6	.	.	PUNCT
cana-1019	394	1	[	[	X
cana-1019	394	2	19	19	NUM
cana-1019	394	3	]	]	PUNCT
cana-1019	394	4	a	a	DET
cana-1019	394	5	werner	werner	NOUN
cana-1019	394	6	,	,	PUNCT
cana-1019	394	7	"	"	PUNCT
cana-1019	394	8	organizing	organize	VERB
cana-1019	394	9	music	music	NOUN
cana-1019	394	10	organizing	organize	VERB
cana-1019	394	11	gender	gender	NOUN
cana-1019	394	12	:	:	PUNCT
cana-1019	394	13	algorithmic	algorithmic	ADJ
cana-1019	394	14	culture	culture	NOUN
cana-1019	394	15	and	and	CCONJ
cana-1019	394	16	spotify	spotify	VERB
cana-1019	394	17	recommendations[j	recommendations[j	PROPN
cana-1019	394	18	]	]	PUNCT
cana-1019	394	19	"	"	PUNCT
cana-1019	394	20	,	,	PUNCT
cana-1019	394	21	popular	popular	ADJ
cana-1019	394	22	communication	communication	NOUN
cana-1019	394	23	,	,	PUNCT
cana-1019	394	24	vol	vol	NOUN
cana-1019	394	25	.	.	PROPN
cana-1019	394	26	18	18	NUM
cana-1019	394	27	,	,	PUNCT
cana-1019	394	28	no	no	INTJ
cana-1019	394	29	.	.	NOUN
cana-1019	394	30	1	1	NUM
cana-1019	394	31	,	,	PUNCT
cana-1019	394	32	pp	pp	ADJ
cana-1019	394	33	.	.	PUNCT
cana-1019	395	1	78	78	NUM
cana-1019	395	2	-	-	SYM
cana-1019	395	3	90	90	NUM
cana-1019	395	4	,	,	PUNCT
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cana-1019	395	6	.	.	PUNCT
cana-1019	396	1	[	[	X
cana-1019	396	2	20	20	NUM
cana-1019	396	3	]	]	SYM
cana-1019	396	4	m	m	PROPN
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cana-1019	396	6	and	and	CCONJ
cana-1019	396	7	a	a	DET
cana-1019	396	8	fry	fry	NOUN
cana-1019	396	9	,	,	PUNCT
cana-1019	396	10	"	"	PUNCT
cana-1019	396	11	beyond	beyond	ADP
cana-1019	396	12	the	the	DET
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cana-1019	396	14	box	box	NOUN
cana-1019	396	15	in	in	ADP
cana-1019	396	16	music	music	NOUN
cana-1019	396	17	streaming	streaming	NOUN
cana-1019	396	18	:	:	PUNCT
cana-1019	396	19	the	the	DET
cana-1019	396	20	impact	impact	NOUN
cana-1019	396	21	of	of	ADP
cana-1019	396	22	recommendation	recommendation	NOUN
cana-1019	396	23	systems	system	NOUN
cana-1019	396	24	upon	upon	SCONJ
cana-1019	396	25	artists	artist	NOUN
cana-1019	396	26	[	[	X
cana-1019	396	27	j	j	X
cana-1019	396	28	]	]	X
cana-1019	396	29	"	"	PUNCT
cana-1019	396	30	,	,	PUNCT
cana-1019	396	31	popular	popular	ADJ
cana-1019	396	32	communication	communication	NOUN
cana-1019	396	33	,	,	PUNCT
cana-1019	396	34	vol	vol	NOUN
cana-1019	396	35	.	.	PROPN
cana-1019	396	36	18	18	NUM
cana-1019	396	37	,	,	PUNCT
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cana-1019	396	39	.	.	NOUN
cana-1019	396	40	1	1	NUM
cana-1019	396	41	,	,	PUNCT
cana-1019	396	42	pp	pp	ADJ
cana-1019	396	43	.	.	PUNCT
cana-1019	396	44	65	65	NUM
cana-1019	396	45	-	-	SYM
cana-1019	396	46	77	77	NUM
cana-1019	396	47	,	,	PUNCT
cana-1019	396	48	2020	2020	NUM
cana-1019	396	49	.	.	PUNCT
cana-1019	397	1	[	[	X
cana-1019	397	2	21	21	NUM
cana-1019	397	3	]	]	X
cana-1019	397	4	a	a	DET
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cana-1019	397	6	chodos	chodo	NOUN
cana-1019	397	7	,	,	PUNCT
cana-1019	397	8	"	"	PUNCT
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cana-1019	397	10	does	do	AUX
cana-1019	397	11	music	music	NOUN
cana-1019	397	12	mean	mean	VERB
cana-1019	397	13	to	to	PART
cana-1019	397	14	spotify	spotify	VERB
cana-1019	397	15	?	?	PUNCT
cana-1019	398	1	an	an	DET
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cana-1019	398	3	on	on	ADP
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cana-1019	398	5	significance	significance	NOUN
cana-1019	398	6	in	in	ADP
cana-1019	398	7	the	the	DET
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cana-1019	398	9	of	of	ADP
cana-1019	398	10	digital	digital	ADJ
cana-1019	398	11	curation[j	curation[j	NOUN
cana-1019	398	12	]	]	PUNCT
cana-1019	398	13	"	"	PUNCT
cana-1019	398	14	,	,	PUNCT
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cana-1019	398	17	of	of	ADP
cana-1019	398	18	contemporary	contemporary	ADJ
cana-1019	398	19	music	music	NOUN
cana-1019	398	20	art	art	NOUN
cana-1019	398	21	and	and	CCONJ
cana-1019	398	22	technology	technology	NOUN
cana-1019	398	23	,	,	PUNCT
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cana-1019	398	26	1	1	NUM
cana-1019	398	27	,	,	PUNCT
cana-1019	398	28	no	no	INTJ
cana-1019	398	29	.	.	NOUN
cana-1019	398	30	2	2	NUM
cana-1019	398	31	,	,	PUNCT
cana-1019	398	32	pp	pp	ADJ
cana-1019	398	33	.	.	PUNCT
cana-1019	399	1	36	36	NUM
cana-1019	399	2	-	-	SYM
cana-1019	399	3	64	64	NUM
cana-1019	399	4	,	,	PUNCT
cana-1019	399	5	2019	2019	NUM
cana-1019	399	6	.	.	PUNCT
cana-1019	400	1	[	[	X
cana-1019	400	2	22	22	NUM
cana-1019	400	3	]	]	PUNCT
cana-1019	400	4	a	a	DET
cana-1019	400	5	garg	garg	NOUN
cana-1019	400	6	,	,	PUNCT
cana-1019	400	7	v	v	PROPN
cana-1019	400	8	chaturvedi	chaturvedi	PROPN
cana-1019	400	9	,	,	PUNCT
cana-1019	400	10	a	a	DET
cana-1019	400	11	b	b	X
cana-1019	400	12	kaur	kaur	NOUN
cana-1019	400	13	et	et	PROPN
cana-1019	400	14	al	al	PROPN
cana-1019	400	15	.	.	PROPN
cana-1019	400	16	,	,	PUNCT
cana-1019	400	17	"	"	PUNCT
cana-1019	400	18	machine	machine	NOUN
cana-1019	400	19	learning	learning	NOUN
cana-1019	400	20	model	model	NOUN
cana-1019	400	21	for	for	ADP
cana-1019	400	22	mapping	mapping	NOUN
cana-1019	400	23	of	of	ADP
cana-1019	400	24	music	music	NOUN
cana-1019	400	25	mood	mood	NOUN
cana-1019	400	26	and	and	CCONJ
cana-1019	400	27	human	human	ADJ
cana-1019	400	28	emotion	emotion	NOUN
cana-1019	400	29	based	base	VERB
cana-1019	400	30	on	on	ADP
cana-1019	400	31	physiological	physiological	ADJ
cana-1019	400	32	signals[j	signals[j	NOUN
cana-1019	400	33	]	]	PUNCT
cana-1019	400	34	"	"	PUNCT
cana-1019	400	35	,	,	PUNCT
cana-1019	400	36	multimedia	multimedia	NOUN
cana-1019	400	37	tools	tool	NOUN
cana-1019	400	38	and	and	CCONJ
cana-1019	400	39	applications	application	NOUN
cana-1019	400	40	,	,	PUNCT
cana-1019	400	41	vol	vol	NOUN
cana-1019	400	42	.	.	PROPN
cana-1019	400	43	81	81	NUM
cana-1019	400	44	,	,	PUNCT
cana-1019	400	45	no	no	INTJ
cana-1019	400	46	.	.	NOUN
cana-1019	400	47	4	4	NUM
cana-1019	400	48	,	,	PUNCT
cana-1019	400	49	pp	pp	ADJ
cana-1019	400	50	.	.	PUNCT
cana-1019	401	1	5137	5137	NUM
cana-1019	401	2	-	-	SYM
cana-1019	401	3	5177	5177	NUM
cana-1019	401	4	,	,	PUNCT
cana-1019	401	5	2022	2022	NUM
cana-1019	401	6	.	.	PUNCT
cana-1019	402	1	[	[	X
cana-1019	402	2	23	23	NUM
cana-1019	402	3	]	]	PUNCT
cana-1019	402	4	e.	e.	PROPN
cana-1019	402	5	p.	p.	PROPN
cana-1019	402	6	ijjina	ijjina	PROPN
cana-1019	403	1	,	,	PUNCT
cana-1019	403	2	"	"	PUNCT
cana-1019	403	3	classification	classification	NOUN
cana-1019	403	4	of	of	ADP
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cana-1019	403	6	actions	action	NOUN
cana-1019	403	7	using	use	VERB
cana-1019	403	8	pose	pose	NOUN
cana-1019	403	9	-	-	PUNCT
cana-1019	403	10	based	base	VERB
cana-1019	403	11	features	feature	NOUN
cana-1019	403	12	and	and	CCONJ
cana-1019	403	13	stacked	stack	VERB
cana-1019	403	14	auto	auto	NOUN
cana-1019	403	15	encoder	encoder	NOUN
cana-1019	403	16	"	"	PUNCT
cana-1019	403	17	,	,	PUNCT
cana-1019	403	18	pattern	pattern	NOUN
cana-1019	403	19	recognition	recognition	NOUN
cana-1019	403	20	letters	letter	NOUN
cana-1019	403	21	,	,	PUNCT
cana-1019	403	22	vol	vol	NOUN
cana-1019	403	23	.	.	PROPN
cana-1019	403	24	83	83	NUM
cana-1019	403	25	,	,	PUNCT
cana-1019	403	26	pp	pp	ADJ
cana-1019	403	27	.	.	PUNCT
cana-1019	404	1	268	268	NUM
cana-1019	404	2	-	-	SYM
cana-1019	404	3	277	277	NUM
cana-1019	404	4	,	,	PUNCT
cana-1019	404	5	2016	2016	NUM
cana-1019	404	6	.	.	PUNCT
cana-1019	405	1	[	[	X
cana-1019	405	2	24	24	NUM
cana-1019	405	3	]	]	SYM
cana-1019	405	4	v	v	NOUN
cana-1019	405	5	moscato	moscato	PROPN
cana-1019	405	6	,	,	PUNCT
cana-1019	405	7	a	a	DET
cana-1019	405	8	picariello	picariello	NOUN
cana-1019	405	9	and	and	CCONJ
cana-1019	405	10	g	g	NOUN
cana-1019	405	11	sperli	sperli	NOUN
cana-1019	405	12	,	,	PUNCT
cana-1019	405	13	"	"	PUNCT
cana-1019	405	14	an	an	DET
cana-1019	405	15	emotional	emotional	ADJ
cana-1019	405	16	recommender	recommender	NOUN
cana-1019	405	17	system	system	NOUN
cana-1019	405	18	for	for	ADP
cana-1019	405	19	music[j	music[j	ADJ
cana-1019	405	20	]	]	X
cana-1019	405	21	"	"	PUNCT
cana-1019	405	22	,	,	PUNCT
cana-1019	405	23	ieee	ieee	VERB
cana-1019	405	24	intelligent	intelligent	ADJ
cana-1019	405	25	systems	system	NOUN
cana-1019	405	26	,	,	PUNCT
cana-1019	405	27	vol	vol	NOUN
cana-1019	405	28	.	.	PROPN
cana-1019	405	29	36	36	NUM
cana-1019	405	30	,	,	PUNCT
cana-1019	405	31	no	no	INTJ
cana-1019	405	32	.	.	NOUN
cana-1019	405	33	5	5	NUM
cana-1019	405	34	,	,	PUNCT
cana-1019	405	35	pp	pp	ADJ
cana-1019	405	36	.	.	PUNCT
cana-1019	406	1	57	57	NUM
cana-1019	406	2	-	-	SYM
cana-1019	406	3	68	68	NUM
cana-1019	406	4	,	,	PUNCT
cana-1019	406	5	2020	2020	NUM
cana-1019	406	6	.	.	PUNCT
cana-1019	407	1	[	[	X
cana-1019	407	2	25	25	NUM
cana-1019	407	3	]	]	PUNCT
cana-1019	407	4	afis	afis	PROPN
cana-1019	407	5	julianto	julianto	PROPN
cana-1019	407	6	,	,	PUNCT
cana-1019	407	7	andi	andi	PROPN
cana-1019	407	8	sunyoto	sunyoto	PROPN
cana-1019	407	9	and	and	CCONJ
cana-1019	407	10	ferry	ferry	PROPN
cana-1019	407	11	wahyu	wahyu	PROPN
cana-1019	407	12	wibowo	wibowo	NOUN
cana-1019	407	13	,	,	PUNCT
cana-1019	407	14	"	"	PUNCT
cana-1019	407	15	optimasi	optimasi	NOUN
cana-1019	407	16	hyperparameter	hyperparameter	NOUN
cana-1019	407	17	convolutional	convolutional	ADJ
cana-1019	407	18	neural	neural	ADJ
cana-1019	407	19	network	network	NOUN
cana-1019	407	20	untuk	untuk	PROPN
cana-1019	407	21	klasifikasi	klasifikasi	PROPN
cana-1019	407	22	penyakit	penyakit	PROPN
cana-1019	407	23	tanaman	tanaman	PROPN
cana-1019	407	24	padi	padi	PROPN
cana-1019	407	25	"	"	PUNCT
cana-1019	407	26	,	,	PUNCT
cana-1019	407	27	tek	tek	PROPN
cana-1019	407	28	.	.	PROPN
cana-1019	407	29	teknol	teknol	PROPN
cana-1019	407	30	.	.	PUNCT
cana-1019	408	1	inf	inf	PROPN
cana-1019	408	2	.	.	PUNCT
cana-1019	409	1	dan	dan	PROPN
cana-1019	409	2	multimed	multimed	PROPN
cana-1019	409	3	.	.	PUNCT
cana-1019	410	1	,	,	PUNCT
cana-1019	410	2	vol	vol	NOUN
cana-1019	410	3	.	.	PROPN
cana-1019	411	1	3	3	NUM
cana-1019	411	2	,	,	PUNCT
cana-1019	411	3	no	no	INTJ
cana-1019	411	4	.	.	NOUN
cana-1019	411	5	2	2	NUM
cana-1019	411	6	,	,	PUNCT
cana-1019	411	7	pp	pp	ADJ
cana-1019	411	8	.	.	PUNCT
cana-1019	412	1	98	98	NUM
cana-1019	412	2	-	-	SYM
cana-1019	412	3	105	105	NUM
cana-1019	412	4	,	,	PUNCT
cana-1019	412	5	2022	2022	NUM
cana-1019	412	6	.	.	PUNCT
cana-1019	413	1	[	[	X
cana-1019	413	2	26	26	NUM
cana-1019	413	3	]	]	X
cana-1019	413	4	y.	y.	PROPN
cana-1019	413	5	a.	a.	PROPN
cana-1019	413	6	ali	ali	PROPN
cana-1019	413	7	,	,	PUNCT
cana-1019	413	8	e.	e.	PROPN
cana-1019	413	9	m.	m.	PROPN
cana-1019	413	10	awwad	awwad	PROPN
cana-1019	413	11	,	,	PUNCT
cana-1019	413	12	m.	m.	NOUN
cana-1019	413	13	al	al	PROPN
cana-1019	413	14	-	-	PUNCT
cana-1019	413	15	razgan	razgan	PROPN
cana-1019	413	16	and	and	CCONJ
cana-1019	413	17	a.	a.	NOUN
cana-1019	413	18	maarouf	maarouf	PROPN
cana-1019	413	19	,	,	PUNCT
cana-1019	413	20	"	"	PUNCT
cana-1019	413	21	hyperparameter	hyperparameter	NOUN
cana-1019	413	22	search	search	NOUN
cana-1019	413	23	for	for	ADP
cana-1019	413	24	machine	machine	NOUN
cana-1019	413	25	learning	learn	VERB
cana-1019	413	26	algorithms	algorithm	NOUN
cana-1019	413	27	for	for	ADP
cana-1019	413	28	optimizing	optimize	VERB
cana-1019	413	29	the	the	DET
cana-1019	413	30	computational	computational	ADJ
cana-1019	413	31	complexity	complexity	NOUN
cana-1019	413	32	"	"	PUNCT
cana-1019	413	33	,	,	PUNCT
cana-1019	413	34	processes	process	NOUN
cana-1019	413	35	,	,	PUNCT
cana-1019	413	36	vol	vol	NOUN
cana-1019	413	37	.	.	PROPN
cana-1019	413	38	11	11	NUM
cana-1019	413	39	,	,	PUNCT
cana-1019	413	40	no	no	INTJ
cana-1019	413	41	.	.	NOUN
cana-1019	413	42	2	2	NUM
cana-1019	413	43	,	,	PUNCT
cana-1019	413	44	pp	pp	ADJ
cana-1019	413	45	.	.	PUNCT
cana-1019	414	1	1	1	NUM
cana-1019	414	2	-	-	SYM
cana-1019	414	3	22	22	NUM
cana-1019	414	4	,	,	PUNCT
cana-1019	414	5	2023	2023	NUM
cana-1019	414	6	.	.	PUNCT
cana-1019	415	1	[	[	X
cana-1019	415	2	27	27	NUM
cana-1019	415	3	]	]	PUNCT
cana-1019	415	4	s.	s.	PROPN
cana-1019	415	5	zhang	zhang	PROPN
cana-1019	415	6	and	and	CCONJ
cana-1019	415	7	n.	n.	PROPN
cana-1019	415	8	jiang	jiang	PROPN
cana-1019	415	9	,	,	PUNCT
cana-1019	415	10	"	"	PUNCT
cana-1019	415	11	towards	towards	ADP
cana-1019	415	12	hyperparameter	hyperparameter	NOUN
cana-1019	415	13	-	-	PUNCT
cana-1019	415	14	free	free	ADJ
cana-1019	415	15	policy	policy	NOUN
cana-1019	415	16	selection	selection	NOUN
cana-1019	415	17	for	for	ADP
cana-1019	415	18	offline	offline	ADJ
cana-1019	415	19	reinforcement	reinforcement	NOUN
cana-1019	415	20	learning	learning	NOUN
cana-1019	415	21	"	"	PUNCT
cana-1019	415	22	,	,	PUNCT
cana-1019	415	23	adv	adv	PROPN
cana-1019	415	24	.	.	PUNCT
cana-1019	415	25	neural	neural	PROPN
cana-1019	415	26	inf	inf	PROPN
cana-1019	415	27	.	.	PUNCT
cana-1019	415	28	process	process	NOUN
cana-1019	415	29	.	.	PUNCT
cana-1019	416	1	syst	syst	PROPN
cana-1019	416	2	.	.	PUNCT
cana-1019	416	3	,	,	PUNCT
cana-1019	416	4	vol	vol	NOUN
cana-1019	416	5	.	.	PROPN
cana-1019	417	1	16	16	NUM
cana-1019	417	2	,	,	PUNCT
cana-1019	417	3	no	no	INTJ
cana-1019	417	4	.	.	PUNCT
cana-1019	418	1	neurips	neurip	NOUN
cana-1019	418	2	,	,	PUNCT
cana-1019	418	3	pp	pp	ADP
cana-1019	418	4	.	.	PUNCT
cana-1019	419	1	12864	12864	NUM
cana-1019	419	2	-	-	SYM
cana-1019	419	3	12875	12875	NUM
cana-1019	419	4	,	,	PUNCT
cana-1019	419	5	2021	2021	NUM
cana-1019	419	6	.	.	PUNCT