id	sid	tid	token	lemma	pos
fcis-30674	1	1	frontiers	frontier	NOUN
fcis-30674	1	2	in	in	ADP
fcis-30674	1	3	computing	computing	NOUN
fcis-30674	1	4	and	and	CCONJ
fcis-30674	1	5	intelligent	intelligent	ADJ
fcis-30674	1	6	systems	system	NOUN
fcis-30674	1	7	issn	issn	VERB
fcis-30674	1	8	:	:	PUNCT
fcis-30674	1	9	2832	2832	NUM
fcis-30674	1	10	-	-	SYM
fcis-30674	1	11	6024	6024	NUM
fcis-30674	1	12	|	|	NOUN
fcis-30674	1	13	vol	vol	NOUN
fcis-30674	1	14	.	.	PROPN
fcis-30674	2	1	12	12	NUM
fcis-30674	2	2	,	,	PUNCT
fcis-30674	2	3	no	no	INTJ
fcis-30674	2	4	.	.	NOUN
fcis-30674	2	5	1	1	NUM
fcis-30674	2	6	,	,	PUNCT
fcis-30674	2	7	2025	2025	NUM
fcis-30674	2	8	210	210	NUM
fcis-30674	3	1	an	an	DET
fcis-30674	3	2	ensemble	ensemble	ADJ
fcis-30674	3	3	multi‐model	multi‐model	NOUN
fcis-30674	3	4	voting	voting	NOUN
fcis-30674	3	5	method	method	NOUN
fcis-30674	3	6	for	for	ADP
fcis-30674	3	7	adapting	adapt	VERB
fcis-30674	3	8	to	to	PART
fcis-30674	3	9	concept	concept	NOUN
fcis-30674	3	10	drift	drift	NOUN
fcis-30674	3	11	min	min	PROPN
fcis-30674	3	12	wang	wang	PROPN
fcis-30674	3	13	school	school	PROPN
fcis-30674	3	14	of	of	ADP
fcis-30674	3	15	computer	computer	NOUN
fcis-30674	3	16	science	science	NOUN
fcis-30674	3	17	and	and	CCONJ
fcis-30674	3	18	technology	technology	NOUN
fcis-30674	3	19	,	,	PUNCT
fcis-30674	3	20	taiyuan	taiyuan	PROPN
fcis-30674	3	21	normal	normal	ADJ
fcis-30674	3	22	university	university	PROPN
fcis-30674	3	23	,	,	PUNCT
fcis-30674	3	24	jinzhong	jinzhong	PROPN
fcis-30674	3	25	shanxi	shanxi	PROPN
fcis-30674	3	26	,	,	PUNCT
fcis-30674	3	27	030619	030619	NUM
fcis-30674	3	28	,	,	PUNCT
fcis-30674	3	29	china	china	PROPN
fcis-30674	3	30	abstract	abstract	NOUN
fcis-30674	3	31	:	:	PUNCT
fcis-30674	3	32	aiming	aim	VERB
fcis-30674	3	33	at	at	ADP
fcis-30674	3	34	the	the	DET
fcis-30674	3	35	challenges	challenge	NOUN
fcis-30674	3	36	posed	pose	VERB
fcis-30674	3	37	by	by	ADP
fcis-30674	3	38	concept	concept	NOUN
fcis-30674	3	39	drift	drift	NOUN
fcis-30674	3	40	in	in	ADP
fcis-30674	3	41	streaming	stream	VERB
fcis-30674	3	42	data	datum	NOUN
fcis-30674	3	43	mining	mining	NOUN
fcis-30674	3	44	,	,	PUNCT
fcis-30674	3	45	this	this	DET
fcis-30674	3	46	paper	paper	NOUN
fcis-30674	3	47	proposes	propose	VERB
fcis-30674	3	48	an	an	DET
fcis-30674	3	49	ensemble	ensemble	ADJ
fcis-30674	3	50	multimodel	multimodel	NOUN
fcis-30674	3	51	voting	voting	NOUN
fcis-30674	3	52	method	method	NOUN
fcis-30674	3	53	for	for	ADP
fcis-30674	3	54	adapting	adapt	VERB
fcis-30674	3	55	to	to	ADP
fcis-30674	3	56	concept	concept	NOUN
fcis-30674	3	57	drift	drift	NOUN
fcis-30674	3	58	(	(	PUNCT
fcis-30674	3	59	emvm_atcd	emvm_atcd	NOUN
fcis-30674	3	60	)	)	PUNCT
fcis-30674	3	61	.	.	PUNCT
fcis-30674	4	1	the	the	DET
fcis-30674	4	2	method	method	NOUN
fcis-30674	4	3	employs	employ	VERB
fcis-30674	4	4	integrated	integrated	ADJ
fcis-30674	4	5	multi	multi	NOUN
fcis-30674	4	6	-	-	NOUN
fcis-30674	4	7	classifiers	classifier	NOUN
fcis-30674	4	8	to	to	PART
fcis-30674	4	9	improve	improve	VERB
fcis-30674	4	10	model	model	NOUN
fcis-30674	4	11	stability	stability	NOUN
fcis-30674	4	12	,	,	PUNCT
fcis-30674	4	13	uses	use	VERB
fcis-30674	4	14	online	online	ADJ
fcis-30674	4	15	learning	learn	VERB
fcis-30674	4	16	methods	method	NOUN
fcis-30674	4	17	to	to	PART
fcis-30674	4	18	update	update	VERB
fcis-30674	4	19	the	the	DET
fcis-30674	4	20	model	model	NOUN
fcis-30674	4	21	,	,	PUNCT
fcis-30674	4	22	and	and	CCONJ
fcis-30674	4	23	adds	add	VERB
fcis-30674	4	24	a	a	DET
fcis-30674	4	25	dropout	dropout	NOUN
fcis-30674	4	26	layer	layer	NOUN
fcis-30674	4	27	to	to	PART
fcis-30674	4	28	force	force	VERB
fcis-30674	4	29	the	the	DET
fcis-30674	4	30	model	model	NOUN
fcis-30674	4	31	to	to	PART
fcis-30674	4	32	learn	learn	VERB
fcis-30674	4	33	different	different	ADJ
fcis-30674	4	34	combinations	combination	NOUN
fcis-30674	4	35	to	to	PART
fcis-30674	4	36	enhance	enhance	VERB
fcis-30674	4	37	generalization	generalization	NOUN
fcis-30674	4	38	ability	ability	NOUN
fcis-30674	4	39	.	.	PUNCT
fcis-30674	5	1	a	a	DET
fcis-30674	5	2	voting	voting	NOUN
fcis-30674	5	3	mechanism	mechanism	NOUN
fcis-30674	5	4	is	be	AUX
fcis-30674	5	5	used	use	VERB
fcis-30674	5	6	to	to	PART
fcis-30674	5	7	process	process	VERB
fcis-30674	5	8	the	the	DET
fcis-30674	5	9	model	model	NOUN
fcis-30674	5	10	prediction	prediction	NOUN
fcis-30674	5	11	results	result	VERB
fcis-30674	5	12	to	to	PART
fcis-30674	5	13	enhance	enhance	VERB
fcis-30674	5	14	the	the	DET
fcis-30674	5	15	ability	ability	NOUN
fcis-30674	5	16	to	to	PART
fcis-30674	5	17	cope	cope	VERB
fcis-30674	5	18	with	with	ADP
fcis-30674	5	19	concept	concept	NOUN
fcis-30674	5	20	drift	drift	NOUN
fcis-30674	5	21	.	.	PUNCT
fcis-30674	6	1	experimental	experimental	ADJ
fcis-30674	6	2	results	result	NOUN
fcis-30674	6	3	show	show	VERB
fcis-30674	6	4	that	that	SCONJ
fcis-30674	6	5	the	the	DET
fcis-30674	6	6	method	method	NOUN
fcis-30674	6	7	achieves	achieve	VERB
fcis-30674	6	8	performance	performance	NOUN
fcis-30674	6	9	improvements	improvement	NOUN
fcis-30674	6	10	ranging	range	VERB
fcis-30674	6	11	from	from	ADP
fcis-30674	6	12	0.1	0.1	NUM
fcis-30674	6	13	%	%	NOUN
fcis-30674	6	14	to	to	PART
fcis-30674	6	15	10	10	NUM
fcis-30674	6	16	%	%	NOUN
fcis-30674	6	17	on	on	ADP
fcis-30674	6	18	multiple	multiple	ADJ
fcis-30674	6	19	datasets	dataset	NOUN
fcis-30674	6	20	,	,	PUNCT
fcis-30674	6	21	proving	prove	VERB
fcis-30674	6	22	that	that	SCONJ
fcis-30674	6	23	it	it	PRON
fcis-30674	6	24	can	can	AUX
fcis-30674	6	25	effectively	effectively	ADV
fcis-30674	6	26	handle	handle	VERB
fcis-30674	6	27	various	various	ADJ
fcis-30674	6	28	types	type	NOUN
fcis-30674	6	29	of	of	ADP
fcis-30674	6	30	data	datum	NOUN
fcis-30674	6	31	.	.	PUNCT
fcis-30674	7	1	keywords	keyword	NOUN
fcis-30674	7	2	:	:	PUNCT
fcis-30674	7	3	streaming	stream	VERB
fcis-30674	7	4	data	datum	NOUN
fcis-30674	7	5	mining	mining	NOUN
fcis-30674	7	6	;	;	PUNCT
fcis-30674	7	7	integration	integration	NOUN
fcis-30674	7	8	methods	method	NOUN
fcis-30674	7	9	;	;	PUNCT
fcis-30674	7	10	voting	vote	VERB
fcis-30674	7	11	mechanisms	mechanism	NOUN
fcis-30674	7	12	;	;	PUNCT
fcis-30674	7	13	concept	concept	NOUN
fcis-30674	7	14	drift	drift	NOUN
fcis-30674	7	15	.	.	PUNCT
fcis-30674	8	1	1	1	X
fcis-30674	8	2	.	.	X
fcis-30674	8	3	introduction	introduction	NOUN
fcis-30674	8	4	concept	concept	NOUN
fcis-30674	8	5	drift	drift	NOUN
fcis-30674	8	6	[	[	X
fcis-30674	8	7	1	1	NUM
fcis-30674	8	8	-	-	SYM
fcis-30674	8	9	2	2	NUM
fcis-30674	8	10	]	]	PUNCT
fcis-30674	8	11	is	be	AUX
fcis-30674	8	12	a	a	DET
fcis-30674	8	13	challenging	challenging	ADJ
fcis-30674	8	14	task	task	NOUN
fcis-30674	8	15	in	in	ADP
fcis-30674	8	16	the	the	DET
fcis-30674	8	17	field	field	NOUN
fcis-30674	8	18	of	of	ADP
fcis-30674	8	19	streaming	streaming	NOUN
fcis-30674	8	20	data	datum	NOUN
fcis-30674	8	21	mining	mining	NOUN
fcis-30674	8	22	.	.	PUNCT
fcis-30674	9	1	concept	concept	NOUN
fcis-30674	9	2	drift	drift	NOUN
fcis-30674	9	3	is	be	AUX
fcis-30674	9	4	a	a	DET
fcis-30674	9	5	phenomenon	phenomenon	NOUN
fcis-30674	9	6	where	where	SCONJ
fcis-30674	9	7	the	the	DET
fcis-30674	9	8	data	data	NOUN
fcis-30674	9	9	generation	generation	NOUN
fcis-30674	9	10	process	process	NOUN
fcis-30674	9	11	changes	change	NOUN
fcis-30674	9	12	at	at	ADP
fcis-30674	9	13	a	a	DET
fcis-30674	9	14	point	point	NOUN
fcis-30674	9	15	in	in	ADP
fcis-30674	9	16	time	time	NOUN
fcis-30674	9	17	.	.	PUNCT
fcis-30674	10	1	the	the	DET
fcis-30674	10	2	difference	difference	NOUN
fcis-30674	10	3	with	with	ADP
fcis-30674	10	4	traditional	traditional	ADJ
fcis-30674	10	5	data	datum	NOUN
fcis-30674	10	6	mining	mining	NOUN
fcis-30674	10	7	lies	lie	NOUN
fcis-30674	10	8	in	in	ADP
fcis-30674	10	9	the	the	DET
fcis-30674	10	10	fact	fact	NOUN
fcis-30674	10	11	that	that	SCONJ
fcis-30674	10	12	data	datum	NOUN
fcis-30674	10	13	in	in	ADP
fcis-30674	10	14	streaming	streaming	NOUN
fcis-30674	10	15	data	datum	NOUN
fcis-30674	10	16	is	be	AUX
fcis-30674	10	17	constantly	constantly	ADV
fcis-30674	10	18	generated	generate	VERB
fcis-30674	10	19	and	and	CCONJ
fcis-30674	10	20	arrives	arrive	VERB
fcis-30674	10	21	in	in	ADP
fcis-30674	10	22	real	real	ADJ
fcis-30674	10	23	time	time	NOUN
fcis-30674	10	24	.	.	PUNCT
fcis-30674	11	1	the	the	DET
fcis-30674	11	2	underlying	underlie	VERB
fcis-30674	11	3	model	model	NOUN
fcis-30674	11	4	is	be	AUX
fcis-30674	11	5	unable	unable	ADJ
fcis-30674	11	6	to	to	PART
fcis-30674	11	7	provide	provide	VERB
fcis-30674	11	8	accurate	accurate	ADJ
fcis-30674	11	9	predictions	prediction	NOUN
fcis-30674	11	10	when	when	SCONJ
fcis-30674	11	11	faced	face	VERB
fcis-30674	11	12	with	with	ADP
fcis-30674	11	13	data	datum	NOUN
fcis-30674	11	14	distributions	distribution	NOUN
fcis-30674	11	15	that	that	PRON
fcis-30674	11	16	are	be	AUX
fcis-30674	11	17	subject	subject	ADJ
fcis-30674	11	18	to	to	ADP
fcis-30674	11	19	change	change	NOUN
fcis-30674	11	20	,	,	PUNCT
fcis-30674	11	21	and	and	CCONJ
fcis-30674	11	22	the	the	DET
fcis-30674	11	23	emergence	emergence	NOUN
fcis-30674	11	24	of	of	ADP
fcis-30674	11	25	conceptual	conceptual	ADJ
fcis-30674	11	26	drift	drift	NOUN
fcis-30674	11	27	makes	make	VERB
fcis-30674	11	28	many	many	ADJ
fcis-30674	11	29	classical	classical	ADJ
fcis-30674	11	30	learning	learn	VERB
fcis-30674	11	31	algorithms	algorithm	NOUN
fcis-30674	11	32	become	become	VERB
fcis-30674	11	33	inapplicable	inapplicable	ADJ
fcis-30674	11	34	when	when	SCONJ
fcis-30674	11	35	dealing	deal	VERB
fcis-30674	11	36	with	with	ADP
fcis-30674	11	37	streaming	streaming	NOUN
fcis-30674	11	38	data	datum	NOUN
fcis-30674	11	39	,	,	PUNCT
fcis-30674	11	40	bringing	bring	VERB
fcis-30674	11	41	great	great	ADJ
fcis-30674	11	42	adjustments	adjustment	NOUN
fcis-30674	11	43	to	to	ADP
fcis-30674	11	44	data	datum	NOUN
fcis-30674	11	45	mining	mining	NOUN
fcis-30674	11	46	.	.	PUNCT
fcis-30674	12	1	with	with	ADP
fcis-30674	12	2	the	the	DET
fcis-30674	12	3	development	development	NOUN
fcis-30674	12	4	of	of	ADP
fcis-30674	12	5	research	research	NOUN
fcis-30674	12	6	,	,	PUNCT
fcis-30674	12	7	deep	deep	ADJ
fcis-30674	12	8	learning	learning	NOUN
fcis-30674	12	9	[	[	X
fcis-30674	12	10	3	3	NUM
fcis-30674	12	11	-	-	SYM
fcis-30674	12	12	6	6	NUM
fcis-30674	12	13	]	]	PUNCT
fcis-30674	12	14	techniques	technique	NOUN
fcis-30674	12	15	have	have	AUX
fcis-30674	12	16	gradually	gradually	ADV
fcis-30674	12	17	emerged	emerge	VERB
fcis-30674	12	18	to	to	PART
fcis-30674	12	19	cope	cope	VERB
fcis-30674	12	20	with	with	ADP
fcis-30674	12	21	concept	concept	NOUN
fcis-30674	12	22	drift	drift	NOUN
fcis-30674	12	23	.	.	PUNCT
fcis-30674	13	1	for	for	ADP
fcis-30674	13	2	concept	concept	NOUN
fcis-30674	13	3	drift	drift	NOUN
fcis-30674	13	4	,	,	PUNCT
fcis-30674	13	5	common	common	ADJ
fcis-30674	13	6	strategies	strategy	NOUN
fcis-30674	13	7	include	include	VERB
fcis-30674	13	8	incremental	incremental	ADJ
fcis-30674	13	9	learning	learning	NOUN
fcis-30674	13	10	,	,	PUNCT
fcis-30674	13	11	adaptive	adaptive	ADJ
fcis-30674	13	12	strategies	strategy	NOUN
fcis-30674	13	13	,	,	PUNCT
fcis-30674	13	14	sliding	slide	VERB
fcis-30674	13	15	window	window	NOUN
fcis-30674	13	16	methods	method	NOUN
fcis-30674	13	17	,	,	PUNCT
fcis-30674	13	18	and	and	CCONJ
fcis-30674	13	19	integration	integration	NOUN
fcis-30674	13	20	methods	method	NOUN
fcis-30674	13	21	.	.	PUNCT
fcis-30674	14	1	these	these	DET
fcis-30674	14	2	strategies	strategy	NOUN
fcis-30674	14	3	aim	aim	VERB
fcis-30674	14	4	to	to	PART
fcis-30674	14	5	help	help	VERB
fcis-30674	14	6	models	model	NOUN
fcis-30674	14	7	cope	cope	VERB
fcis-30674	14	8	with	with	ADP
fcis-30674	14	9	changes	change	NOUN
fcis-30674	14	10	in	in	ADP
fcis-30674	14	11	data	datum	NOUN
fcis-30674	14	12	while	while	SCONJ
fcis-30674	14	13	improving	improve	VERB
fcis-30674	14	14	robustness	robustness	NOUN
fcis-30674	14	15	.	.	PUNCT
fcis-30674	15	1	incremental	incremental	ADJ
fcis-30674	15	2	learning	learning	NOUN
fcis-30674	15	3	is	be	AUX
fcis-30674	15	4	a	a	DET
fcis-30674	15	5	batch	batch	NOUN
fcis-30674	15	6	learning	learning	NOUN
fcis-30674	15	7	method	method	NOUN
fcis-30674	15	8	that	that	PRON
fcis-30674	15	9	does	do	AUX
fcis-30674	15	10	not	not	PART
fcis-30674	15	11	rely	rely	VERB
fcis-30674	15	12	on	on	ADP
fcis-30674	15	13	learning	learn	VERB
fcis-30674	15	14	from	from	ADP
fcis-30674	15	15	the	the	DET
fcis-30674	15	16	complete	complete	ADJ
fcis-30674	15	17	dataset	dataset	NOUN
fcis-30674	15	18	,	,	PUNCT
fcis-30674	15	19	and	and	CCONJ
fcis-30674	15	20	its	its	PRON
fcis-30674	15	21	updates	update	NOUN
fcis-30674	15	22	the	the	DET
fcis-30674	15	23	model	model	NOUN
fcis-30674	15	24	parameters	parameter	NOUN
fcis-30674	15	25	by	by	ADP
fcis-30674	15	26	learning	learn	VERB
fcis-30674	15	27	incrementally	incrementally	ADV
fcis-30674	15	28	as	as	SCONJ
fcis-30674	15	29	new	new	ADJ
fcis-30674	15	30	data	datum	NOUN
fcis-30674	15	31	arrives	arrive	VERB
fcis-30674	15	32	.	.	PUNCT
fcis-30674	16	1	adapting	adapt	VERB
fcis-30674	16	2	to	to	ADP
fcis-30674	16	3	data	data	NOUN
fcis-30674	16	4	changes	change	NOUN
fcis-30674	16	5	by	by	ADP
fcis-30674	16	6	means	mean	NOUN
fcis-30674	16	7	of	of	ADP
fcis-30674	16	8	adjusting	adjust	VERB
fcis-30674	16	9	the	the	DET
fcis-30674	16	10	learning	learning	NOUN
fcis-30674	16	11	rate	rate	NOUN
fcis-30674	16	12	,	,	PUNCT
fcis-30674	16	13	regularization	regularization	NOUN
fcis-30674	16	14	and	and	CCONJ
fcis-30674	16	15	model	model	NOUN
fcis-30674	16	16	changes	change	NOUN
fcis-30674	16	17	are	be	AUX
fcis-30674	16	18	adaptive	adaptive	ADJ
fcis-30674	16	19	strategies	strategy	NOUN
fcis-30674	16	20	.	.	PUNCT
fcis-30674	17	1	weighted	weight	VERB
fcis-30674	17	2	voting	voting	NOUN
fcis-30674	17	3	mechanism	mechanism	NOUN
fcis-30674	17	4	retains	retain	VERB
fcis-30674	17	5	the	the	DET
fcis-30674	17	6	prediction	prediction	NOUN
fcis-30674	17	7	results	result	NOUN
fcis-30674	17	8	of	of	ADP
fcis-30674	17	9	both	both	CCONJ
fcis-30674	17	10	historical	historical	ADJ
fcis-30674	17	11	and	and	CCONJ
fcis-30674	17	12	newly	newly	ADV
fcis-30674	17	13	arrived	arrive	VERB
fcis-30674	17	14	data	datum	NOUN
fcis-30674	17	15	,	,	PUNCT
fcis-30674	17	16	and	and	CCONJ
fcis-30674	17	17	in	in	ADP
fcis-30674	17	18	order	order	NOUN
fcis-30674	17	19	to	to	PART
fcis-30674	17	20	focus	focus	VERB
fcis-30674	17	21	the	the	DET
fcis-30674	17	22	model	model	NOUN
fcis-30674	17	23	's	's	PART
fcis-30674	17	24	attention	attention	NOUN
fcis-30674	17	25	on	on	ADP
fcis-30674	17	26	the	the	DET
fcis-30674	17	27	current	current	ADJ
fcis-30674	17	28	data	datum	NOUN
fcis-30674	17	29	,	,	PUNCT
fcis-30674	17	30	the	the	DET
fcis-30674	17	31	newly	newly	ADV
fcis-30674	17	32	arrived	arrive	VERB
fcis-30674	17	33	data	datum	NOUN
fcis-30674	17	34	is	be	AUX
fcis-30674	17	35	given	give	VERB
fcis-30674	17	36	a	a	DET
fcis-30674	17	37	higher	high	ADJ
fcis-30674	17	38	weight	weight	NOUN
fcis-30674	17	39	to	to	PART
fcis-30674	17	40	enhance	enhance	VERB
fcis-30674	17	41	the	the	DET
fcis-30674	17	42	adaptability	adaptability	NOUN
fcis-30674	17	43	.	.	PUNCT
fcis-30674	18	1	the	the	DET
fcis-30674	18	2	sliding	slide	VERB
fcis-30674	18	3	window	window	NOUN
fcis-30674	18	4	approach	approach	NOUN
fcis-30674	18	5	sets	set	VERB
fcis-30674	18	6	up	up	ADP
fcis-30674	18	7	a	a	DET
fcis-30674	18	8	fixed	fix	VERB
fcis-30674	18	9	-	-	PUNCT
fcis-30674	18	10	size	size	NOUN
fcis-30674	18	11	data	datum	NOUN
fcis-30674	18	12	window	window	NOUN
fcis-30674	18	13	,	,	PUNCT
fcis-30674	18	14	retains	retain	VERB
fcis-30674	18	15	the	the	DET
fcis-30674	18	16	latest	late	ADJ
fcis-30674	18	17	samples	sample	NOUN
fcis-30674	18	18	to	to	PART
fcis-30674	18	19	train	train	VERB
fcis-30674	18	20	the	the	DET
fcis-30674	18	21	model	model	NOUN
fcis-30674	18	22	,	,	PUNCT
fcis-30674	18	23	and	and	CCONJ
fcis-30674	18	24	as	as	SCONJ
fcis-30674	18	25	new	new	ADJ
fcis-30674	18	26	data	datum	NOUN
fcis-30674	18	27	arrives	arrive	VERB
fcis-30674	18	28	,	,	PUNCT
fcis-30674	18	29	the	the	DET
fcis-30674	18	30	window	window	NOUN
fcis-30674	18	31	slides	slide	VERB
fcis-30674	18	32	while	while	SCONJ
fcis-30674	18	33	removing	remove	VERB
fcis-30674	18	34	the	the	DET
fcis-30674	18	35	oldest	old	ADJ
fcis-30674	18	36	data	datum	NOUN
fcis-30674	18	37	to	to	PART
fcis-30674	18	38	detect	detect	VERB
fcis-30674	18	39	changes	change	NOUN
fcis-30674	18	40	in	in	ADP
fcis-30674	18	41	the	the	DET
fcis-30674	18	42	data	datum	NOUN
fcis-30674	18	43	distribution	distribution	NOUN
fcis-30674	18	44	.	.	PUNCT
fcis-30674	19	1	as	as	SCONJ
fcis-30674	19	2	the	the	DET
fcis-30674	19	3	research	research	NOUN
fcis-30674	19	4	on	on	ADP
fcis-30674	19	5	conceptual	conceptual	ADJ
fcis-30674	19	6	drift	drift	NOUN
fcis-30674	19	7	is	be	AUX
fcis-30674	19	8	gradually	gradually	ADV
fcis-30674	19	9	deepening	deepen	VERB
fcis-30674	19	10	,	,	PUNCT
fcis-30674	19	11	practical	practical	ADJ
fcis-30674	19	12	application	application	NOUN
fcis-30674	19	13	areas	area	NOUN
fcis-30674	19	14	are	be	AUX
fcis-30674	19	15	beginning	begin	VERB
fcis-30674	19	16	to	to	PART
fcis-30674	19	17	use	use	VERB
fcis-30674	19	18	models	model	NOUN
fcis-30674	19	19	based	base	VERB
fcis-30674	19	20	on	on	ADP
fcis-30674	19	21	conceptual	conceptual	ADJ
fcis-30674	19	22	drift	drift	NOUN
fcis-30674	19	23	research	research	NOUN
fcis-30674	19	24	for	for	ADP
fcis-30674	19	25	data	datum	NOUN
fcis-30674	19	26	analysis	analysis	NOUN
fcis-30674	19	27	.	.	PUNCT
fcis-30674	20	1	long	long	ADV
fcis-30674	20	2	and	and	CCONJ
fcis-30674	20	3	shortterm	shortterm	PROPN
fcis-30674	20	4	memory	memory	NOUN
fcis-30674	20	5	networks	network	NOUN
fcis-30674	20	6	(	(	PUNCT
fcis-30674	20	7	i	i	NOUN
fcis-30674	20	8	-	-	PUNCT
fcis-30674	20	9	ltsm	ltsm	NOUN
fcis-30674	20	10	)	)	PUNCT
fcis-30674	21	1	[	[	X
fcis-30674	21	2	7	7	NUM
fcis-30674	21	3	]	]	PUNCT
fcis-30674	21	4	are	be	AUX
fcis-30674	21	5	used	use	VERB
fcis-30674	21	6	in	in	ADP
fcis-30674	21	7	the	the	DET
fcis-30674	21	8	field	field	NOUN
fcis-30674	21	9	of	of	ADP
fcis-30674	21	10	information	information	NOUN
fcis-30674	21	11	security	security	NOUN
fcis-30674	21	12	to	to	PART
fcis-30674	21	13	enhance	enhance	VERB
fcis-30674	21	14	the	the	DET
fcis-30674	21	15	adaptive	adaptive	ADJ
fcis-30674	21	16	protection	protection	NOUN
fcis-30674	21	17	of	of	ADP
fcis-30674	21	18	the	the	DET
fcis-30674	21	19	system	system	NOUN
fcis-30674	21	20	against	against	ADP
fcis-30674	21	21	attacks	attack	NOUN
fcis-30674	21	22	by	by	ADP
fcis-30674	21	23	detecting	detect	VERB
fcis-30674	21	24	anomalies	anomaly	NOUN
fcis-30674	21	25	in	in	ADP
fcis-30674	21	26	the	the	DET
fcis-30674	21	27	network	network	NOUN
fcis-30674	21	28	.	.	PUNCT
fcis-30674	22	1	deepbreath	deepbreath	NOUN
fcis-30674	23	1	[	[	X
fcis-30674	23	2	8	8	NUM
fcis-30674	23	3	]	]	X
fcis-30674	23	4	analyzes	analyze	VERB
fcis-30674	23	5	data	datum	NOUN
fcis-30674	23	6	from	from	ADP
fcis-30674	23	7	the	the	DET
fcis-30674	23	8	financial	financial	ADJ
fcis-30674	23	9	market	market	NOUN
fcis-30674	23	10	to	to	PART
fcis-30674	23	11	mitigate	mitigate	VERB
fcis-30674	23	12	financial	financial	ADJ
fcis-30674	23	13	risks	risk	NOUN
fcis-30674	23	14	and	and	CCONJ
fcis-30674	23	15	has	have	AUX
fcis-30674	23	16	been	be	AUX
fcis-30674	23	17	verified	verify	VERB
fcis-30674	23	18	to	to	PART
fcis-30674	23	19	yield	yield	VERB
fcis-30674	23	20	better	well	ADJ
fcis-30674	23	21	returns	return	NOUN
fcis-30674	23	22	than	than	ADP
fcis-30674	23	23	expert	expert	ADJ
fcis-30674	23	24	investments	investment	NOUN
fcis-30674	23	25	.	.	PUNCT
fcis-30674	24	1	concept	concept	NOUN
fcis-30674	24	2	explorer	explorer	NOUN
fcis-30674	25	1	[	[	X
fcis-30674	25	2	9	9	NUM
fcis-30674	25	3	]	]	PUNCT
fcis-30674	25	4	visualizes	visualize	VERB
fcis-30674	25	5	concept	concept	NOUN
fcis-30674	25	6	drift	drift	NOUN
fcis-30674	25	7	to	to	PART
fcis-30674	25	8	helps	help	VERB
fcis-30674	25	9	decision	decision	NOUN
fcis-30674	25	10	makers	maker	NOUN
fcis-30674	25	11	to	to	PART
fcis-30674	25	12	visualize	visualize	VERB
fcis-30674	25	13	and	and	CCONJ
fcis-30674	25	14	monitor	monitor	VERB
fcis-30674	25	15	in	in	ADP
fcis-30674	25	16	behavioral	behavioral	ADJ
fcis-30674	25	17	analysis	analysis	NOUN
fcis-30674	25	18	and	and	CCONJ
fcis-30674	25	19	air	air	NOUN
fcis-30674	25	20	quality	quality	NOUN
fcis-30674	25	21	detection	detection	NOUN
fcis-30674	25	22	,	,	PUNCT
fcis-30674	25	23	and	and	CCONJ
fcis-30674	25	24	helps	help	VERB
fcis-30674	25	25	to	to	PART
fcis-30674	25	26	inform	inform	VERB
fcis-30674	25	27	the	the	DET
fcis-30674	25	28	adoption	adoption	NOUN
fcis-30674	25	29	of	of	ADP
fcis-30674	25	30	subsequent	subsequent	ADJ
fcis-30674	25	31	strategies	strategy	NOUN
fcis-30674	25	32	by	by	ADP
fcis-30674	25	33	experts	expert	NOUN
fcis-30674	25	34	.	.	PUNCT
fcis-30674	26	1	thus	thus	ADV
fcis-30674	26	2	,	,	PUNCT
fcis-30674	26	3	the	the	DET
fcis-30674	26	4	concept	concept	NOUN
fcis-30674	26	5	drift	drift	NOUN
fcis-30674	26	6	adaptation	adaptation	NOUN
fcis-30674	26	7	approach	approach	NOUN
fcis-30674	26	8	enables	enable	VERB
fcis-30674	26	9	the	the	DET
fcis-30674	26	10	potential	potential	ADJ
fcis-30674	26	11	relationships	relationship	NOUN
fcis-30674	26	12	in	in	ADP
fcis-30674	26	13	the	the	DET
fcis-30674	26	14	data	datum	NOUN
fcis-30674	26	15	to	to	PART
fcis-30674	26	16	be	be	AUX
fcis-30674	26	17	recognized	recognize	VERB
fcis-30674	26	18	,	,	PUNCT
fcis-30674	26	19	and	and	CCONJ
fcis-30674	26	20	with	with	ADP
fcis-30674	26	21	the	the	DET
fcis-30674	26	22	understanding	understanding	NOUN
fcis-30674	26	23	of	of	ADP
fcis-30674	26	24	the	the	DET
fcis-30674	26	25	data	datum	NOUN
fcis-30674	26	26	patterns	pattern	NOUN
fcis-30674	26	27	,	,	PUNCT
fcis-30674	26	28	decision	decision	NOUN
fcis-30674	26	29	makers	maker	NOUN
fcis-30674	26	30	can	can	AUX
fcis-30674	26	31	effectively	effectively	ADV
fcis-30674	26	32	determine	determine	VERB
fcis-30674	26	33	the	the	DET
fcis-30674	26	34	main	main	ADJ
fcis-30674	26	35	influencing	influence	VERB
fcis-30674	26	36	factors	factor	NOUN
fcis-30674	26	37	and	and	CCONJ
fcis-30674	26	38	identify	identify	VERB
fcis-30674	26	39	potential	potential	ADJ
fcis-30674	26	40	dangers	danger	NOUN
fcis-30674	26	41	in	in	ADP
fcis-30674	26	42	order	order	NOUN
fcis-30674	26	43	to	to	PART
fcis-30674	26	44	make	make	VERB
fcis-30674	26	45	the	the	DET
fcis-30674	26	46	right	right	ADJ
fcis-30674	26	47	decisions	decision	NOUN
fcis-30674	26	48	.	.	PUNCT
fcis-30674	27	1	conceptual	conceptual	ADJ
fcis-30674	27	2	drift	drift	NOUN
fcis-30674	27	3	research	research	NOUN
fcis-30674	27	4	applied	apply	VERB
fcis-30674	27	5	to	to	ADP
fcis-30674	27	6	practical	practical	ADJ
fcis-30674	27	7	problems	problem	NOUN
fcis-30674	27	8	can	can	AUX
fcis-30674	27	9	support	support	VERB
fcis-30674	27	10	decision	decision	NOUN
fcis-30674	27	11	making	making	NOUN
fcis-30674	27	12	in	in	ADP
fcis-30674	27	13	different	different	ADJ
fcis-30674	27	14	fields	field	NOUN
fcis-30674	27	15	.	.	PUNCT
fcis-30674	28	1	in	in	ADP
fcis-30674	28	2	this	this	DET
fcis-30674	28	3	paper	paper	NOUN
fcis-30674	28	4	,	,	PUNCT
fcis-30674	28	5	based	base	VERB
fcis-30674	28	6	on	on	ADP
fcis-30674	28	7	the	the	DET
fcis-30674	28	8	existing	exist	VERB
fcis-30674	28	9	deep	deep	ADJ
fcis-30674	28	10	learning	learning	NOUN
fcis-30674	28	11	methods	method	NOUN
fcis-30674	28	12	,	,	PUNCT
fcis-30674	28	13	the	the	DET
fcis-30674	28	14	network	network	NOUN
fcis-30674	28	15	model	model	NOUN
fcis-30674	28	16	with	with	ADP
fcis-30674	28	17	fast	fast	ADJ
fcis-30674	28	18	convergence	convergence	NOUN
fcis-30674	28	19	speed	speed	NOUN
fcis-30674	28	20	and	and	CCONJ
fcis-30674	28	21	guaranteed	guarantee	VERB
fcis-30674	28	22	real	real	ADJ
fcis-30674	28	23	-	-	PUNCT
fcis-30674	28	24	time	time	NOUN
fcis-30674	28	25	update	update	NOUN
fcis-30674	28	26	is	be	AUX
fcis-30674	28	27	selected	select	VERB
fcis-30674	28	28	,	,	PUNCT
fcis-30674	28	29	in	in	ADP
fcis-30674	28	30	order	order	NOUN
fcis-30674	28	31	to	to	PART
fcis-30674	28	32	improve	improve	VERB
fcis-30674	28	33	the	the	DET
fcis-30674	28	34	prediction	prediction	NOUN
fcis-30674	28	35	performance	performance	NOUN
fcis-30674	28	36	while	while	SCONJ
fcis-30674	28	37	improving	improve	VERB
fcis-30674	28	38	the	the	DET
fcis-30674	28	39	ability	ability	NOUN
fcis-30674	28	40	to	to	PART
fcis-30674	28	41	adapt	adapt	VERB
fcis-30674	28	42	to	to	ADP
fcis-30674	28	43	the	the	DET
fcis-30674	28	44	conceptual	conceptual	ADJ
fcis-30674	28	45	drift	drift	NOUN
fcis-30674	28	46	of	of	ADP
fcis-30674	28	47	the	the	DET
fcis-30674	28	48	model	model	NOUN
fcis-30674	28	49	.	.	PUNCT
fcis-30674	29	1	in	in	ADP
fcis-30674	29	2	this	this	DET
fcis-30674	29	3	paper	paper	NOUN
fcis-30674	29	4	,	,	PUNCT
fcis-30674	29	5	a	a	DET
fcis-30674	29	6	classification	classification	NOUN
fcis-30674	29	7	prediction	prediction	NOUN
fcis-30674	29	8	model	model	NOUN
fcis-30674	29	9	integrating	integrate	VERB
fcis-30674	29	10	multi	multi	ADJ
fcis-30674	29	11	-	-	ADJ
fcis-30674	29	12	model	model	ADJ
fcis-30674	29	13	voting	voting	NOUN
fcis-30674	29	14	is	be	AUX
fcis-30674	29	15	designed	design	VERB
fcis-30674	29	16	based	base	VERB
fcis-30674	29	17	on	on	ADP
fcis-30674	29	18	a	a	DET
fcis-30674	29	19	single	single	ADJ
fcis-30674	29	20	hidden	hide	VERB
fcis-30674	29	21	layer	layer	NOUN
fcis-30674	29	22	neural	neural	ADJ
fcis-30674	29	23	network	network	NOUN
fcis-30674	29	24	,	,	PUNCT
fcis-30674	29	25	and	and	CCONJ
fcis-30674	29	26	the	the	DET
fcis-30674	29	27	overall	overall	ADJ
fcis-30674	29	28	structure	structure	NOUN
fcis-30674	29	29	is	be	AUX
fcis-30674	29	30	designed	design	VERB
fcis-30674	29	31	by	by	ADP
fcis-30674	29	32	integrating	integrate	VERB
fcis-30674	29	33	multiple	multiple	ADJ
fcis-30674	29	34	networks	network	NOUN
fcis-30674	29	35	,	,	PUNCT
fcis-30674	29	36	adding	add	VERB
fcis-30674	29	37	dropout	dropout	NOUN
fcis-30674	29	38	layers	layer	NOUN
fcis-30674	29	39	and	and	CCONJ
fcis-30674	29	40	other	other	ADJ
fcis-30674	29	41	methods	method	NOUN
fcis-30674	29	42	.	.	PUNCT
fcis-30674	30	1	the	the	DET
fcis-30674	30	2	results	result	NOUN
fcis-30674	30	3	show	show	VERB
fcis-30674	30	4	that	that	SCONJ
fcis-30674	30	5	the	the	DET
fcis-30674	30	6	method	method	NOUN
fcis-30674	30	7	adapts	adapt	VERB
fcis-30674	30	8	to	to	ADP
fcis-30674	30	9	the	the	DET
fcis-30674	30	10	occurrence	occurrence	NOUN
fcis-30674	30	11	of	of	ADP
fcis-30674	30	12	conceptual	conceptual	ADJ
fcis-30674	30	13	drift	drift	NOUN
fcis-30674	30	14	while	while	SCONJ
fcis-30674	30	15	improving	improve	VERB
fcis-30674	30	16	the	the	DET
fcis-30674	30	17	prediction	prediction	NOUN
fcis-30674	30	18	ability	ability	NOUN
fcis-30674	30	19	.	.	PUNCT
fcis-30674	31	1	2	2	X
fcis-30674	31	2	.	.	X
fcis-30674	31	3	basics	basic	NOUN
fcis-30674	31	4	2.1	2.1	NUM
fcis-30674	31	5	.	.	PUNCT
fcis-30674	32	1	single	single	ADJ
fcis-30674	32	2	hidden	hide	VERB
fcis-30674	32	3	layer	layer	NOUN
fcis-30674	32	4	neural	neural	ADJ
fcis-30674	32	5	networks	network	NOUN
fcis-30674	32	6	the	the	DET
fcis-30674	32	7	design	design	NOUN
fcis-30674	32	8	of	of	ADP
fcis-30674	32	9	neural	neural	ADJ
fcis-30674	32	10	networks	network	NOUN
fcis-30674	32	11	originates	originate	NOUN
fcis-30674	32	12	from	from	ADP
fcis-30674	32	13	the	the	DET
fcis-30674	32	14	biological	biological	ADJ
fcis-30674	32	15	nervous	nervous	ADJ
fcis-30674	32	16	system	system	NOUN
fcis-30674	32	17	by	by	ADP
fcis-30674	32	18	mimicking	mimic	VERB
fcis-30674	32	19	the	the	DET
fcis-30674	32	20	connection	connection	NOUN
fcis-30674	32	21	between	between	ADP
fcis-30674	32	22	neurons	neuron	NOUN
fcis-30674	32	23	and	and	CCONJ
fcis-30674	32	24	the	the	DET
fcis-30674	32	25	process	process	NOUN
fcis-30674	32	26	of	of	ADP
fcis-30674	32	27	information	information	NOUN
fcis-30674	32	28	transfer	transfer	NOUN
fcis-30674	32	29	.	.	PUNCT
fcis-30674	33	1	a	a	DET
fcis-30674	33	2	single	single	ADJ
fcis-30674	33	3	hidden	hide	VERB
fcis-30674	33	4	layer	layer	NOUN
fcis-30674	33	5	neural	neural	ADJ
fcis-30674	33	6	network	network	NOUN
fcis-30674	33	7	[	[	X
fcis-30674	33	8	10	10	NUM
fcis-30674	33	9	]	]	PUNCT
fcis-30674	33	10	is	be	AUX
fcis-30674	33	11	a	a	DET
fcis-30674	33	12	neural	neural	ADJ
fcis-30674	33	13	network	network	NOUN
fcis-30674	33	14	that	that	PRON
fcis-30674	33	15	contains	contain	VERB
fcis-30674	33	16	only	only	ADV
fcis-30674	33	17	one	one	NUM
fcis-30674	33	18	hidden	hide	VERB
fcis-30674	33	19	layer	layer	NOUN
fcis-30674	33	20	,	,	PUNCT
fcis-30674	33	21	which	which	PRON
fcis-30674	33	22	is	be	AUX
fcis-30674	33	23	less	less	ADV
fcis-30674	33	24	computationally	computationally	ADV
fcis-30674	33	25	intensive	intensive	ADJ
fcis-30674	33	26	and	and	CCONJ
fcis-30674	33	27	the	the	DET
fcis-30674	33	28	training	training	NOUN
fcis-30674	33	29	process	process	NOUN
fcis-30674	33	30	is	be	AUX
fcis-30674	33	31	more	more	ADV
fcis-30674	33	32	efficient	efficient	ADJ
fcis-30674	33	33	compared	compare	VERB
fcis-30674	33	34	to	to	ADP
fcis-30674	33	35	deep	deep	ADJ
fcis-30674	33	36	neural	neural	ADJ
fcis-30674	33	37	networks	network	NOUN
fcis-30674	33	38	.	.	PUNCT
fcis-30674	34	1	each	each	DET
fcis-30674	34	2	layer	layer	NOUN
fcis-30674	34	3	in	in	ADP
fcis-30674	34	4	the	the	DET
fcis-30674	34	5	structure	structure	NOUN
fcis-30674	34	6	consists	consist	VERB
fcis-30674	34	7	of	of	ADP
fcis-30674	34	8	multiple	multiple	ADJ
fcis-30674	34	9	neurons	neuron	NOUN
fcis-30674	34	10	and	and	CCONJ
fcis-30674	34	11	each	each	DET
fcis-30674	34	12	neuron	neuron	NOUN
fcis-30674	34	13	has	have	VERB
fcis-30674	34	14	its	its	PRON
fcis-30674	34	15	weight	weight	NOUN
fcis-30674	34	16	,	,	PUNCT
fcis-30674	34	17	the	the	DET
fcis-30674	34	18	hidden	hide	VERB
fcis-30674	34	19	layer	layer	NOUN
fcis-30674	34	20	will	will	AUX
fcis-30674	34	21	transform	transform	VERB
fcis-30674	34	22	the	the	DET
fcis-30674	34	23	input	input	NOUN
fcis-30674	34	24	information	information	NOUN
fcis-30674	34	25	in	in	ADP
fcis-30674	34	26	a	a	DET
fcis-30674	34	27	non	non	ADJ
fcis-30674	34	28	-	-	ADJ
fcis-30674	34	29	linear	linear	ADJ
fcis-30674	34	30	way	way	NOUN
fcis-30674	34	31	and	and	CCONJ
fcis-30674	34	32	the	the	DET
fcis-30674	34	33	output	output	NOUN
fcis-30674	34	34	layer	layer	NOUN
fcis-30674	34	35	is	be	AUX
fcis-30674	34	36	processed	process	VERB
fcis-30674	34	37	by	by	ADP
fcis-30674	34	38	activation	activation	NOUN
fcis-30674	34	39	function	function	NOUN
fcis-30674	34	40	for	for	ADP
fcis-30674	34	41	prediction	prediction	NOUN
fcis-30674	34	42	.	.	PUNCT
fcis-30674	35	1	the	the	DET
fcis-30674	35	2	neural	neural	ADJ
fcis-30674	35	3	network	network	NOUN
fcis-30674	35	4	relies	rely	VERB
fcis-30674	35	5	on	on	ADP
fcis-30674	35	6	the	the	DET
fcis-30674	35	7	back	back	ADJ
fcis-30674	35	8	propagation	propagation	NOUN
fcis-30674	35	9	algorithm	algorithm	NOUN
fcis-30674	35	10	[	[	X
fcis-30674	35	11	11	11	NUM
fcis-30674	35	12	]	]	PUNCT
fcis-30674	35	13	,	,	PUNCT
fcis-30674	35	14	which	which	PRON
fcis-30674	35	15	adjusts	adjust	VERB
fcis-30674	35	16	the	the	DET
fcis-30674	35	17	weight	weight	NOUN
fcis-30674	35	18	changes	change	NOUN
fcis-30674	35	19	by	by	ADP
fcis-30674	35	20	calculating	calculate	VERB
fcis-30674	35	21	the	the	DET
fcis-30674	35	22	error	error	NOUN
fcis-30674	35	23	between	between	ADP
fcis-30674	35	24	the	the	DET
fcis-30674	35	25	predicted	predict	VERB
fcis-30674	35	26	and	and	CCONJ
fcis-30674	35	27	true	true	ADJ
fcis-30674	35	28	results	result	NOUN
fcis-30674	35	29	so	so	SCONJ
fcis-30674	35	30	that	that	SCONJ
fcis-30674	35	31	it	it	PRON
fcis-30674	35	32	can	can	AUX
fcis-30674	35	33	approximate	approximate	VERB
fcis-30674	35	34	the	the	DET
fcis-30674	35	35	true	true	ADJ
fcis-30674	35	36	results	result	NOUN
fcis-30674	35	37	.	.	PUNCT
fcis-30674	36	1	in	in	ADP
fcis-30674	36	2	this	this	DET
fcis-30674	36	3	paper	paper	NOUN
fcis-30674	36	4	,	,	PUNCT
fcis-30674	36	5	the	the	DET
fcis-30674	36	6	model	model	NOUN
fcis-30674	36	7	is	be	AUX
fcis-30674	36	8	constructed	construct	VERB
fcis-30674	36	9	by	by	ADP
fcis-30674	36	10	integrating	integrate	VERB
fcis-30674	36	11	a	a	DET
fcis-30674	36	12	single	single	ADJ
fcis-30674	36	13	hidden	hide	VERB
fcis-30674	36	14	layer	layer	NOUN
fcis-30674	36	15	neural	neural	ADJ
fcis-30674	36	16	network	network	NOUN
fcis-30674	36	17	as	as	ADP
fcis-30674	36	18	a	a	DET
fcis-30674	36	19	way	way	NOUN
fcis-30674	36	20	to	to	PART
fcis-30674	36	21	cope	cope	VERB
fcis-30674	36	22	with	with	ADP
fcis-30674	36	23	complex	complex	ADJ
fcis-30674	36	24	scenarios	scenario	NOUN
fcis-30674	36	25	containing	contain	VERB
fcis-30674	36	26	conceptual	conceptual	ADJ
fcis-30674	36	27	drift	drift	NOUN
fcis-30674	36	28	.	.	PUNCT
fcis-30674	37	1	2.2	2.2	NUM
fcis-30674	37	2	.	.	PUNCT
fcis-30674	37	3	dropout	dropout	NOUN
fcis-30674	37	4	regularization	regularization	NOUN
fcis-30674	37	5	technique	technique	NOUN
fcis-30674	37	6	dropout	dropout	NOUN
fcis-30674	37	7	enhances	enhance	VERB
fcis-30674	37	8	the	the	DET
fcis-30674	37	9	model	model	NOUN
fcis-30674	37	10	's	's	PART
fcis-30674	37	11	ability	ability	NOUN
fcis-30674	37	12	to	to	PART
fcis-30674	37	13	learn	learn	VERB
fcis-30674	37	14	various	various	ADJ
fcis-30674	37	15	data	datum	NOUN
fcis-30674	37	16	211	211	NUM
fcis-30674	37	17	characteristics	characteristic	NOUN
fcis-30674	37	18	by	by	ADP
fcis-30674	37	19	randomly	randomly	ADV
fcis-30674	37	20	discarding	discard	VERB
fcis-30674	37	21	some	some	DET
fcis-30674	37	22	neurons	neuron	NOUN
fcis-30674	37	23	during	during	ADP
fcis-30674	37	24	neural	neural	ADJ
fcis-30674	37	25	network	network	NOUN
fcis-30674	37	26	computation	computation	NOUN
fcis-30674	37	27	so	so	SCONJ
fcis-30674	37	28	that	that	SCONJ
fcis-30674	37	29	the	the	DET
fcis-30674	37	30	model	model	NOUN
fcis-30674	37	31	does	do	AUX
fcis-30674	37	32	not	not	PART
fcis-30674	37	33	only	only	ADV
fcis-30674	37	34	look	look	VERB
fcis-30674	37	35	at	at	ADP
fcis-30674	37	36	certain	certain	ADJ
fcis-30674	37	37	neurons	neuron	NOUN
fcis-30674	37	38	or	or	CCONJ
fcis-30674	37	39	features	feature	NOUN
fcis-30674	37	40	.	.	PUNCT
fcis-30674	38	1	when	when	SCONJ
fcis-30674	38	2	the	the	DET
fcis-30674	38	3	training	training	NOUN
fcis-30674	38	4	data	data	NOUN
fcis-30674	38	5	is	be	AUX
fcis-30674	38	6	unbalanced	unbalanced	ADJ
fcis-30674	38	7	,	,	PUNCT
fcis-30674	38	8	the	the	DET
fcis-30674	38	9	neural	neural	ADJ
fcis-30674	38	10	network	network	NOUN
fcis-30674	38	11	performs	perform	VERB
fcis-30674	38	12	well	well	ADV
fcis-30674	38	13	on	on	ADP
fcis-30674	38	14	classes	class	NOUN
fcis-30674	38	15	with	with	ADP
fcis-30674	38	16	a	a	DET
fcis-30674	38	17	high	high	ADJ
fcis-30674	38	18	number	number	NOUN
fcis-30674	38	19	of	of	ADP
fcis-30674	38	20	occurrences	occurrence	NOUN
fcis-30674	38	21	and	and	CCONJ
fcis-30674	38	22	will	will	AUX
fcis-30674	38	23	ignore	ignore	VERB
fcis-30674	38	24	the	the	DET
fcis-30674	38	25	presence	presence	NOUN
fcis-30674	38	26	of	of	ADP
fcis-30674	38	27	a	a	DET
fcis-30674	38	28	few	few	ADJ
fcis-30674	38	29	classes	class	NOUN
fcis-30674	38	30	,	,	PUNCT
fcis-30674	38	31	resulting	result	VERB
fcis-30674	38	32	in	in	ADP
fcis-30674	38	33	a	a	DET
fcis-30674	38	34	model	model	NOUN
fcis-30674	38	35	that	that	PRON
fcis-30674	38	36	is	be	AUX
fcis-30674	38	37	poor	poor	ADJ
fcis-30674	38	38	at	at	ADP
fcis-30674	38	39	recognizing	recognize	VERB
fcis-30674	38	40	a	a	DET
fcis-30674	38	41	few	few	ADJ
fcis-30674	38	42	classes	class	NOUN
fcis-30674	38	43	.	.	PUNCT
fcis-30674	39	1	the	the	DET
fcis-30674	39	2	proposal	proposal	NOUN
fcis-30674	39	3	of	of	ADP
fcis-30674	39	4	the	the	DET
fcis-30674	39	5	dropout	dropout	NOUN
fcis-30674	39	6	layer	layer	NOUN
fcis-30674	39	7	breaks	break	VERB
fcis-30674	39	8	this	this	DET
fcis-30674	39	9	neuron	neuron	NOUN
fcis-30674	39	10	dependence	dependence	NOUN
fcis-30674	39	11	so	so	SCONJ
fcis-30674	39	12	that	that	SCONJ
fcis-30674	39	13	the	the	DET
fcis-30674	39	14	network	network	NOUN
fcis-30674	39	15	has	have	VERB
fcis-30674	39	16	to	to	PART
fcis-30674	39	17	learn	learn	VERB
fcis-30674	39	18	from	from	ADP
fcis-30674	39	19	a	a	DET
fcis-30674	39	20	wider	wide	ADJ
fcis-30674	39	21	range	range	NOUN
fcis-30674	39	22	of	of	ADP
fcis-30674	39	23	features	feature	NOUN
fcis-30674	39	24	,	,	PUNCT
fcis-30674	39	25	and	and	CCONJ
fcis-30674	39	26	each	each	DET
fcis-30674	39	27	neuron	neuron	NOUN
fcis-30674	39	28	learns	learn	VERB
fcis-30674	39	29	the	the	DET
fcis-30674	39	30	data	data	NOUN
fcis-30674	39	31	features	feature	NOUN
fcis-30674	39	32	,	,	PUNCT
fcis-30674	39	33	increasing	increase	VERB
fcis-30674	39	34	the	the	DET
fcis-30674	39	35	independence	independence	NOUN
fcis-30674	39	36	of	of	ADP
fcis-30674	39	37	the	the	DET
fcis-30674	39	38	neurons	neuron	NOUN
fcis-30674	39	39	.	.	PUNCT
fcis-30674	40	1	dropout	dropout	NOUN
fcis-30674	40	2	is	be	AUX
fcis-30674	40	3	applicable	applicable	ADJ
fcis-30674	40	4	to	to	ADP
fcis-30674	40	5	a	a	DET
fcis-30674	40	6	variety	variety	NOUN
fcis-30674	40	7	of	of	ADP
fcis-30674	40	8	neural	neural	ADJ
fcis-30674	40	9	networks	network	NOUN
fcis-30674	40	10	and	and	CCONJ
fcis-30674	40	11	has	have	AUX
fcis-30674	40	12	achieved	achieve	VERB
fcis-30674	40	13	significant	significant	ADJ
fcis-30674	40	14	results	result	NOUN
fcis-30674	40	15	in	in	ADP
fcis-30674	40	16	many	many	ADJ
fcis-30674	40	17	tasks	task	NOUN
fcis-30674	40	18	.	.	PUNCT
fcis-30674	41	1	3	3	X
fcis-30674	41	2	.	.	X
fcis-30674	41	3	integration	integration	NOUN
fcis-30674	41	4	model	model	NOUN
fcis-30674	41	5	3.1	3.1	NUM
fcis-30674	41	6	.	.	PUNCT
fcis-30674	42	1	algorithmic	algorithmic	ADJ
fcis-30674	42	2	model	model	NOUN
fcis-30674	42	3	emvm_atcd	emvm_atcd	NOUN
fcis-30674	42	4	obtains	obtain	VERB
fcis-30674	42	5	an	an	DET
fcis-30674	42	6	integrated	integrated	ADJ
fcis-30674	42	7	model	model	NOUN
fcis-30674	42	8	with	with	ADP
fcis-30674	42	9	high	high	ADJ
fcis-30674	42	10	performance	performance	NOUN
fcis-30674	42	11	by	by	ADP
fcis-30674	42	12	integrating	integrate	VERB
fcis-30674	42	13	a	a	DET
fcis-30674	42	14	single	single	ADJ
fcis-30674	42	15	hidden	hide	VERB
fcis-30674	42	16	layer	layer	NOUN
fcis-30674	42	17	neural	neural	ADJ
fcis-30674	42	18	network	network	NOUN
fcis-30674	42	19	.	.	PUNCT
fcis-30674	43	1	the	the	DET
fcis-30674	43	2	prediction	prediction	NOUN
fcis-30674	43	3	results	result	NOUN
fcis-30674	43	4	of	of	ADP
fcis-30674	43	5	the	the	DET
fcis-30674	43	6	integrated	integrate	VERB
fcis-30674	43	7	model	model	NOUN
fcis-30674	43	8	are	be	AUX
fcis-30674	43	9	weighted	weight	VERB
fcis-30674	43	10	and	and	CCONJ
fcis-30674	43	11	the	the	DET
fcis-30674	43	12	model	model	NOUN
fcis-30674	43	13	is	be	AUX
fcis-30674	43	14	updated	update	VERB
fcis-30674	43	15	in	in	ADP
fcis-30674	43	16	reverse	reverse	NOUN
fcis-30674	43	17	to	to	PART
fcis-30674	43	18	improve	improve	VERB
fcis-30674	43	19	the	the	DET
fcis-30674	43	20	extraction	extraction	NOUN
fcis-30674	43	21	of	of	ADP
fcis-30674	43	22	data	datum	NOUN
fcis-30674	43	23	patterns	pattern	NOUN
fcis-30674	43	24	,	,	PUNCT
fcis-30674	43	25	and	and	CCONJ
fcis-30674	43	26	a	a	DET
fcis-30674	43	27	dropout	dropout	NOUN
fcis-30674	43	28	layer	layer	NOUN
fcis-30674	43	29	is	be	AUX
fcis-30674	43	30	introduced	introduce	VERB
fcis-30674	43	31	to	to	PART
fcis-30674	43	32	improve	improve	VERB
fcis-30674	43	33	the	the	DET
fcis-30674	43	34	generalization	generalization	NOUN
fcis-30674	43	35	performance	performance	NOUN
fcis-30674	43	36	and	and	CCONJ
fcis-30674	43	37	prevent	prevent	VERB
fcis-30674	43	38	overfitting	overfitte	VERB
fcis-30674	43	39	to	to	PART
fcis-30674	43	40	cope	cope	VERB
fcis-30674	43	41	with	with	ADP
fcis-30674	43	42	the	the	DET
fcis-30674	43	43	concept	concept	NOUN
fcis-30674	43	44	drift	drift	NOUN
fcis-30674	43	45	challenge	challenge	NOUN
fcis-30674	43	46	.	.	PUNCT
fcis-30674	44	1	the	the	DET
fcis-30674	44	2	model	model	NOUN
fcis-30674	44	3	structure	structure	NOUN
fcis-30674	44	4	is	be	AUX
fcis-30674	44	5	shown	show	VERB
fcis-30674	44	6	in	in	ADP
fcis-30674	44	7	figure	figure	NOUN
fcis-30674	44	8	1	1	NUM
fcis-30674	44	9	:	:	PUNCT
fcis-30674	44	10	fig	fig	NOUN
fcis-30674	44	11	1	1	NUM
fcis-30674	44	12	.	.	PUNCT
fcis-30674	45	1	structure	structure	NOUN
fcis-30674	45	2	of	of	ADP
fcis-30674	45	3	the	the	DET
fcis-30674	45	4	emvm_atcd	emvm_atcd	NOUN
fcis-30674	45	5	model	model	NOUN
fcis-30674	45	6	the	the	DET
fcis-30674	45	7	algorithm	algorithm	NOUN
fcis-30674	45	8	enters	enter	VERB
fcis-30674	45	9	the	the	DET
fcis-30674	45	10	model	model	NOUN
fcis-30674	45	11	sequentially	sequentially	ADV
fcis-30674	45	12	in	in	ADP
fcis-30674	45	13	the	the	DET
fcis-30674	45	14	form	form	NOUN
fcis-30674	45	15	of	of	ADP
fcis-30674	45	16	data	datum	NOUN
fcis-30674	45	17	blocks	block	NOUN
fcis-30674	45	18	with	with	ADP
fcis-30674	45	19	a	a	DET
fcis-30674	45	20	serially	serially	ADV
fcis-30674	45	21	integrated	integrate	VERB
fcis-30674	45	22	single	single	ADJ
fcis-30674	45	23	hidden	hide	VERB
fcis-30674	45	24	layer	layer	NOUN
fcis-30674	45	25	neural	neural	ADJ
fcis-30674	45	26	network	network	NOUN
fcis-30674	45	27	structure	structure	NOUN
fcis-30674	45	28	,	,	PUNCT
fcis-30674	45	29	and	and	CCONJ
fcis-30674	45	30	after	after	SCONJ
fcis-30674	45	31	each	each	DET
fcis-30674	45	32	base	base	NOUN
fcis-30674	45	33	model	model	NOUN
fcis-30674	45	34	has	have	AUX
fcis-30674	45	35	finished	finish	VERB
fcis-30674	45	36	running	run	VERB
fcis-30674	45	37	,	,	PUNCT
fcis-30674	45	38	it	it	PRON
fcis-30674	45	39	passes	pass	VERB
fcis-30674	45	40	the	the	DET
fcis-30674	45	41	prediction	prediction	NOUN
fcis-30674	45	42	probability	probability	NOUN
fcis-30674	45	43	to	to	ADP
fcis-30674	45	44	all	all	DET
fcis-30674	45	45	the	the	DET
fcis-30674	45	46	base	base	NOUN
fcis-30674	45	47	models	model	NOUN
fcis-30674	45	48	behind	behind	ADP
fcis-30674	45	49	it	it	PRON
fcis-30674	45	50	,	,	PUNCT
fcis-30674	45	51	and	and	CCONJ
fcis-30674	45	52	the	the	DET
fcis-30674	45	53	models	model	NOUN
fcis-30674	45	54	behind	behind	ADP
fcis-30674	45	55	it	it	PRON
fcis-30674	45	56	will	will	AUX
fcis-30674	45	57	combine	combine	VERB
fcis-30674	45	58	the	the	DET
fcis-30674	45	59	current	current	ADJ
fcis-30674	45	60	model	model	NOUN
fcis-30674	45	61	prediction	prediction	NOUN
fcis-30674	45	62	probability	probability	NOUN
fcis-30674	45	63	with	with	ADP
fcis-30674	45	64	the	the	DET
fcis-30674	45	65	transmitted	transmit	VERB
fcis-30674	45	66	prediction	prediction	NOUN
fcis-30674	45	67	probability	probability	NOUN
fcis-30674	45	68	and	and	CCONJ
fcis-30674	45	69	weight	weight	NOUN
fcis-30674	45	70	the	the	DET
fcis-30674	45	71	probabilities	probability	NOUN
fcis-30674	45	72	to	to	PART
fcis-30674	45	73	calculate	calculate	VERB
fcis-30674	45	74	the	the	DET
fcis-30674	45	75	output	output	NOUN
fcis-30674	45	76	result	result	NOUN
fcis-30674	45	77	.	.	PUNCT
fcis-30674	46	1	subsequently	subsequently	ADV
fcis-30674	46	2	,	,	PUNCT
fcis-30674	46	3	the	the	DET
fcis-30674	46	4	output	output	NOUN
fcis-30674	46	5	results	result	NOUN
fcis-30674	46	6	of	of	ADP
fcis-30674	46	7	multiple	multiple	ADJ
fcis-30674	46	8	base	base	NOUN
fcis-30674	46	9	models	model	NOUN
fcis-30674	46	10	are	be	AUX
fcis-30674	46	11	voted	vote	VERB
fcis-30674	46	12	to	to	PART
fcis-30674	46	13	get	get	VERB
fcis-30674	46	14	the	the	DET
fcis-30674	46	15	final	final	ADJ
fcis-30674	46	16	result	result	NOUN
fcis-30674	46	17	,	,	PUNCT
fcis-30674	46	18	which	which	PRON
fcis-30674	46	19	improves	improve	VERB
fcis-30674	46	20	the	the	DET
fcis-30674	46	21	robustness	robustness	NOUN
fcis-30674	46	22	of	of	ADP
fcis-30674	46	23	the	the	DET
fcis-30674	46	24	model	model	NOUN
fcis-30674	46	25	.	.	PUNCT
fcis-30674	47	1	in	in	ADP
fcis-30674	47	2	this	this	PRON
fcis-30674	47	3	,	,	PUNCT
fcis-30674	47	4	each	each	DET
fcis-30674	47	5	base	base	NOUN
fcis-30674	47	6	model	model	NOUN
fcis-30674	47	7	updates	update	VERB
fcis-30674	47	8	the	the	DET
fcis-30674	47	9	model	model	NOUN
fcis-30674	47	10	by	by	ADP
fcis-30674	47	11	calculating	calculate	VERB
fcis-30674	47	12	the	the	DET
fcis-30674	47	13	error	error	NOUN
fcis-30674	47	14	based	base	VERB
fcis-30674	47	15	on	on	ADP
fcis-30674	47	16	the	the	DET
fcis-30674	47	17	prediction	prediction	NOUN
fcis-30674	47	18	results	result	NOUN
fcis-30674	47	19	to	to	PART
fcis-30674	47	20	improve	improve	VERB
fcis-30674	47	21	the	the	DET
fcis-30674	47	22	model	model	NOUN
fcis-30674	47	23	's	's	PART
fcis-30674	47	24	adaptability	adaptability	NOUN
fcis-30674	47	25	in	in	ADP
fcis-30674	47	26	the	the	DET
fcis-30674	47	27	event	event	NOUN
fcis-30674	47	28	of	of	ADP
fcis-30674	47	29	conceptual	conceptual	ADJ
fcis-30674	47	30	drift	drift	NOUN
fcis-30674	47	31	.	.	PUNCT
fcis-30674	48	1	3.2	3.2	NUM
fcis-30674	48	2	.	.	PUNCT
fcis-30674	48	3	model	model	NOUN
fcis-30674	48	4	structure	structure	NOUN
fcis-30674	48	5	the	the	DET
fcis-30674	48	6	model	model	NOUN
fcis-30674	48	7	structure	structure	NOUN
fcis-30674	48	8	of	of	ADP
fcis-30674	48	9	the	the	DET
fcis-30674	48	10	algorithm	algorithm	NOUN
fcis-30674	48	11	combines	combine	VERB
fcis-30674	48	12	incremental	incremental	ADJ
fcis-30674	48	13	learning	learning	NOUN
fcis-30674	48	14	and	and	CCONJ
fcis-30674	48	15	integration	integration	NOUN
fcis-30674	48	16	methods	method	NOUN
fcis-30674	48	17	[	[	X
fcis-30674	48	18	12	12	NUM
fcis-30674	48	19	-	-	SYM
fcis-30674	48	20	17	17	NUM
fcis-30674	48	21	]	]	PUNCT
fcis-30674	48	22	,	,	PUNCT
fcis-30674	48	23	serially	serially	ADV
fcis-30674	48	24	integrating	integrate	VERB
fcis-30674	48	25	multiple	multiple	ADJ
fcis-30674	48	26	single	single	ADJ
fcis-30674	48	27	hidden	hide	VERB
fcis-30674	48	28	layer	layer	NOUN
fcis-30674	48	29	neural	neural	ADJ
fcis-30674	48	30	network	network	NOUN
fcis-30674	48	31	models	model	NOUN
fcis-30674	48	32	to	to	PART
fcis-30674	48	33	compensate	compensate	VERB
fcis-30674	48	34	for	for	ADP
fcis-30674	48	35	the	the	DET
fcis-30674	48	36	limitations	limitation	NOUN
fcis-30674	48	37	of	of	ADP
fcis-30674	48	38	single	single	ADJ
fcis-30674	48	39	hidden	hide	VERB
fcis-30674	48	40	layer	layer	NOUN
fcis-30674	48	41	neural	neural	ADJ
fcis-30674	48	42	networks	network	NOUN
fcis-30674	48	43	in	in	ADP
fcis-30674	48	44	processing	processing	NOUN
fcis-30674	48	45	complex	complex	ADJ
fcis-30674	48	46	data	datum	NOUN
fcis-30674	48	47	.	.	PUNCT
fcis-30674	49	1	incremental	incremental	ADJ
fcis-30674	49	2	learning	learning	NOUN
fcis-30674	49	3	is	be	AUX
fcis-30674	49	4	introduced	introduce	VERB
fcis-30674	49	5	to	to	PART
fcis-30674	49	6	update	update	VERB
fcis-30674	49	7	the	the	DET
fcis-30674	49	8	model	model	NOUN
fcis-30674	49	9	parameters	parameter	NOUN
fcis-30674	49	10	every	every	DET
fcis-30674	49	11	time	time	NOUN
fcis-30674	49	12	new	new	ADJ
fcis-30674	49	13	information	information	NOUN
fcis-30674	49	14	is	be	AUX
fcis-30674	49	15	learned	learn	VERB
fcis-30674	49	16	,	,	PUNCT
fcis-30674	49	17	which	which	PRON
fcis-30674	49	18	improves	improve	VERB
fcis-30674	49	19	the	the	DET
fcis-30674	49	20	generalization	generalization	NOUN
fcis-30674	49	21	performance	performance	NOUN
fcis-30674	49	22	in	in	ADP
fcis-30674	49	23	the	the	DET
fcis-30674	49	24	face	face	NOUN
fcis-30674	49	25	of	of	ADP
fcis-30674	49	26	concept	concept	NOUN
fcis-30674	49	27	drift	drift	NOUN
fcis-30674	49	28	and	and	CCONJ
fcis-30674	49	29	reduces	reduce	VERB
fcis-30674	49	30	computational	computational	ADJ
fcis-30674	49	31	resources	resource	NOUN
fcis-30674	49	32	through	through	ADP
fcis-30674	49	33	continuous	continuous	ADJ
fcis-30674	49	34	iteration	iteration	NOUN
fcis-30674	49	35	.	.	PUNCT
fcis-30674	50	1	each	each	DET
fcis-30674	50	2	base	base	ADJ
fcis-30674	50	3	learner	learner	NOUN
fcis-30674	50	4	in	in	ADP
fcis-30674	50	5	the	the	DET
fcis-30674	50	6	integrated	integrate	VERB
fcis-30674	50	7	approach	approach	NOUN
fcis-30674	50	8	has	have	VERB
fcis-30674	50	9	a	a	DET
fcis-30674	50	10	different	different	ADJ
fcis-30674	50	11	learning	learning	NOUN
fcis-30674	50	12	process	process	NOUN
fcis-30674	50	13	for	for	ADP
fcis-30674	50	14	the	the	DET
fcis-30674	50	15	data	datum	NOUN
fcis-30674	50	16	,	,	PUNCT
fcis-30674	50	17	which	which	PRON
fcis-30674	50	18	can	can	AUX
fcis-30674	50	19	better	well	ADV
fcis-30674	50	20	capture	capture	VERB
fcis-30674	50	21	the	the	DET
fcis-30674	50	22	diversity	diversity	NOUN
fcis-30674	50	23	and	and	CCONJ
fcis-30674	50	24	changes	change	NOUN
fcis-30674	50	25	in	in	ADP
fcis-30674	50	26	the	the	DET
fcis-30674	50	27	data	datum	NOUN
fcis-30674	50	28	and	and	CCONJ
fcis-30674	50	29	improve	improve	VERB
fcis-30674	50	30	the	the	DET
fcis-30674	50	31	overall	overall	ADJ
fcis-30674	50	32	stability	stability	NOUN
fcis-30674	50	33	of	of	ADP
fcis-30674	50	34	the	the	DET
fcis-30674	50	35	model	model	NOUN
fcis-30674	50	36	.	.	PUNCT
fcis-30674	51	1	as	as	SCONJ
fcis-30674	51	2	shown	show	VERB
fcis-30674	51	3	in	in	ADP
fcis-30674	51	4	figure	figure	NOUN
fcis-30674	51	5	2	2	NUM
fcis-30674	51	6	:	:	PUNCT
fcis-30674	51	7	the	the	DET
fcis-30674	51	8	integrated	integrate	VERB
fcis-30674	51	9	model	model	NOUN
fcis-30674	51	10	structure	structure	NOUN
fcis-30674	51	11	is	be	AUX
fcis-30674	51	12	a	a	DET
fcis-30674	51	13	serial	serial	ADJ
fcis-30674	51	14	model	model	NOUN
fcis-30674	51	15	,	,	PUNCT
fcis-30674	51	16	first	first	ADV
fcis-30674	51	17	of	of	ADP
fcis-30674	51	18	all	all	DET
fcis-30674	51	19	the	the	DET
fcis-30674	51	20	data	data	NOUN
fcis-30674	51	21	enters	enter	VERB
fcis-30674	51	22	the	the	DET
fcis-30674	51	23	model	model	NOUN
fcis-30674	51	24	sequentially	sequentially	ADV
fcis-30674	51	25	in	in	ADP
fcis-30674	51	26	the	the	DET
fcis-30674	51	27	form	form	NOUN
fcis-30674	51	28	of	of	ADP
fcis-30674	51	29	a	a	DET
fcis-30674	51	30	fixed	fix	VERB
fcis-30674	51	31	data	data	NOUN
fcis-30674	51	32	block	block	NOUN
fcis-30674	51	33	,	,	PUNCT
fcis-30674	51	34	and	and	CCONJ
fcis-30674	51	35	the	the	DET
fcis-30674	51	36	output	output	NOUN
fcis-30674	51	37	of	of	ADP
fcis-30674	51	38	the	the	DET
fcis-30674	51	39	j	j	PROPN
fcis-30674	51	40	-	-	PUNCT
fcis-30674	51	41	th	th	VERB
fcis-30674	51	42	base	base	NOUN
fcis-30674	51	43	model	model	NOUN
fcis-30674	51	44	is	be	AUX
fcis-30674	51	45	,	,	PUNCT
fcis-30674	51	46	,	,	PUNCT
fcis-30674	51	47	1,2,⋯	1,2,⋯	NUM
fcis-30674	51	48	,	,	PUNCT
fcis-30674	51	49	,	,	PUNCT
fcis-30674	51	50	1,2,⋯	1,2,⋯	NUM
fcis-30674	51	51	,	,	PUNCT
fcis-30674	51	52	,	,	PUNCT
fcis-30674	51	53	in	in	ADP
fcis-30674	51	54	which	which	PRON
fcis-30674	51	55	is	be	AUX
fcis-30674	51	56	the	the	DET
fcis-30674	51	57	parameter	parameter	NOUN
fcis-30674	51	58	set	set	VERB
fcis-30674	51	59	in	in	ADP
fcis-30674	51	60	the	the	DET
fcis-30674	51	61	corresponding	corresponding	ADJ
fcis-30674	51	62	base	base	NOUN
fcis-30674	51	63	model	model	NOUN
fcis-30674	51	64	,	,	PUNCT
fcis-30674	51	65	and	and	CCONJ
fcis-30674	51	66	there	there	PRON
fcis-30674	51	67	is	be	VERB
fcis-30674	51	68	also	also	ADV
fcis-30674	51	69	a	a	DET
fcis-30674	51	70	corresponding	corresponding	ADJ
fcis-30674	51	71	weight	weight	NOUN
fcis-30674	51	72	,	,	PUNCT
fcis-30674	51	73	0,1,⋯	0,1,⋯	PUNCT
fcis-30674	51	74	,	,	PUNCT
fcis-30674	51	75	in	in	ADP
fcis-30674	51	76	each	each	DET
fcis-30674	51	77	base	base	NOUN
fcis-30674	51	78	model	model	NOUN
fcis-30674	51	79	,	,	PUNCT
fcis-30674	51	80	so	so	SCONJ
fcis-30674	51	81	that	that	SCONJ
fcis-30674	51	82	the	the	DET
fcis-30674	51	83	output	output	NOUN
fcis-30674	51	84	of	of	ADP
fcis-30674	51	85	each	each	DET
fcis-30674	51	86	model	model	NOUN
fcis-30674	51	87	is	be	AUX
fcis-30674	51	88	:	:	PUNCT
fcis-30674	51	89	∑	∑	PUNCT
fcis-30674	51	90	,	,	PUNCT
fcis-30674	51	91	(	(	PUNCT
fcis-30674	51	92	1	1	X
fcis-30674	51	93	)	)	PUNCT
fcis-30674	51	94	the	the	DET
fcis-30674	51	95	model	model	NOUN
fcis-30674	51	96	is	be	AUX
fcis-30674	51	97	designed	design	VERB
fcis-30674	51	98	on	on	ADP
fcis-30674	51	99	the	the	DET
fcis-30674	51	100	basis	basis	NOUN
fcis-30674	51	101	of	of	ADP
fcis-30674	51	102	incremental	incremental	ADJ
fcis-30674	51	103	learning	learning	NOUN
fcis-30674	51	104	,	,	PUNCT
fcis-30674	51	105	and	and	CCONJ
fcis-30674	51	106	the	the	DET
fcis-30674	51	107	output	output	NOUN
fcis-30674	51	108	in	in	ADP
fcis-30674	51	109	eq	eq	ADP
fcis-30674	51	110	.	.	PUNCT
fcis-30674	52	1	(	(	PUNCT
fcis-30674	52	2	1	1	X
fcis-30674	52	3	)	)	PUNCT
fcis-30674	52	4	will	will	AUX
fcis-30674	52	5	be	be	AUX
fcis-30674	52	6	weighted	weight	VERB
fcis-30674	52	7	by	by	ADP
fcis-30674	52	8	combining	combine	VERB
fcis-30674	52	9	the	the	DET
fcis-30674	52	10	prediction	prediction	NOUN
fcis-30674	52	11	results	result	NOUN
fcis-30674	52	12	of	of	ADP
fcis-30674	52	13	previous	previous	ADJ
fcis-30674	52	14	base	base	NOUN
fcis-30674	52	15	models	model	NOUN
fcis-30674	52	16	,	,	PUNCT
fcis-30674	52	17	which	which	PRON
fcis-30674	52	18	can	can	AUX
fcis-30674	52	19	improve	improve	VERB
fcis-30674	52	20	the	the	DET
fcis-30674	52	21	overall	overall	ADJ
fcis-30674	52	22	performance	performance	NOUN
fcis-30674	52	23	.	.	PUNCT
fcis-30674	53	1	as	as	SCONJ
fcis-30674	53	2	the	the	DET
fcis-30674	53	3	data	datum	NOUN
fcis-30674	53	4	block	block	VERB
fcis-30674	53	5	x_i	x_i	PUNCT
fcis-30674	53	6	enters	enter	NOUN
fcis-30674	53	7	,	,	PUNCT
fcis-30674	53	8	each	each	DET
fcis-30674	53	9	base	base	NOUN
fcis-30674	53	10	model	model	NOUN
fcis-30674	53	11	will	will	AUX
fcis-30674	53	12	make	make	VERB
fcis-30674	53	13	a	a	DET
fcis-30674	53	14	prediction	prediction	NOUN
fcis-30674	53	15	based	base	VERB
fcis-30674	53	16	on	on	ADP
fcis-30674	53	17	the	the	DET
fcis-30674	53	18	current	current	ADJ
fcis-30674	53	19	parameters	parameter	NOUN
fcis-30674	53	20	,	,	PUNCT
fcis-30674	53	21	which	which	PRON
fcis-30674	53	22	are	be	AUX
fcis-30674	53	23	the	the	DET
fcis-30674	53	24	parameters	parameter	NOUN
fcis-30674	53	25	of	of	ADP
fcis-30674	53	26	the	the	DET
fcis-30674	53	27	updated	update	VERB
fcis-30674	53	28	model	model	NOUN
fcis-30674	53	29	after	after	SCONJ
fcis-30674	53	30	the	the	DET
fcis-30674	53	31	prediction	prediction	NOUN
fcis-30674	53	32	of	of	ADP
fcis-30674	53	33	the	the	DET
fcis-30674	53	34	previous	previous	ADJ
fcis-30674	53	35	data	data	NOUN
fcis-30674	53	36	block	block	NOUN
fcis-30674	53	37	is	be	AUX
fcis-30674	53	38	finished	finish	VERB
fcis-30674	53	39	.	.	PUNCT
fcis-30674	54	1	the	the	DET
fcis-30674	54	2	m-1st	m-1st	PROPN
fcis-30674	54	3	base	base	NOUN
fcis-30674	54	4	learner	learner	NOUN
fcis-30674	54	5	process	process	NOUN
fcis-30674	54	6	is	be	AUX
fcis-30674	54	7	as	as	SCONJ
fcis-30674	54	8	follows	follow	VERB
fcis-30674	54	9	:	:	PUNCT
fcis-30674	54	10	∑	∑	INTJ
fcis-30674	54	11	,	,	PUNCT
fcis-30674	54	12	,	,	PUNCT
fcis-30674	54	13	,	,	PUNCT
fcis-30674	54	14	(	(	PUNCT
fcis-30674	54	15	2	2	X
fcis-30674	54	16	)	)	PUNCT
fcis-30674	54	17	the	the	DET
fcis-30674	54	18	result	result	NOUN
fcis-30674	54	19	of	of	ADP
fcis-30674	54	20	the	the	DET
fcis-30674	54	21	mth	mth	NOUN
fcis-30674	54	22	base	base	NOUN
fcis-30674	54	23	learner	learner	NOUN
fcis-30674	54	24	is	be	AUX
fcis-30674	54	25	to	to	PART
fcis-30674	54	26	add	add	VERB
fcis-30674	54	27	the	the	DET
fcis-30674	54	28	corresponding	correspond	VERB
fcis-30674	54	29	output	output	NOUN
fcis-30674	54	30	to	to	ADP
fcis-30674	54	31	eq	eq	PROPN
fcis-30674	54	32	.	.	PUNCT
fcis-30674	55	1	(	(	PUNCT
fcis-30674	55	2	2	2	NUM
fcis-30674	55	3	)	)	PUNCT
fcis-30674	55	4	.	.	PUNCT
fcis-30674	56	1	in	in	ADP
fcis-30674	56	2	the	the	DET
fcis-30674	56	3	process	process	NOUN
fcis-30674	56	4	of	of	ADP
fcis-30674	56	5	weighting	weight	VERB
fcis-30674	56	6	the	the	DET
fcis-30674	56	7	output	output	NOUN
fcis-30674	56	8	of	of	ADP
fcis-30674	56	9	multiple	multiple	ADJ
fcis-30674	56	10	models	model	NOUN
fcis-30674	56	11	,	,	PUNCT
fcis-30674	56	12	the	the	DET
fcis-30674	56	13	input	input	NOUN
fcis-30674	56	14	of	of	ADP
fcis-30674	56	15	the	the	DET
fcis-30674	56	16	mth	mth	NOUN
fcis-30674	56	17	base	base	NOUN
fcis-30674	56	18	learner	learner	NOUN
fcis-30674	56	19	will	will	AUX
fcis-30674	56	20	combine	combine	VERB
fcis-30674	56	21	the	the	DET
fcis-30674	56	22	original	original	ADJ
fcis-30674	56	23	input	input	NOUN
fcis-30674	56	24	and	and	CCONJ
fcis-30674	56	25	the	the	DET
fcis-30674	56	26	hidden	hide	VERB
fcis-30674	56	27	layer	layer	NOUN
fcis-30674	56	28	output	output	NOUN
fcis-30674	56	29	of	of	ADP
fcis-30674	56	30	the	the	DET
fcis-30674	56	31	m-1st	m-1st	PROPN
fcis-30674	56	32	base	base	NOUN
fcis-30674	56	33	model	model	NOUN
fcis-30674	56	34	,	,	PUNCT
fcis-30674	56	35	expanding	expand	VERB
fcis-30674	56	36	the	the	DET
fcis-30674	56	37	capacity	capacity	NOUN
fcis-30674	56	38	of	of	ADP
fcis-30674	56	39	the	the	DET
fcis-30674	56	40	model	model	NOUN
fcis-30674	56	41	while	while	SCONJ
fcis-30674	56	42	increasing	increase	VERB
fcis-30674	56	43	the	the	DET
fcis-30674	56	44	complexity	complexity	NOUN
fcis-30674	56	45	of	of	ADP
fcis-30674	56	46	the	the	DET
fcis-30674	56	47	model	model	NOUN
fcis-30674	56	48	,	,	PUNCT
fcis-30674	56	49	and	and	CCONJ
fcis-30674	56	50	ensuring	ensure	VERB
fcis-30674	56	51	the	the	DET
fcis-30674	56	52	performance	performance	NOUN
fcis-30674	56	53	of	of	ADP
fcis-30674	56	54	model	model	NOUN
fcis-30674	56	55	adaptation	adaptation	NOUN
fcis-30674	56	56	through	through	ADP
fcis-30674	56	57	multiple	multiple	ADJ
fcis-30674	56	58	rounds	round	NOUN
fcis-30674	56	59	of	of	ADP
fcis-30674	56	60	computational	computational	ADJ
fcis-30674	56	61	iterations	iteration	NOUN
fcis-30674	56	62	.	.	PUNCT
fcis-30674	57	1	3.3	3.3	NUM
fcis-30674	57	2	.	.	PUNCT
fcis-30674	58	1	parameter	parameter	NOUN
fcis-30674	58	2	updates	update	VERB
fcis-30674	58	3	each	each	DET
fcis-30674	58	4	base	base	ADJ
fcis-30674	58	5	learner	learner	NOUN
fcis-30674	58	6	in	in	ADP
fcis-30674	58	7	the	the	DET
fcis-30674	58	8	integrated	integrate	VERB
fcis-30674	58	9	model	model	NOUN
fcis-30674	58	10	updates	update	VERB
fcis-30674	58	11	its	its	PRON
fcis-30674	58	12	parameters	parameter	NOUN
fcis-30674	58	13	through	through	ADP
fcis-30674	58	14	a	a	DET
fcis-30674	58	15	back	back	ADJ
fcis-30674	58	16	-	-	PUNCT
fcis-30674	58	17	propagation	propagation	NOUN
fcis-30674	58	18	mechanism	mechanism	NOUN
fcis-30674	58	19	,	,	PUNCT
fcis-30674	58	20	which	which	PRON
fcis-30674	58	21	first	first	ADV
fcis-30674	58	22	calculates	calculate	VERB
fcis-30674	58	23	the	the	DET
fcis-30674	58	24	error	error	NOUN
fcis-30674	58	25	and	and	CCONJ
fcis-30674	58	26	updates	update	VERB
fcis-30674	58	27	the	the	DET
fcis-30674	58	28	error	error	NOUN
fcis-30674	58	29	in	in	ADP
fcis-30674	58	30	the	the	DET
fcis-30674	58	31	opposite	opposite	ADJ
fcis-30674	58	32	direction	direction	NOUN
fcis-30674	58	33	to	to	PART
fcis-30674	58	34	optimize	optimize	VERB
fcis-30674	58	35	all	all	DET
fcis-30674	58	36	parameters	parameter	NOUN
fcis-30674	58	37	and	and	CCONJ
fcis-30674	58	38	weights	weight	NOUN
fcis-30674	58	39	in	in	ADP
fcis-30674	58	40	the	the	DET
fcis-30674	58	41	base	base	NOUN
fcis-30674	58	42	learner	learner	NOUN
fcis-30674	58	43	.	.	PUNCT
fcis-30674	59	1	the	the	DET
fcis-30674	59	2	data	datum	NOUN
fcis-30674	59	3	and	and	CCONJ
fcis-30674	59	4	labels	label	NOUN
fcis-30674	59	5	of	of	ADP
fcis-30674	59	6	the	the	DET
fcis-30674	59	7	sample	sample	NOUN
fcis-30674	59	8	are	be	AUX
fcis-30674	59	9	divided	divide	VERB
fcis-30674	59	10	into	into	ADP
fcis-30674	59	11	,	,	PUNCT
fcis-30674	59	12	by	by	ADP
fcis-30674	59	13	data	datum	NOUN
fcis-30674	59	14	blocks	block	NOUN
fcis-30674	59	15	,	,	PUNCT
fcis-30674	59	16	and	and	CCONJ
fcis-30674	59	17	the	the	DET
fcis-30674	59	18	parameter	parameter	NOUN
fcis-30674	59	19	set	set	NOUN
fcis-30674	59	20	obtained	obtain	VERB
fcis-30674	59	21	through	through	ADP
fcis-30674	59	22	212	212	NUM
fcis-30674	59	23	optimization	optimization	NOUN
fcis-30674	59	24	is	be	AUX
fcis-30674	59	25	,	,	PUNCT
fcis-30674	59	26	,	,	PUNCT
fcis-30674	59	27	,	,	PUNCT
fcis-30674	59	28	at	at	ADP
fcis-30674	59	29	this	this	DET
fcis-30674	59	30	time	time	NOUN
fcis-30674	59	31	,	,	PUNCT
fcis-30674	59	32	the	the	DET
fcis-30674	59	33	classification	classification	NOUN
fcis-30674	59	34	result	result	NOUN
fcis-30674	59	35	of	of	ADP
fcis-30674	59	36	the	the	DET
fcis-30674	59	37	j	j	PROPN
fcis-30674	59	38	-	-	PUNCT
fcis-30674	59	39	th	th	VERB
fcis-30674	59	40	base	base	NOUN
fcis-30674	59	41	learner	learner	NOUN
fcis-30674	59	42	is	be	AUX
fcis-30674	59	43	:	:	PUNCT
fcis-30674	59	44	,	,	PUNCT
fcis-30674	59	45	(	(	PUNCT
fcis-30674	59	46	3	3	X
fcis-30674	59	47	)	)	PUNCT
fcis-30674	59	48	fig	fig	NOUN
fcis-30674	59	49	2	2	NUM
fcis-30674	59	50	.	.	PUNCT
fcis-30674	59	51	model	model	NOUN
fcis-30674	59	52	structure	structure	NOUN
fcis-30674	59	53	diagram	diagram	NOUN
fcis-30674	59	54	at	at	ADP
fcis-30674	59	55	this	this	DET
fcis-30674	59	56	moment	moment	NOUN
fcis-30674	59	57	a	a	DET
fcis-30674	59	58	reverse	reverse	ADJ
fcis-30674	59	59	parameter	parameter	NOUN
fcis-30674	59	60	update	update	NOUN
fcis-30674	59	61	is	be	AUX
fcis-30674	59	62	performed	perform	VERB
fcis-30674	59	63	based	base	VERB
fcis-30674	59	64	on	on	ADP
fcis-30674	59	65	the	the	DET
fcis-30674	59	66	classification	classification	NOUN
fcis-30674	59	67	results	result	NOUN
fcis-30674	59	68	,	,	PUNCT
fcis-30674	59	69	utilizing	utilize	VERB
fcis-30674	59	70	eq	eq	ADP
fcis-30674	59	71	.	.	PUNCT
fcis-30674	60	1	(	(	PUNCT
fcis-30674	60	2	4	4	NUM
fcis-30674	60	3	)	)	PUNCT
fcis-30674	60	4	,	,	PUNCT
fcis-30674	60	5	with	with	ADP
fcis-30674	60	6	η	η	PROPN
fcis-30674	60	7	being	be	AUX
fcis-30674	60	8	the	the	DET
fcis-30674	60	9	learning	learning	NOUN
fcis-30674	60	10	rate	rate	NOUN
fcis-30674	60	11	:	:	PUNCT
fcis-30674	60	12	,	,	PUNCT
fcis-30674	60	13	,	,	PUNCT
fcis-30674	60	14	,	,	PUNCT
fcis-30674	60	15	(	(	PUNCT
fcis-30674	60	16	4	4	NUM
fcis-30674	60	17	)	)	PUNCT
fcis-30674	60	18	and	and	CCONJ
fcis-30674	60	19	eq	eq	NOUN
fcis-30674	60	20	.	.	PUNCT
fcis-30674	61	1	(	(	PUNCT
fcis-30674	61	2	5	5	NUM
fcis-30674	61	3	)	)	PUNCT
fcis-30674	61	4	,	,	PUNCT
fcis-30674	61	5	with	with	ADP
fcis-30674	61	6	λ	λ	NOUN
fcis-30674	61	7	being	be	AUX
fcis-30674	61	8	the	the	DET
fcis-30674	61	9	proportion	proportion	NOUN
fcis-30674	61	10	of	of	ADP
fcis-30674	61	11	discarded	discard	VERB
fcis-30674	61	12	neurons	neuron	NOUN
fcis-30674	61	13	added	add	VERB
fcis-30674	61	14	to	to	ADP
fcis-30674	61	15	the	the	DET
fcis-30674	61	16	dropout	dropout	NOUN
fcis-30674	61	17	layer	layer	NOUN
fcis-30674	61	18	:	:	PUNCT
fcis-30674	61	19	,	,	PUNCT
fcis-30674	61	20	,	,	PUNCT
fcis-30674	61	21	,	,	PUNCT
fcis-30674	61	22	(	(	PUNCT
fcis-30674	61	23	5	5	NUM
fcis-30674	61	24	)	)	PUNCT
fcis-30674	61	25	in	in	ADP
fcis-30674	61	26	the	the	DET
fcis-30674	61	27	update	update	NOUN
fcis-30674	61	28	parameter	parameter	NOUN
fcis-30674	61	29	equation	equation	NOUN
fcis-30674	61	30	,	,	PUNCT
fcis-30674	61	31	l	l	NOUN
fcis-30674	61	32	represents	represent	VERB
fcis-30674	61	33	the	the	DET
fcis-30674	61	34	crossentropy	crossentropy	NOUN
fcis-30674	61	35	loss	loss	NOUN
fcis-30674	61	36	function	function	NOUN
fcis-30674	61	37	,	,	PUNCT
fcis-30674	61	38	where	where	SCONJ
fcis-30674	61	39	c	c	PROPN
fcis-30674	61	40	is	be	AUX
fcis-30674	61	41	the	the	DET
fcis-30674	61	42	number	number	NOUN
fcis-30674	61	43	of	of	ADP
fcis-30674	61	44	categories	category	NOUN
fcis-30674	61	45	:	:	PUNCT
fcis-30674	61	46	,	,	PUNCT
fcis-30674	61	47	∑	∑	PUNCT
fcis-30674	61	48	⋅	⋅	PROPN
fcis-30674	61	49	log	log	NOUN
fcis-30674	61	50	(	(	PUNCT
fcis-30674	61	51	6	6	NUM
fcis-30674	61	52	)	)	PUNCT
fcis-30674	61	53	after	after	ADP
fcis-30674	61	54	several	several	ADJ
fcis-30674	61	55	rounds	round	NOUN
fcis-30674	61	56	of	of	ADP
fcis-30674	61	57	updating	updating	NOUN
fcis-30674	61	58	,	,	PUNCT
fcis-30674	61	59	the	the	DET
fcis-30674	61	60	final	final	ADJ
fcis-30674	61	61	results	result	NOUN
fcis-30674	61	62	for	for	ADP
fcis-30674	61	63	multiple	multiple	ADJ
fcis-30674	61	64	base	base	NOUN
fcis-30674	61	65	models	model	NOUN
fcis-30674	61	66	are	be	AUX
fcis-30674	61	67	:	:	PUNCT
fcis-30674	61	68	(	(	PUNCT
fcis-30674	61	69	7	7	X
fcis-30674	61	70	)	)	PUNCT
fcis-30674	61	71	a	a	DET
fcis-30674	61	72	final	final	ADJ
fcis-30674	61	73	vote	vote	NOUN
fcis-30674	61	74	was	be	AUX
fcis-30674	61	75	taken	take	VERB
fcis-30674	61	76	to	to	PART
fcis-30674	61	77	obtain	obtain	VERB
fcis-30674	61	78	the	the	DET
fcis-30674	61	79	following	follow	VERB
fcis-30674	61	80	results	result	NOUN
fcis-30674	61	81	:	:	PUNCT
fcis-30674	61	82	,	,	PUNCT
fcis-30674	61	83	…	…	PUNCT
fcis-30674	61	84	,	,	PUNCT
fcis-30674	61	85	(	(	PUNCT
fcis-30674	61	86	8)	8)	NUM
fcis-30674	61	87	the	the	DET
fcis-30674	61	88	gradient	gradient	ADJ
fcis-30674	61	89	optimization	optimization	NOUN
fcis-30674	61	90	method	method	NOUN
fcis-30674	61	91	used	use	VERB
fcis-30674	61	92	in	in	ADP
fcis-30674	61	93	this	this	DET
fcis-30674	61	94	algorithm	algorithm	NOUN
fcis-30674	61	95	updates	update	VERB
fcis-30674	61	96	the	the	DET
fcis-30674	61	97	model	model	NOUN
fcis-30674	61	98	parameters	parameter	NOUN
fcis-30674	61	99	by	by	ADP
fcis-30674	61	100	optimizing	optimize	VERB
fcis-30674	61	101	the	the	DET
fcis-30674	61	102	loss	loss	NOUN
fcis-30674	61	103	function	function	NOUN
fcis-30674	61	104	,	,	PUNCT
fcis-30674	61	105	effectively	effectively	ADV
fcis-30674	61	106	accelerating	accelerate	VERB
fcis-30674	61	107	the	the	DET
fcis-30674	61	108	model	model	NOUN
fcis-30674	61	109	learning	learning	NOUN
fcis-30674	61	110	process	process	NOUN
fcis-30674	61	111	.	.	PUNCT
fcis-30674	62	1	the	the	DET
fcis-30674	62	2	introduction	introduction	NOUN
fcis-30674	62	3	of	of	ADP
fcis-30674	62	4	dropout	dropout	NOUN
fcis-30674	62	5	regularization	regularization	NOUN
fcis-30674	62	6	method	method	NOUN
fcis-30674	62	7	is	be	AUX
fcis-30674	62	8	added	add	VERB
fcis-30674	62	9	to	to	PART
fcis-30674	62	10	reduce	reduce	VERB
fcis-30674	62	11	the	the	DET
fcis-30674	62	12	risk	risk	NOUN
fcis-30674	62	13	of	of	ADP
fcis-30674	62	14	overfitting	overfitte	VERB
fcis-30674	62	15	and	and	CCONJ
fcis-30674	62	16	make	make	VERB
fcis-30674	62	17	each	each	DET
fcis-30674	62	18	base	base	NOUN
fcis-30674	62	19	learner	learner	NOUN
fcis-30674	62	20	more	more	ADV
fcis-30674	62	21	independent	independent	ADJ
fcis-30674	62	22	.	.	PUNCT
fcis-30674	63	1	voting	voting	NOUN
fcis-30674	63	2	mechanism	mechanism	NOUN
fcis-30674	63	3	is	be	AUX
fcis-30674	63	4	used	use	VERB
fcis-30674	63	5	as	as	ADP
fcis-30674	63	6	a	a	DET
fcis-30674	63	7	core	core	NOUN
fcis-30674	63	8	part	part	NOUN
fcis-30674	63	9	to	to	PART
fcis-30674	63	10	enhance	enhance	VERB
fcis-30674	63	11	robustness	robustness	NOUN
fcis-30674	63	12	.	.	PUNCT
fcis-30674	64	1	combining	combine	VERB
fcis-30674	64	2	multiple	multiple	ADJ
fcis-30674	64	3	strategies	strategy	NOUN
fcis-30674	64	4	enhances	enhance	VERB
fcis-30674	64	5	the	the	DET
fcis-30674	64	6	final	final	ADJ
fcis-30674	64	7	decision	decision	NOUN
fcis-30674	64	8	accuracy	accuracy	NOUN
fcis-30674	64	9	.	.	PUNCT
fcis-30674	65	1	213	213	NUM
fcis-30674	65	2	4	4	NUM
fcis-30674	65	3	.	.	PUNCT
fcis-30674	65	4	experimental	experimental	ADJ
fcis-30674	65	5	design	design	NOUN
fcis-30674	65	6	and	and	CCONJ
fcis-30674	65	7	experimental	experimental	ADJ
fcis-30674	65	8	results	result	NOUN
fcis-30674	65	9	4.1	4.1	NUM
fcis-30674	65	10	.	.	PUNCT
fcis-30674	66	1	experimental	experimental	ADJ
fcis-30674	66	2	details	detail	NOUN
fcis-30674	66	3	4.1.1	4.1.1	PROPN
fcis-30674	66	4	.	.	PUNCT
fcis-30674	67	1	dataset	dataset	VERB
fcis-30674	67	2	six	six	NUM
fcis-30674	67	3	synthetic	synthetic	ADJ
fcis-30674	67	4	datasets	dataset	NOUN
fcis-30674	67	5	and	and	CCONJ
fcis-30674	67	6	four	four	NUM
fcis-30674	67	7	real	real	ADJ
fcis-30674	67	8	datasets	dataset	NOUN
fcis-30674	67	9	generated	generate	VERB
fcis-30674	67	10	with	with	ADP
fcis-30674	67	11	moa	moa	NOUN
fcis-30674	67	12	stream	stream	NOUN
fcis-30674	67	13	generator	generator	NOUN
fcis-30674	67	14	are	be	AUX
fcis-30674	67	15	selected	select	VERB
fcis-30674	67	16	.	.	PUNCT
fcis-30674	68	1	the	the	DET
fcis-30674	68	2	dataset	dataset	ADJ
fcis-30674	68	3	information	information	NOUN
fcis-30674	68	4	is	be	AUX
fcis-30674	68	5	in	in	ADP
fcis-30674	68	6	table	table	NOUN
fcis-30674	68	7	1	1	NUM
fcis-30674	68	8	.	.	PUNCT
fcis-30674	68	9	table	table	NOUN
fcis-30674	68	10	1	1	NUM
fcis-30674	68	11	.	.	PUNCT
fcis-30674	69	1	the	the	DET
fcis-30674	69	2	dataset	dataset	NOUN
fcis-30674	69	3	used	use	VERB
fcis-30674	69	4	for	for	ADP
fcis-30674	69	5	the	the	DET
fcis-30674	69	6	experiment	experiment	NOUN
fcis-30674	69	7	dataset	dataset	NOUN
fcis-30674	69	8	sample	sample	NOUN
fcis-30674	69	9	/k	/k	PUNCT
fcis-30674	69	10	feature	feature	NOUN
fcis-30674	69	11	dimension	dimension	NOUN
fcis-30674	69	12	class	class	NOUN
fcis-30674	69	13	label	label	NOUN
fcis-30674	69	14	drift	drift	NOUN
fcis-30674	69	15	type	type	NOUN
fcis-30674	69	16	sea	sea	NOUN
fcis-30674	69	17	100	100	NUM
fcis-30674	69	18	3	3	NUM
fcis-30674	69	19	2	2	NUM
fcis-30674	69	20	gradual	gradual	ADJ
fcis-30674	69	21	drift	drift	NOUN
fcis-30674	69	22	hyperplane	hyperplane	NOUN
fcis-30674	69	23	100	100	NUM
fcis-30674	69	24	10	10	NUM
fcis-30674	69	25	2	2	NUM
fcis-30674	69	26	incremental	incremental	ADJ
fcis-30674	69	27	drift	drift	NOUN
fcis-30674	69	28	rbfblips	rbfblip	NOUN
fcis-30674	69	29	100	100	NUM
fcis-30674	69	30	20	20	NUM
fcis-30674	69	31	4	4	NUM
fcis-30674	69	32	abrupt	abrupt	ADJ
fcis-30674	69	33	drift	drift	NOUN
fcis-30674	69	34	led_abrupt	led_abrupt	NOUN
fcis-30674	69	35	100	100	NUM
fcis-30674	69	36	24	24	NUM
fcis-30674	69	37	10	10	NUM
fcis-30674	69	38	abrupt	abrupt	ADJ
fcis-30674	69	39	drift	drift	NOUN
fcis-30674	69	40	led_gradual	led_gradual	ADJ
fcis-30674	69	41	100	100	NUM
fcis-30674	69	42	24	24	NUM
fcis-30674	69	43	10	10	NUM
fcis-30674	69	44	gradual	gradual	ADJ
fcis-30674	69	45	drift	drift	NOUN
fcis-30674	69	46	tree	tree	NOUN
fcis-30674	69	47	100	100	NUM
fcis-30674	69	48	30	30	NUM
fcis-30674	69	49	10	10	NUM
fcis-30674	69	50	abrupt	abrupt	ADJ
fcis-30674	69	51	drift	drift	NOUN
fcis-30674	69	52	electricity	electricity	NOUN
fcis-30674	69	53	45.3	45.3	NUM
fcis-30674	69	54	6	6	NUM
fcis-30674	69	55	2	2	NUM
fcis-30674	69	56	unknow	unknow	PROPN
fcis-30674	69	57	kddcup99	kddcup99	NOUN
fcis-30674	69	58	494	494	NUM
fcis-30674	69	59	41	41	NUM
fcis-30674	69	60	23	23	NUM
fcis-30674	69	61	unknow	unknow	ADJ
fcis-30674	69	62	covertype	covertype	NOUN
fcis-30674	69	63	581	581	NUM
fcis-30674	69	64	54	54	NUM
fcis-30674	69	65	7	7	NUM
fcis-30674	69	66	unknow	unknow	ADJ
fcis-30674	69	67	weather	weather	NOUN
fcis-30674	69	68	95.1	95.1	NUM
fcis-30674	69	69	9	9	NUM
fcis-30674	69	70	3	3	NUM
fcis-30674	69	71	unknow	unknow	PROPN
fcis-30674	69	72	4.1.2	4.1.2	NUM
fcis-30674	69	73	.	.	PUNCT
fcis-30674	69	74	evaluation	evaluation	NOUN
fcis-30674	69	75	criteria	criterion	NOUN
fcis-30674	69	76	this	this	DET
fcis-30674	69	77	paper	paper	NOUN
fcis-30674	69	78	evaluates	evaluate	NOUN
fcis-30674	69	79	and	and	CCONJ
fcis-30674	69	80	analyzes	analyze	VERB
fcis-30674	69	81	the	the	DET
fcis-30674	69	82	performance	performance	NOUN
fcis-30674	69	83	of	of	ADP
fcis-30674	69	84	the	the	DET
fcis-30674	69	85	model	model	NOUN
fcis-30674	69	86	from	from	ADP
fcis-30674	69	87	several	several	ADJ
fcis-30674	69	88	aspects	aspect	NOUN
fcis-30674	69	89	,	,	PUNCT
fcis-30674	69	90	mainly	mainly	ADV
fcis-30674	69	91	including	include	VERB
fcis-30674	69	92	the	the	DET
fcis-30674	69	93	model	model	NOUN
fcis-30674	69	94	's	's	PART
fcis-30674	69	95	performance	performance	NOUN
fcis-30674	69	96	in	in	ADP
fcis-30674	69	97	dealing	deal	VERB
fcis-30674	69	98	with	with	ADP
fcis-30674	69	99	unknown	unknown	ADJ
fcis-30674	69	100	data	datum	NOUN
fcis-30674	69	101	(	(	PUNCT
fcis-30674	69	102	i.e.	i.e.	X
fcis-30674	69	103	,	,	PUNCT
fcis-30674	69	104	generalization	generalization	NOUN
fcis-30674	69	105	ability	ability	NOUN
fcis-30674	69	106	)	)	PUNCT
fcis-30674	69	107	.	.	PUNCT
fcis-30674	70	1	(	(	PUNCT
fcis-30674	70	2	1	1	X
fcis-30674	70	3	)	)	PUNCT
fcis-30674	70	4	average	average	ADJ
fcis-30674	70	5	real	real	ADJ
fcis-30674	70	6	-	-	PUNCT
fcis-30674	70	7	time	time	NOUN
fcis-30674	70	8	accuracy	accuracy	NOUN
fcis-30674	70	9	is	be	AUX
fcis-30674	70	10	a	a	DET
fcis-30674	70	11	measure	measure	NOUN
fcis-30674	70	12	of	of	ADP
fcis-30674	70	13	the	the	DET
fcis-30674	70	14	average	average	ADJ
fcis-30674	70	15	level	level	NOUN
fcis-30674	70	16	of	of	ADP
fcis-30674	70	17	model	model	NOUN
fcis-30674	70	18	performance	performance	NOUN
fcis-30674	70	19	over	over	ADP
fcis-30674	70	20	the	the	DET
fcis-30674	70	21	entire	entire	ADJ
fcis-30674	70	22	time	time	NOUN
fcis-30674	70	23	course	course	NOUN
fcis-30674	70	24	,	,	PUNCT
fcis-30674	70	25	defined	define	VERB
fcis-30674	70	26	as	as	ADP
fcis-30674	70	27	:	:	PUNCT
fcis-30674	70	28	∑	∑	PUNCT
fcis-30674	70	29	(	(	PUNCT
fcis-30674	70	30	9	9	NUM
fcis-30674	70	31	)	)	PUNCT
fcis-30674	70	32	where	where	SCONJ
fcis-30674	70	33	represents	represent	VERB
fcis-30674	70	34	the	the	DET
fcis-30674	70	35	accuracy	accuracy	NOUN
fcis-30674	70	36	of	of	ADP
fcis-30674	70	37	the	the	DET
fcis-30674	70	38	model	model	NOUN
fcis-30674	70	39	at	at	ADP
fcis-30674	70	40	time	time	NOUN
fcis-30674	70	41	node	node	PROPN
fcis-30674	70	42	.	.	PUNCT
fcis-30674	71	1	a	a	DET
fcis-30674	71	2	higher	high	ADJ
fcis-30674	71	3	average	average	ADJ
fcis-30674	71	4	real	real	ADJ
fcis-30674	71	5	-	-	PUNCT
fcis-30674	71	6	time	time	NOUN
fcis-30674	71	7	accuracy	accuracy	NOUN
fcis-30674	71	8	indicates	indicate	VERB
fcis-30674	71	9	a	a	DET
fcis-30674	71	10	better	well	ADJ
fcis-30674	71	11	real	real	ADJ
fcis-30674	71	12	-	-	PUNCT
fcis-30674	71	13	time	time	NOUN
fcis-30674	71	14	classification	classification	NOUN
fcis-30674	71	15	ability	ability	NOUN
fcis-30674	71	16	of	of	ADP
fcis-30674	71	17	the	the	DET
fcis-30674	71	18	model	model	NOUN
fcis-30674	71	19	.	.	PUNCT
fcis-30674	72	1	to	to	PART
fcis-30674	72	2	cope	cope	VERB
fcis-30674	72	3	with	with	ADP
fcis-30674	72	4	datasets	dataset	NOUN
fcis-30674	72	5	with	with	ADP
fcis-30674	72	6	imbalanced	imbalanced	ADJ
fcis-30674	72	7	categories	category	NOUN
fcis-30674	72	8	,	,	PUNCT
fcis-30674	72	9	uses	use	VERB
fcis-30674	72	10	the	the	DET
fcis-30674	72	11	balanced	balanced	ADJ
fcis-30674	72	12	accuracy	accuracy	NOUN
fcis-30674	72	13	score	score	NOUN
fcis-30674	73	1	[	[	X
fcis-30674	73	2	18	18	NUM
fcis-30674	73	3	]	]	PUNCT
fcis-30674	73	4	,	,	PUNCT
fcis-30674	73	5	which	which	PRON
fcis-30674	73	6	calculates	calculate	VERB
fcis-30674	73	7	the	the	DET
fcis-30674	73	8	average	average	NOUN
fcis-30674	73	9	of	of	ADP
fcis-30674	73	10	the	the	DET
fcis-30674	73	11	accuracies	accuracy	NOUN
fcis-30674	73	12	for	for	ADP
fcis-30674	73	13	each	each	DET
fcis-30674	73	14	category	category	NOUN
fcis-30674	73	15	:	:	PUNCT
fcis-30674	73	16	∑	∑	PUNCT
fcis-30674	73	17	∗	∗	X
fcis-30674	73	18	(	(	PUNCT
fcis-30674	73	19	10	10	NUM
fcis-30674	73	20	)	)	PUNCT
fcis-30674	73	21	where	where	SCONJ
fcis-30674	73	22	denotes	denote	VERB
fcis-30674	73	23	the	the	DET
fcis-30674	73	24	total	total	ADJ
fcis-30674	73	25	number	number	NOUN
fcis-30674	73	26	of	of	ADP
fcis-30674	73	27	categories	category	NOUN
fcis-30674	73	28	,	,	PUNCT
fcis-30674	73	29	represents	represent	VERB
fcis-30674	73	30	the	the	DET
fcis-30674	73	31	elements	element	NOUN
fcis-30674	73	32	in	in	ADP
fcis-30674	73	33	row	row	NOUN
fcis-30674	73	34	and	and	CCONJ
fcis-30674	73	35	column	column	NOUN
fcis-30674	73	36	of	of	ADP
fcis-30674	73	37	the	the	DET
fcis-30674	73	38	confusion	confusion	NOUN
fcis-30674	73	39	matrix	matrix	NOUN
fcis-30674	73	40	,	,	PUNCT
fcis-30674	73	41	and	and	CCONJ
fcis-30674	73	42	∗	∗	NOUN
fcis-30674	73	43	refers	refer	VERB
fcis-30674	73	44	to	to	ADP
fcis-30674	73	45	the	the	DET
fcis-30674	73	46	sum	sum	NOUN
fcis-30674	73	47	of	of	ADP
fcis-30674	73	48	all	all	DET
fcis-30674	73	49	elements	element	NOUN
fcis-30674	73	50	in	in	ADP
fcis-30674	73	51	column	column	NOUN
fcis-30674	73	52	i	i	PRON
fcis-30674	73	53	of	of	ADP
fcis-30674	73	54	the	the	DET
fcis-30674	73	55	confusion	confusion	NOUN
fcis-30674	73	56	matrix	matrix	NOUN
fcis-30674	73	57	.	.	PUNCT
fcis-30674	74	1	(	(	PUNCT
fcis-30674	74	2	2	2	X
fcis-30674	74	3	)	)	PUNCT
fcis-30674	74	4	the	the	DET
fcis-30674	74	5	final	final	ADJ
fcis-30674	74	6	cumulative	cumulative	ADJ
fcis-30674	74	7	accuracy	accuracy	NOUN
fcis-30674	74	8	is	be	AUX
fcis-30674	74	9	a	a	DET
fcis-30674	74	10	measure	measure	NOUN
fcis-30674	74	11	of	of	ADP
fcis-30674	74	12	the	the	DET
fcis-30674	74	13	overall	overall	ADJ
fcis-30674	74	14	performance	performance	NOUN
fcis-30674	74	15	of	of	ADP
fcis-30674	74	16	the	the	DET
fcis-30674	74	17	model	model	NOUN
fcis-30674	74	18	over	over	ADP
fcis-30674	74	19	the	the	DET
fcis-30674	74	20	entire	entire	ADJ
fcis-30674	74	21	time	time	NOUN
fcis-30674	74	22	period	period	NOUN
fcis-30674	74	23	and	and	CCONJ
fcis-30674	74	24	is	be	AUX
fcis-30674	74	25	defined	define	VERB
fcis-30674	74	26	as	as	ADP
fcis-30674	74	27	:	:	PUNCT
fcis-30674	74	28	∑	∑	PUNCT
fcis-30674	74	29	(	(	PUNCT
fcis-30674	74	30	11	11	NUM
fcis-30674	74	31	)	)	PUNCT
fcis-30674	74	32	where	where	SCONJ
fcis-30674	74	33	represents	represent	VERB
fcis-30674	74	34	the	the	DET
fcis-30674	74	35	number	number	NOUN
fcis-30674	74	36	of	of	ADP
fcis-30674	74	37	samples	sample	NOUN
fcis-30674	74	38	in	in	ADP
fcis-30674	74	39	the	the	DET
fcis-30674	74	40	data	datum	NOUN
fcis-30674	74	41	block	block	NOUN
fcis-30674	74	42	and	and	CCONJ
fcis-30674	74	43	is	be	AUX
fcis-30674	74	44	the	the	DET
fcis-30674	74	45	number	number	NOUN
fcis-30674	74	46	of	of	ADP
fcis-30674	74	47	samples	sample	NOUN
fcis-30674	74	48	that	that	PRON
fcis-30674	74	49	the	the	DET
fcis-30674	74	50	model	model	NOUN
fcis-30674	74	51	is	be	AUX
fcis-30674	74	52	correct	correct	ADJ
fcis-30674	74	53	at	at	ADP
fcis-30674	74	54	the	the	DET
fcis-30674	74	55	time	time	NOUN
fcis-30674	74	56	node	node	NOUN
fcis-30674	74	57	of	of	ADP
fcis-30674	74	58	moment	moment	NOUN
fcis-30674	74	59	.	.	PUNCT
fcis-30674	75	1	the	the	DET
fcis-30674	75	2	final	final	ADJ
fcis-30674	75	3	cumulative	cumulative	ADJ
fcis-30674	75	4	accuracy	accuracy	NOUN
fcis-30674	75	5	reflects	reflect	VERB
fcis-30674	75	6	the	the	DET
fcis-30674	75	7	overall	overall	ADJ
fcis-30674	75	8	performance	performance	NOUN
fcis-30674	75	9	of	of	ADP
fcis-30674	75	10	the	the	DET
fcis-30674	75	11	model	model	NOUN
fcis-30674	75	12	.	.	PUNCT
fcis-30674	76	1	4.1.3	4.1.3	X
fcis-30674	76	2	.	.	PUNCT
fcis-30674	76	3	experimental	experimental	ADJ
fcis-30674	76	4	environment	environment	NOUN
fcis-30674	76	5	and	and	CCONJ
fcis-30674	76	6	setup	setup	VERB
fcis-30674	76	7	the	the	DET
fcis-30674	76	8	computer	computer	NOUN
fcis-30674	76	9	used	use	VERB
fcis-30674	76	10	for	for	ADP
fcis-30674	76	11	the	the	DET
fcis-30674	76	12	experiments	experiment	NOUN
fcis-30674	76	13	was	be	AUX
fcis-30674	76	14	configured	configure	VERB
fcis-30674	76	15	with	with	ADP
fcis-30674	76	16	an	an	DET
fcis-30674	76	17	intel(r	intel(r	NOUN
fcis-30674	76	18	)	)	PUNCT
fcis-30674	76	19	core	core	NOUN
fcis-30674	76	20	(	(	PUNCT
fcis-30674	76	21	tm	tm	NOUN
fcis-30674	76	22	)	)	PUNCT
fcis-30674	76	23	i9	i9	NOUN
fcis-30674	76	24	-	-	PUNCT
fcis-30674	76	25	13900hx	13900hx	VERB
fcis-30674	76	26	2.20	2.20	NUM
fcis-30674	76	27	ghz	ghz	NOUN
fcis-30674	76	28	processor	processor	NOUN
fcis-30674	76	29	,	,	PUNCT
fcis-30674	76	30	16	16	NUM
fcis-30674	76	31	gb	gb	NOUN
fcis-30674	76	32	of	of	ADP
fcis-30674	76	33	ddr5	ddr5	NOUN
fcis-30674	76	34	ram	ram	NOUN
fcis-30674	76	35	,	,	PUNCT
fcis-30674	76	36	and	and	CCONJ
fcis-30674	76	37	an	an	DET
fcis-30674	76	38	nvidia	nvidia	PROPN
fcis-30674	76	39	geforce	geforce	NOUN
fcis-30674	76	40	rtx	rtx	PROPN
fcis-30674	76	41	4060	4060	NUM
fcis-30674	76	42	laptop	laptop	PROPN
fcis-30674	76	43	8	8	NUM
fcis-30674	76	44	gb	gb	NOUN
fcis-30674	76	45	graphics	graphic	NOUN
fcis-30674	76	46	card	card	NOUN
fcis-30674	76	47	.	.	PUNCT
fcis-30674	77	1	the	the	DET
fcis-30674	77	2	data	data	NOUN
fcis-30674	77	3	block	block	NOUN
fcis-30674	77	4	size	size	NOUN
fcis-30674	77	5	is	be	AUX
fcis-30674	77	6	set	set	VERB
fcis-30674	77	7	to	to	ADP
fcis-30674	77	8	100	100	NUM
fcis-30674	77	9	,	,	PUNCT
fcis-30674	77	10	the	the	DET
fcis-30674	77	11	activation	activation	NOUN
fcis-30674	77	12	function	function	NOUN
fcis-30674	77	13	is	be	AUX
fcis-30674	77	14	chosen	choose	VERB
fcis-30674	77	15	to	to	PART
fcis-30674	77	16	be	be	AUX
fcis-30674	77	17	relu	relu	NOUN
fcis-30674	77	18	,	,	PUNCT
fcis-30674	77	19	the	the	DET
fcis-30674	77	20	learning	learning	NOUN
fcis-30674	77	21	rate	rate	NOUN
fcis-30674	77	22	is	be	AUX
fcis-30674	77	23	fixed	fix	VERB
fcis-30674	77	24	to	to	ADP
fcis-30674	77	25	0.01	0.01	NUM
fcis-30674	77	26	,	,	PUNCT
fcis-30674	77	27	the	the	DET
fcis-30674	77	28	threshold	threshold	NOUN
fcis-30674	77	29	is	be	AUX
fcis-30674	77	30	set	set	VERB
fcis-30674	77	31	to	to	ADP
fcis-30674	77	32	0.8	0.8	NUM
fcis-30674	77	33	,	,	PUNCT
fcis-30674	77	34	the	the	DET
fcis-30674	77	35	number	number	NOUN
fcis-30674	77	36	of	of	ADP
fcis-30674	77	37	base	base	NOUN
fcis-30674	77	38	learners	learner	NOUN
fcis-30674	77	39	is	be	AUX
fcis-30674	77	40	3	3	NUM
fcis-30674	77	41	,	,	PUNCT
fcis-30674	77	42	and	and	CCONJ
fcis-30674	77	43	the	the	DET
fcis-30674	77	44	number	number	NOUN
fcis-30674	77	45	of	of	ADP
fcis-30674	77	46	hidden	hide	VERB
fcis-30674	77	47	layer	layer	NOUN
fcis-30674	77	48	nodes	node	NOUN
fcis-30674	77	49	is	be	AUX
fcis-30674	77	50	100	100	NUM
fcis-30674	77	51	.	.	PUNCT
fcis-30674	78	1	4.2	4.2	NUM
fcis-30674	78	2	.	.	PUNCT
fcis-30674	79	1	experimental	experimental	ADJ
fcis-30674	79	2	results	result	NOUN
fcis-30674	79	3	table	table	NOUN
fcis-30674	79	4	2	2	NUM
fcis-30674	79	5	.	.	PUNCT
fcis-30674	79	6	comparison	comparison	NOUN
fcis-30674	79	7	of	of	ADP
fcis-30674	79	8	average	average	ADJ
fcis-30674	79	9	real	real	ADJ
fcis-30674	79	10	-	-	PUNCT
fcis-30674	79	11	time	time	NOUN
fcis-30674	79	12	accuracy	accuracy	NOUN
fcis-30674	79	13	of	of	ADP
fcis-30674	79	14	different	different	ADJ
fcis-30674	79	15	methods	method	NOUN
fcis-30674	79	16	dataset	dataset	VERB
fcis-30674	79	17	average	average	ADJ
fcis-30674	79	18	real	real	ADJ
fcis-30674	79	19	-	-	PUNCT
fcis-30674	79	20	time	time	NOUN
fcis-30674	79	21	accuracy	accuracy	NOUN
fcis-30674	79	22	(	(	PUNCT
fcis-30674	79	23	ranked	rank	VERB
fcis-30674	79	24	)	)	PUNCT
fcis-30674	80	1	dnn2	dnn2	PROPN
fcis-30674	80	2	dnn4	dnn4	PROPN
fcis-30674	80	3	dnn8	dnn8	PROPN
fcis-30674	80	4	resnet	resnet	PROPN
fcis-30674	80	5	highway	highway	NOUN
fcis-30674	80	6	hbp	hbp	PROPN
fcis-30674	80	7	emvm_atcd	emvm_atcd	PROPN
fcis-30674	80	8	sea	sea	PROPN
fcis-30674	80	9	0.6217(7	0.6217(7	PROPN
fcis-30674	80	10	)	)	PUNCT
fcis-30674	80	11	0.6782(6	0.6782(6	NOUN
fcis-30674	80	12	)	)	PUNCT
fcis-30674	80	13	0.6782(5	0.6782(5	NOUN
fcis-30674	80	14	)	)	PUNCT
fcis-30674	80	15	0.7698(4	0.7698(4	NUM
fcis-30674	80	16	)	)	PUNCT
fcis-30674	80	17	0.8064(2	0.8064(2	NUM
fcis-30674	80	18	)	)	PUNCT
fcis-30674	80	19	0.8056(3	0.8056(3	NOUN
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fcis-30674	80	21	0.8462(1	0.8462(1	NOUN
fcis-30674	80	22	)	)	PUNCT
fcis-30674	80	23	hyperplane	hyperplane	NOUN
fcis-30674	80	24	0.8913(5	0.8913(5	X
fcis-30674	80	25	)	)	PUNCT
fcis-30674	80	26	0.8930(4	0.8930(4	NUM
fcis-30674	80	27	)	)	PUNCT
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fcis-30674	80	29	)	)	PUNCT
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fcis-30674	80	35	)	)	PUNCT
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fcis-30674	80	37	)	)	PUNCT
fcis-30674	80	38	rbfblips	rbfblip	NOUN
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fcis-30674	80	40	)	)	PUNCT
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fcis-30674	80	44	)	)	PUNCT
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fcis-30674	80	46	)	)	PUNCT
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fcis-30674	80	48	)	)	PUNCT
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fcis-30674	80	50	)	)	PUNCT
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fcis-30674	80	52	)	)	PUNCT
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fcis-30674	80	55	)	)	PUNCT
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fcis-30674	80	57	)	)	PUNCT
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fcis-30674	80	59	)	)	PUNCT
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fcis-30674	80	68	led_gradual	led_gradual	ADJ
fcis-30674	80	69	0.6206(3	0.6206(3	PROPN
fcis-30674	80	70	)	)	PUNCT
fcis-30674	80	71	0.6134(5	0.6134(5	PROPN
fcis-30674	80	72	)	)	PUNCT
fcis-30674	80	73	0.5873(8	0.5873(8	PROPN
fcis-30674	80	74	)	)	PUNCT
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fcis-30674	80	78	)	)	PUNCT
fcis-30674	80	79	0.6191(4	0.6191(4	X
fcis-30674	80	80	)	)	PUNCT
fcis-30674	80	81	0.6310(1	0.6310(1	X
fcis-30674	80	82	)	)	PUNCT
fcis-30674	80	83	tree	tree	NOUN
fcis-30674	80	84	0.3301(3	0.3301(3	NOUN
fcis-30674	80	85	)	)	PUNCT
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fcis-30674	80	87	)	)	PUNCT
fcis-30674	80	88	0.2378(8	0.2378(8	NUM
fcis-30674	80	89	)	)	PUNCT
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fcis-30674	80	91	)	)	PUNCT
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fcis-30674	81	2	)	)	PUNCT
fcis-30674	81	3	0.2799(7	0.2799(7	NOUN
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fcis-30674	81	5	0.6007(1	0.6007(1	NOUN
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fcis-30674	84	5	kddcup99	kddcup99	NOUN
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fcis-30674	84	7	)	)	PUNCT
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fcis-30674	86	3	0.9379(1	0.9379(1	NUM
fcis-30674	86	4	)	)	PUNCT
fcis-30674	87	1	covertype	covertype	NOUN
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fcis-30674	87	3	)	)	PUNCT
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fcis-30674	87	5	)	)	PUNCT
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fcis-30674	87	10	0.6354(3	0.6354(3	SYM
fcis-30674	87	11	)	)	PUNCT
fcis-30674	87	12	0.6465(2	0.6465(2	NUM
fcis-30674	87	13	)	)	PUNCT
fcis-30674	87	14	0.8452(1	0.8452(1	NOUN
fcis-30674	87	15	)	)	PUNCT
fcis-30674	87	16	weather	weather	NOUN
fcis-30674	87	17	0.8478(2	0.8478(2	NOUN
fcis-30674	87	18	)	)	PUNCT
fcis-30674	87	19	0.8050(6	0.8050(6	NUM
fcis-30674	87	20	)	)	PUNCT
fcis-30674	87	21	0.8057(5	0.8057(5	NUM
fcis-30674	87	22	)	)	PUNCT
fcis-30674	87	23	0.8034(7	0.8034(7	NOUN
fcis-30674	87	24	)	)	PUNCT
fcis-30674	87	25	0.7813(8	0.7813(8	NOUN
fcis-30674	87	26	)	)	PUNCT
fcis-30674	87	27	0.8139(3	0.8139(3	NUM
fcis-30674	87	28	)	)	PUNCT
fcis-30674	87	29	0.8393(1	0.8393(1	X
fcis-30674	87	30	)	)	PUNCT
fcis-30674	87	31	average	average	NOUN
fcis-30674	87	32	ranking	rank	VERB
fcis-30674	87	33	3.7	3.7	NUM
fcis-30674	87	34	5.1	5.1	NUM
fcis-30674	87	35	6.2	6.2	NUM
fcis-30674	87	36	6.2	6.2	NUM
fcis-30674	87	37	4.2	4.2	NUM
fcis-30674	87	38	3.7	3.7	NUM
fcis-30674	87	39	1.0	1.0	NUM
fcis-30674	87	40	table	table	NOUN
fcis-30674	87	41	2	2	NUM
fcis-30674	87	42	shows	show	VERB
fcis-30674	87	43	the	the	DET
fcis-30674	87	44	average	average	ADJ
fcis-30674	87	45	real	real	ADJ
fcis-30674	87	46	-	-	PUNCT
fcis-30674	87	47	time	time	NOUN
fcis-30674	87	48	accuracies	accuracy	NOUN
fcis-30674	87	49	and	and	CCONJ
fcis-30674	87	50	rankings	ranking	NOUN
fcis-30674	87	51	of	of	ADP
fcis-30674	87	52	several	several	ADJ
fcis-30674	87	53	models	model	NOUN
fcis-30674	87	54	on	on	ADP
fcis-30674	87	55	different	different	ADJ
fcis-30674	87	56	datasets.the	datasets.the	DET
fcis-30674	87	57	emvm_atcd	emvm_atcd	NOUN
fcis-30674	87	58	method	method	NOUN
fcis-30674	87	59	performs	perform	VERB
fcis-30674	87	60	well	well	ADV
fcis-30674	87	61	on	on	ADP
fcis-30674	87	62	all	all	DET
fcis-30674	87	63	datasets	dataset	NOUN
fcis-30674	87	64	,	,	PUNCT
fcis-30674	87	65	and	and	CCONJ
fcis-30674	87	66	the	the	DET
fcis-30674	87	67	average	average	ADJ
fcis-30674	87	68	real	real	ADJ
fcis-30674	87	69	-	-	PUNCT
fcis-30674	87	70	time	time	NOUN
fcis-30674	87	71	accuracy	accuracy	NOUN
fcis-30674	87	72	ranking	ranking	NOUN
fcis-30674	87	73	is	be	AUX
fcis-30674	87	74	always	always	ADV
fcis-30674	87	75	in	in	ADP
fcis-30674	87	76	the	the	DET
fcis-30674	87	77	first	first	ADJ
fcis-30674	87	78	place	place	NOUN
fcis-30674	87	79	,	,	PUNCT
fcis-30674	87	80	with	with	ADP
fcis-30674	87	81	its	its	PRON
fcis-30674	87	82	stability	stability	NOUN
fcis-30674	87	83	and	and	CCONJ
fcis-30674	87	84	adaptability	adaptability	NOUN
fcis-30674	87	85	.	.	PUNCT
fcis-30674	88	1	its	its	PRON
fcis-30674	88	2	overall	overall	ADJ
fcis-30674	88	3	average	average	ADJ
fcis-30674	88	4	ranking	ranking	NOUN
fcis-30674	88	5	is	be	AUX
fcis-30674	88	6	1.0	1.0	NUM
fcis-30674	88	7	,	,	PUNCT
fcis-30674	88	8	indicating	indicate	VERB
fcis-30674	88	9	that	that	SCONJ
fcis-30674	88	10	its	its	PRON
fcis-30674	88	11	combined	combined	ADJ
fcis-30674	88	12	performance	performance	NOUN
fcis-30674	88	13	on	on	ADP
fcis-30674	88	14	multiple	multiple	ADJ
fcis-30674	88	15	214	214	NUM
fcis-30674	88	16	datasets	dataset	NOUN
fcis-30674	88	17	far	far	ADV
fcis-30674	88	18	exceeds	exceed	VERB
fcis-30674	88	19	that	that	PRON
fcis-30674	88	20	of	of	ADP
fcis-30674	88	21	other	other	ADJ
fcis-30674	88	22	models	model	NOUN
fcis-30674	88	23	.	.	PUNCT
fcis-30674	89	1	the	the	DET
fcis-30674	89	2	method	method	NOUN
fcis-30674	89	3	's	's	PART
fcis-30674	89	4	relatively	relatively	ADV
fcis-30674	89	5	good	good	ADJ
fcis-30674	89	6	performance	performance	NOUN
fcis-30674	89	7	and	and	CCONJ
fcis-30674	89	8	advantages	advantage	NOUN
fcis-30674	89	9	in	in	ADP
fcis-30674	89	10	datasets	dataset	NOUN
fcis-30674	89	11	such	such	ADJ
fcis-30674	89	12	as	as	ADP
fcis-30674	89	13	sea	sea	NOUN
fcis-30674	89	14	,	,	PUNCT
fcis-30674	89	15	hyperplane	hyperplane	NOUN
fcis-30674	89	16	,	,	PUNCT
fcis-30674	89	17	and	and	CCONJ
fcis-30674	89	18	rbfblips	rbfblip	NOUN
fcis-30674	89	19	are	be	AUX
fcis-30674	89	20	especially	especially	ADV
fcis-30674	89	21	obvious	obvious	ADJ
fcis-30674	89	22	,	,	PUNCT
fcis-30674	89	23	and	and	CCONJ
fcis-30674	89	24	emvm_atcd	emvm_atcd	NOUN
fcis-30674	89	25	is	be	AUX
fcis-30674	89	26	able	able	ADJ
fcis-30674	89	27	to	to	PART
fcis-30674	89	28	capture	capture	VERB
fcis-30674	89	29	and	and	CCONJ
fcis-30674	89	30	adapt	adapt	VERB
fcis-30674	89	31	to	to	ADP
fcis-30674	89	32	their	their	PRON
fcis-30674	89	33	changes	change	NOUN
fcis-30674	89	34	better	well	ADV
fcis-30674	89	35	.	.	PUNCT
fcis-30674	90	1	the	the	DET
fcis-30674	90	2	average	average	ADJ
fcis-30674	90	3	ordinal	ordinal	ADJ
fcis-30674	90	4	value	value	NOUN
fcis-30674	90	5	is	be	AUX
fcis-30674	90	6	for	for	SCONJ
fcis-30674	90	7	the	the	DET
fcis-30674	90	8	j	j	PROPN
fcis-30674	90	9	-	-	PUNCT
fcis-30674	90	10	th	th	VERB
fcis-30674	90	11	algorithm	algorithm	NOUN
fcis-30674	90	12	,	,	PUNCT
fcis-30674	90	13	ranked	rank	VERB
fcis-30674	90	14	on	on	ADP
fcis-30674	90	15	the	the	DET
fcis-30674	90	16	dataset	dataset	NOUN
fcis-30674	90	17	,	,	PUNCT
fcis-30674	90	18	the	the	DET
fcis-30674	90	19	average	average	ADJ
fcis-30674	90	20	ordinal	ordinal	ADJ
fcis-30674	90	21	value	value	NOUN
fcis-30674	90	22	∑	∑	PUNCT
fcis-30674	90	23	of	of	ADP
fcis-30674	90	24	the	the	DET
fcis-30674	90	25	j	j	PROPN
fcis-30674	90	26	-	-	PUNCT
fcis-30674	90	27	th	th	VERB
fcis-30674	90	28	algorithm	algorithm	NOUN
fcis-30674	90	29	.	.	PUNCT
fcis-30674	91	1	table	table	NOUN
fcis-30674	91	2	3	3	NUM
fcis-30674	91	3	demonstrates	demonstrate	VERB
fcis-30674	91	4	the	the	DET
fcis-30674	91	5	comparison	comparison	NOUN
fcis-30674	91	6	of	of	ADP
fcis-30674	91	7	the	the	DET
fcis-30674	91	8	final	final	ADJ
fcis-30674	91	9	cumulative	cumulative	ADJ
fcis-30674	91	10	accuracies	accuracy	NOUN
fcis-30674	91	11	of	of	ADP
fcis-30674	91	12	the	the	DET
fcis-30674	91	13	different	different	ADJ
fcis-30674	91	14	methods	method	NOUN
fcis-30674	91	15	on	on	ADP
fcis-30674	91	16	each	each	DET
fcis-30674	91	17	dataset	dataset	NOUN
fcis-30674	91	18	,	,	PUNCT
fcis-30674	91	19	as	as	ADV
fcis-30674	91	20	well	well	ADV
fcis-30674	91	21	as	as	ADP
fcis-30674	91	22	the	the	DET
fcis-30674	91	23	corresponding	corresponding	ADJ
fcis-30674	91	24	rankings	ranking	NOUN
fcis-30674	91	25	.	.	PUNCT
fcis-30674	92	1	taken	take	VERB
fcis-30674	92	2	together	together	ADV
fcis-30674	92	3	,	,	PUNCT
fcis-30674	92	4	the	the	DET
fcis-30674	92	5	emvm_atcd	emvm_atcd	NOUN
fcis-30674	92	6	method	method	NOUN
fcis-30674	92	7	performs	perform	VERB
fcis-30674	92	8	outstandingly	outstandingly	ADV
fcis-30674	92	9	on	on	ADP
fcis-30674	92	10	all	all	DET
fcis-30674	92	11	datasets	dataset	NOUN
fcis-30674	92	12	with	with	ADP
fcis-30674	92	13	an	an	DET
fcis-30674	92	14	average	average	ADJ
fcis-30674	92	15	ranking	ranking	NOUN
fcis-30674	92	16	of	of	ADP
fcis-30674	92	17	1.5	1.5	NUM
fcis-30674	92	18	,	,	PUNCT
fcis-30674	92	19	which	which	PRON
fcis-30674	92	20	is	be	AUX
fcis-30674	92	21	significantly	significantly	ADV
fcis-30674	92	22	better	well	ADJ
fcis-30674	92	23	than	than	ADP
fcis-30674	92	24	the	the	DET
fcis-30674	92	25	other	other	ADJ
fcis-30674	92	26	methods	method	NOUN
fcis-30674	92	27	.	.	PUNCT
fcis-30674	93	1	the	the	DET
fcis-30674	93	2	datasets	datasets	PROPN
fcis-30674	93	3	sea	sea	PROPN
fcis-30674	93	4	,	,	PUNCT
fcis-30674	93	5	hyperplane	hyperplane	PROPN
fcis-30674	93	6	,	,	PUNCT
fcis-30674	93	7	rbfblips	rbfblip	NOUN
fcis-30674	93	8	,	,	PUNCT
fcis-30674	93	9	and	and	CCONJ
fcis-30674	93	10	led_abrupt	led_abrupt	NOUN
fcis-30674	93	11	,	,	PUNCT
fcis-30674	93	12	etc	etc	X
fcis-30674	93	13	.	.	X
fcis-30674	93	14	,	,	PUNCT
fcis-30674	93	15	on	on	ADP
fcis-30674	93	16	which	which	PRON
fcis-30674	93	17	the	the	DET
fcis-30674	93	18	emvm_atcd	emvm_atcd	NOUN
fcis-30674	93	19	method	method	NOUN
fcis-30674	93	20	performs	perform	VERB
fcis-30674	93	21	particularly	particularly	ADV
fcis-30674	93	22	well	well	ADV
fcis-30674	93	23	,	,	PUNCT
fcis-30674	93	24	all	all	PRON
fcis-30674	93	25	of	of	ADP
fcis-30674	93	26	them	they	PRON
fcis-30674	93	27	obtaining	obtain	VERB
fcis-30674	93	28	high	high	ADJ
fcis-30674	93	29	final	final	ADJ
fcis-30674	93	30	cumulative	cumulative	ADJ
fcis-30674	93	31	accuracies	accuracy	NOUN
fcis-30674	93	32	and	and	CCONJ
fcis-30674	93	33	rankings	ranking	NOUN
fcis-30674	93	34	,	,	PUNCT
fcis-30674	93	35	which	which	PRON
fcis-30674	93	36	proves	prove	VERB
fcis-30674	93	37	that	that	SCONJ
fcis-30674	93	38	the	the	DET
fcis-30674	93	39	method	method	NOUN
fcis-30674	93	40	is	be	AUX
fcis-30674	93	41	highly	highly	ADV
fcis-30674	93	42	adaptable	adaptable	ADJ
fcis-30674	93	43	and	and	CCONJ
fcis-30674	93	44	effective	effective	ADJ
fcis-30674	93	45	in	in	ADP
fcis-30674	93	46	dealing	deal	VERB
fcis-30674	93	47	with	with	ADP
fcis-30674	93	48	these	these	DET
fcis-30674	93	49	datasets	dataset	NOUN
fcis-30674	93	50	.	.	PUNCT
fcis-30674	93	51	table	table	NOUN
fcis-30674	93	52	3	3	NUM
fcis-30674	93	53	.	.	PUNCT
fcis-30674	93	54	comparison	comparison	NOUN
fcis-30674	93	55	of	of	ADP
fcis-30674	93	56	final	final	ADJ
fcis-30674	93	57	cumulative	cumulative	ADJ
fcis-30674	93	58	accuracies	accuracy	NOUN
fcis-30674	93	59	of	of	ADP
fcis-30674	93	60	different	different	ADJ
fcis-30674	93	61	methods	method	NOUN
fcis-30674	93	62	dataset	dataset	VERB
fcis-30674	93	63	final	final	ADJ
fcis-30674	93	64	cumulative	cumulative	ADJ
fcis-30674	93	65	accuracy	accuracy	NOUN
fcis-30674	93	66	(	(	PUNCT
fcis-30674	93	67	ranked	rank	VERB
fcis-30674	93	68	)	)	PUNCT
fcis-30674	93	69	dnn2	dnn2	PROPN
fcis-30674	93	70	dnn4	dnn4	PROPN
fcis-30674	93	71	dnn8	dnn8	PROPN
fcis-30674	93	72	resnet	resnet	PROPN
fcis-30674	93	73	highway	highway	NOUN
fcis-30674	93	74	hbp	hbp	PROPN
fcis-30674	93	75	emvm_atcd	emvm_atcd	PROPN
fcis-30674	93	76	sea	sea	PROPN
fcis-30674	93	77	0.6579(7	0.6579(7	PROPN
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fcis-30674	93	91	hyperplane	hyperplane	NOUN
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fcis-30674	95	5	)	)	PUNCT
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fcis-30674	100	12	0.9823(2	0.9823(2	NUM
fcis-30674	100	13	)	)	PUNCT
fcis-30674	101	1	0.9811(3	0.9811(3	NUM
fcis-30674	101	2	)	)	PUNCT
fcis-30674	102	1	covertype	covertype	NOUN
fcis-30674	102	2	0.6984(7	0.6984(7	NOUN
fcis-30674	102	3	)	)	PUNCT
fcis-30674	102	4	0.7336(6	0.7336(6	NOUN
fcis-30674	102	5	)	)	PUNCT
fcis-30674	102	6	0.7677(5	0.7677(5	PROPN
fcis-30674	102	7	)	)	PUNCT
fcis-30674	102	8	0.7730(4	0.7730(4	NUM
fcis-30674	102	9	)	)	PUNCT
fcis-30674	102	10	0.7824(2	0.7824(2	NUM
fcis-30674	102	11	)	)	PUNCT
fcis-30674	102	12	0.7903(1	0.7903(1	NUM
fcis-30674	102	13	)	)	PUNCT
fcis-30674	102	14	0.7762(3	0.7762(3	X
fcis-30674	102	15	)	)	PUNCT
fcis-30674	102	16	weather	weather	NOUN
fcis-30674	102	17	0.8872(1	0.8872(1	NUM
fcis-30674	102	18	)	)	PUNCT
fcis-30674	102	19	0.8743(5	0.8743(5	NOUN
fcis-30674	102	20	)	)	PUNCT
fcis-30674	102	21	0.8754(4	0.8754(4	NOUN
fcis-30674	102	22	)	)	PUNCT
fcis-30674	102	23	0.8708(6	0.8708(6	NOUN
fcis-30674	102	24	)	)	PUNCT
fcis-30674	102	25	0.8362(7	0.8362(7	NOUN
fcis-30674	102	26	)	)	PUNCT
fcis-30674	102	27	0.8824(3	0.8824(3	NUM
fcis-30674	102	28	)	)	PUNCT
fcis-30674	102	29	0.8835(2	0.8835(2	NUM
fcis-30674	102	30	)	)	PUNCT
fcis-30674	102	31	average	average	NOUN
fcis-30674	102	32	ranking	rank	VERB
fcis-30674	102	33	3.8	3.8	NUM
fcis-30674	102	34	5.0	5.0	NUM
fcis-30674	102	35	6.5	6.5	NUM
fcis-30674	102	36	6.2	6.2	NUM
fcis-30674	102	37	4.2	4.2	NUM
fcis-30674	102	38	3.7	3.7	NUM
fcis-30674	102	39	1.5	1.5	NUM
fcis-30674	102	40	5	5	NUM
fcis-30674	102	41	.	.	PUNCT
fcis-30674	103	1	conclusion	conclusion	NOUN
fcis-30674	103	2	classical	classical	ADJ
fcis-30674	103	3	algorithms	algorithm	NOUN
fcis-30674	103	4	have	have	VERB
fcis-30674	103	5	average	average	ADJ
fcis-30674	103	6	performance	performance	NOUN
fcis-30674	103	7	when	when	SCONJ
fcis-30674	103	8	computing	compute	VERB
fcis-30674	103	9	data	datum	NOUN
fcis-30674	103	10	containing	contain	VERB
fcis-30674	103	11	conceptual	conceptual	ADJ
fcis-30674	103	12	drift	drift	NOUN
fcis-30674	103	13	,	,	PUNCT
fcis-30674	103	14	an	an	DET
fcis-30674	103	15	integrated	integrate	VERB
fcis-30674	103	16	multi	multi	ADJ
fcis-30674	103	17	-	-	ADJ
fcis-30674	103	18	model	model	ADJ
fcis-30674	103	19	voting	voting	NOUN
fcis-30674	103	20	method	method	NOUN
fcis-30674	103	21	adapted	adapt	VERB
fcis-30674	103	22	to	to	ADP
fcis-30674	103	23	conceptual	conceptual	ADJ
fcis-30674	103	24	drift	drift	NOUN
fcis-30674	103	25	is	be	AUX
fcis-30674	103	26	proposed	propose	VERB
fcis-30674	103	27	to	to	PART
fcis-30674	103	28	cope	cope	VERB
fcis-30674	103	29	with	with	ADP
fcis-30674	103	30	the	the	DET
fcis-30674	103	31	conceptual	conceptual	ADJ
fcis-30674	103	32	drift	drift	NOUN
fcis-30674	103	33	problem	problem	NOUN
fcis-30674	103	34	by	by	ADP
fcis-30674	103	35	integrating	integrate	VERB
fcis-30674	103	36	a	a	DET
fcis-30674	103	37	single	single	ADJ
fcis-30674	103	38	hidden	hide	VERB
fcis-30674	103	39	-	-	PUNCT
fcis-30674	103	40	layer	layer	NOUN
fcis-30674	103	41	neural	neural	ADJ
fcis-30674	103	42	network	network	NOUN
fcis-30674	103	43	to	to	PART
fcis-30674	103	44	take	take	VERB
fcis-30674	103	45	advantage	advantage	NOUN
fcis-30674	103	46	of	of	ADP
fcis-30674	103	47	multi	multi	NOUN
fcis-30674	103	48	-	-	NOUN
fcis-30674	103	49	models	model	NOUN
fcis-30674	103	50	and	and	CCONJ
fcis-30674	103	51	to	to	PART
fcis-30674	103	52	improve	improve	VERB
fcis-30674	103	53	accuracy	accuracy	NOUN
fcis-30674	103	54	in	in	ADP
fcis-30674	103	55	the	the	DET
fcis-30674	103	56	changing	change	VERB
fcis-30674	103	57	environment	environment	NOUN
fcis-30674	103	58	of	of	ADP
fcis-30674	103	59	data	datum	NOUN
fcis-30674	103	60	.	.	PUNCT
fcis-30674	104	1	the	the	DET
fcis-30674	104	2	neural	neural	ADJ
fcis-30674	104	3	network	network	NOUN
fcis-30674	104	4	has	have	VERB
fcis-30674	104	5	a	a	DET
fcis-30674	104	6	simple	simple	ADJ
fcis-30674	104	7	structure	structure	NOUN
fcis-30674	104	8	,	,	PUNCT
fcis-30674	104	9	which	which	PRON
fcis-30674	104	10	can	can	AUX
fcis-30674	104	11	save	save	VERB
fcis-30674	104	12	a	a	DET
fcis-30674	104	13	lot	lot	NOUN
fcis-30674	104	14	of	of	ADP
fcis-30674	104	15	computational	computational	ADJ
fcis-30674	104	16	resources	resource	NOUN
fcis-30674	104	17	,	,	PUNCT
fcis-30674	104	18	and	and	CCONJ
fcis-30674	104	19	is	be	AUX
fcis-30674	104	20	able	able	ADJ
fcis-30674	104	21	to	to	PART
fcis-30674	104	22	quickly	quickly	ADV
fcis-30674	104	23	learn	learn	VERB
fcis-30674	104	24	and	and	CCONJ
fcis-30674	104	25	adapt	adapt	VERB
fcis-30674	104	26	to	to	ADP
fcis-30674	104	27	changes	change	NOUN
fcis-30674	104	28	in	in	ADP
fcis-30674	104	29	data	datum	NOUN
fcis-30674	104	30	distribution	distribution	NOUN
fcis-30674	104	31	.	.	PUNCT
fcis-30674	105	1	a	a	DET
fcis-30674	105	2	voting	voting	NOUN
fcis-30674	105	3	mechanism	mechanism	NOUN
fcis-30674	105	4	is	be	AUX
fcis-30674	105	5	used	use	VERB
fcis-30674	105	6	along	along	ADP
fcis-30674	105	7	with	with	ADP
fcis-30674	105	8	weighted	weight	VERB
fcis-30674	105	9	prediction	prediction	NOUN
fcis-30674	105	10	computation	computation	NOUN
fcis-30674	105	11	to	to	PART
fcis-30674	105	12	increase	increase	VERB
fcis-30674	105	13	the	the	DET
fcis-30674	105	14	stability	stability	NOUN
fcis-30674	105	15	of	of	ADP
fcis-30674	105	16	the	the	DET
fcis-30674	105	17	model	model	NOUN
fcis-30674	105	18	and	and	CCONJ
fcis-30674	105	19	overcome	overcome	VERB
fcis-30674	105	20	the	the	DET
fcis-30674	105	21	limitations	limitation	NOUN
fcis-30674	105	22	of	of	ADP
fcis-30674	105	23	a	a	DET
fcis-30674	105	24	single	single	ADJ
fcis-30674	105	25	model	model	NOUN
fcis-30674	105	26	.	.	PUNCT
fcis-30674	106	1	the	the	DET
fcis-30674	106	2	results	result	NOUN
fcis-30674	106	3	show	show	VERB
fcis-30674	106	4	that	that	SCONJ
fcis-30674	106	5	the	the	DET
fcis-30674	106	6	proposed	propose	VERB
fcis-30674	106	7	algorithm	algorithm	NOUN
fcis-30674	106	8	in	in	ADP
fcis-30674	106	9	the	the	DET
fcis-30674	106	10	dataset	dataset	NOUN
fcis-30674	106	11	containing	contain	VERB
fcis-30674	106	12	conceptual	conceptual	ADJ
fcis-30674	106	13	drift	drift	NOUN
fcis-30674	106	14	,	,	PUNCT
fcis-30674	106	15	the	the	DET
fcis-30674	106	16	improvement	improvement	NOUN
fcis-30674	106	17	of	of	ADP
fcis-30674	106	18	the	the	DET
fcis-30674	106	19	average	average	ADJ
fcis-30674	106	20	real	real	ADJ
fcis-30674	106	21	-	-	PUNCT
fcis-30674	106	22	time	time	NOUN
fcis-30674	106	23	accuracy	accuracy	NOUN
fcis-30674	106	24	and	and	CCONJ
fcis-30674	106	25	the	the	DET
fcis-30674	106	26	final	final	ADJ
fcis-30674	106	27	cumulative	cumulative	ADJ
fcis-30674	106	28	accuracy	accuracy	NOUN
fcis-30674	106	29	indicate	indicate	VERB
fcis-30674	106	30	that	that	SCONJ
fcis-30674	106	31	the	the	DET
fcis-30674	106	32	emvm_atcd	emvm_atcd	NOUN
fcis-30674	106	33	method	method	NOUN
fcis-30674	106	34	can	can	AUX
fcis-30674	106	35	flexibly	flexibly	ADV
fcis-30674	106	36	cope	cope	VERB
fcis-30674	106	37	with	with	ADP
fcis-30674	106	38	a	a	DET
fcis-30674	106	39	variety	variety	NOUN
fcis-30674	106	40	of	of	ADP
fcis-30674	106	41	types	type	NOUN
fcis-30674	106	42	of	of	ADP
fcis-30674	106	43	data	datum	NOUN
fcis-30674	106	44	and	and	CCONJ
fcis-30674	106	45	realize	realize	VERB
fcis-30674	106	46	the	the	DET
fcis-30674	106	47	overall	overall	ADJ
fcis-30674	106	48	performance	performance	NOUN
fcis-30674	106	49	improvement	improvement	NOUN
fcis-30674	106	50	.	.	PUNCT
fcis-30674	107	1	references	reference	NOUN
fcis-30674	107	2	[	[	X
fcis-30674	107	3	1	1	NUM
fcis-30674	107	4	]	]	X
fcis-30674	107	5	rutkowski	rutkowski	PROPN
fcis-30674	107	6	l	l	PROPN
fcis-30674	107	7	,	,	PUNCT
fcis-30674	107	8	jaworski	jaworski	PROPN
fcis-30674	107	9	m	m	PROPN
fcis-30674	107	10	,	,	PUNCT
fcis-30674	107	11	duda	duda	PROPN
fcis-30674	107	12	p.stream	p.stream	PROPN
fcis-30674	107	13	data	datum	NOUN
fcis-30674	107	14	mining	mining	NOUN
fcis-30674	107	15	:	:	PUNCT
fcis-30674	107	16	algorithms	algorithm	NOUN
fcis-30674	107	17	and	and	CCONJ
fcis-30674	107	18	their	their	PRON
fcis-30674	107	19	probabilistic	probabilistic	ADJ
fcis-30674	107	20	properties[m	properties[m	NOUN
fcis-30674	107	21	]	]	PUNCT
fcis-30674	107	22	.	.	PUNCT
fcis-30674	108	1	germany	germany	PROPN
fcis-30674	108	2	:	:	PUNCT
fcis-30674	108	3	springer	springer	NOUN
fcis-30674	108	4	publishing	publishing	NOUN
fcis-30674	108	5	company	company	NOUN
fcis-30674	108	6	,	,	PUNCT
fcis-30674	108	7	2020:13	2020:13	NUM
fcis-30674	108	8	-	-	SYM
fcis-30674	108	9	33	33	NUM
fcis-30674	108	10	.	.	PUNCT
fcis-30674	109	1	[	[	X
fcis-30674	109	2	2	2	NUM
fcis-30674	109	3	]	]	X
fcis-30674	109	4	gaber	gaber	PROPN
fcis-30674	109	5	m	m	PROPN
fcis-30674	109	6	m	m	PROPN
fcis-30674	109	7	,	,	PUNCT
fcis-30674	109	8	zaslavsky	zaslavsky	PROPN
fcis-30674	109	9	a	a	PRON
fcis-30674	109	10	,	,	PUNCT
fcis-30674	109	11	krishnaswamy	krishnaswamy	PROPN
fcis-30674	109	12	s.a	s.a	PROPN
fcis-30674	109	13	survey	survey	NOUN
fcis-30674	109	14	of	of	ADP
fcis-30674	109	15	classification	classification	NOUN
fcis-30674	109	16	methods	method	NOUN
fcis-30674	109	17	in	in	ADP
fcis-30674	109	18	data	datum	NOUN
fcis-30674	109	19	streams[j].data	streams[j].data	NOUN
fcis-30674	109	20	streams	stream	NOUN
fcis-30674	109	21	:	:	PUNCT
fcis-30674	109	22	models	model	NOUN
fcis-30674	109	23	and	and	CCONJ
fcis-30674	109	24	algorithms	algorithm	NOUN
fcis-30674	109	25	,	,	PUNCT
fcis-30674	109	26	2007	2007	NUM
fcis-30674	109	27	,	,	PUNCT
fcis-30674	109	28	31	31	NUM
fcis-30674	109	29	:	:	SYM
fcis-30674	109	30	39	39	NUM
fcis-30674	109	31	-	-	SYM
fcis-30674	109	32	59	59	NUM
fcis-30674	109	33	.	.	PUNCT
fcis-30674	110	1	[	[	X
fcis-30674	110	2	3	3	NUM
fcis-30674	110	3	]	]	X
fcis-30674	110	4	lecun	lecun	PROPN
fcis-30674	110	5	y	y	PROPN
fcis-30674	110	6	,	,	PUNCT
fcis-30674	110	7	bottou	bottou	PROPN
fcis-30674	110	8	l	l	PROPN
fcis-30674	110	9	,	,	PUNCT
fcis-30674	110	10	bengio	bengio	PROPN
fcis-30674	110	11	y	y	PROPN
fcis-30674	110	12	,	,	PUNCT
fcis-30674	110	13	et	et	PROPN
fcis-30674	110	14	al	al	PROPN
fcis-30674	110	15	.	.	PUNCT
fcis-30674	110	16	gradient	gradient	NOUN
fcis-30674	110	17	-	-	PUNCT
fcis-30674	110	18	based	base	VERB
fcis-30674	110	19	learning	learning	NOUN
fcis-30674	110	20	applied	apply	VERB
fcis-30674	110	21	to	to	ADP
fcis-30674	110	22	document	document	NOUN
fcis-30674	110	23	recognition[j	recognition[j	NOUN
fcis-30674	110	24	]	]	PUNCT
fcis-30674	110	25	.	.	PUNCT
fcis-30674	111	1	proceedings	proceeding	NOUN
fcis-30674	111	2	of	of	ADP
fcis-30674	111	3	the	the	DET
fcis-30674	111	4	ieee	ieee	NOUN
fcis-30674	111	5	,	,	PUNCT
fcis-30674	111	6	1998	1998	NUM
fcis-30674	111	7	,	,	PUNCT
fcis-30674	111	8	86(11	86(11	NUM
fcis-30674	111	9	):	):	PUNCT
fcis-30674	111	10	2278	2278	NUM
fcis-30674	111	11	-	-	SYM
fcis-30674	111	12	2324	2324	NUM
fcis-30674	111	13	.	.	PUNCT
fcis-30674	112	1	[	[	X
fcis-30674	112	2	4	4	X
fcis-30674	112	3	]	]	X
fcis-30674	112	4	elman	elman	PROPN
fcis-30674	112	5	j	j	PROPN
fcis-30674	112	6	l.	l.	PROPN
fcis-30674	112	7	finding	find	VERB
fcis-30674	112	8	structure	structure	NOUN
fcis-30674	112	9	in	in	ADP
fcis-30674	112	10	time[j	time[j	PROPN
fcis-30674	112	11	]	]	PUNCT
fcis-30674	112	12	.	.	PUNCT
fcis-30674	113	1	cognitive	cognitive	ADJ
fcis-30674	113	2	science	science	NOUN
fcis-30674	113	3	,	,	PUNCT
fcis-30674	113	4	1990	1990	NUM
fcis-30674	113	5	,	,	PUNCT
fcis-30674	113	6	14(2	14(2	NUM
fcis-30674	113	7	):	):	PUNCT
fcis-30674	113	8	179	179	NUM
fcis-30674	113	9	-	-	SYM
fcis-30674	113	10	211	211	NUM
fcis-30674	113	11	.	.	PUNCT
fcis-30674	114	1	[	[	X
fcis-30674	114	2	5	5	X
fcis-30674	114	3	]	]	X
fcis-30674	114	4	hochreiter	hochreiter	PROPN
fcis-30674	114	5	s.	s.	PROPN
fcis-30674	114	6	long	long	PROPN
fcis-30674	114	7	short	short	ADJ
fcis-30674	114	8	-	-	PUNCT
fcis-30674	114	9	term	term	NOUN
fcis-30674	114	10	memory[j	memory[j	NOUN
fcis-30674	114	11	]	]	PUNCT
fcis-30674	114	12	.	.	PUNCT
fcis-30674	115	1	neural	neural	ADJ
fcis-30674	115	2	computation	computation	NOUN
fcis-30674	115	3	mit	mit	NOUN
fcis-30674	115	4	-	-	PUNCT
fcis-30674	115	5	press	press	NOUN
fcis-30674	115	6	,	,	PUNCT
fcis-30674	115	7	1997	1997	NUM
fcis-30674	115	8	,	,	PUNCT
fcis-30674	115	9	9(8	9(8	NUM
fcis-30674	115	10	)	)	PUNCT
fcis-30674	115	11	,	,	PUNCT
fcis-30674	115	12	1735	1735	NUM
fcis-30674	115	13	-	-	SYM
fcis-30674	115	14	1780	1780	NUM
fcis-30674	116	1	.	.	PUNCT
fcis-30674	117	1	[	[	X
fcis-30674	117	2	6	6	NUM
fcis-30674	117	3	]	]	PUNCT
fcis-30674	117	4	krizhevsky	krizhevsky	NOUN
fcis-30674	117	5	a	a	PROPN
fcis-30674	117	6	,	,	PUNCT
fcis-30674	117	7	sutskever	sutskever	VERB
fcis-30674	117	8	i	i	PRON
fcis-30674	117	9	,	,	PUNCT
fcis-30674	117	10	hinton	hinton	PROPN
fcis-30674	117	11	g	g	PROPN
fcis-30674	117	12	e.	e.	PROPN
fcis-30674	117	13	imagenet	imagenet	PROPN
fcis-30674	117	14	classification	classification	NOUN
fcis-30674	117	15	with	with	ADP
fcis-30674	117	16	deep	deep	ADJ
fcis-30674	117	17	convolutional	convolutional	ADJ
fcis-30674	117	18	neural	neural	ADJ
fcis-30674	117	19	networks[j	networks[j	NOUN
fcis-30674	117	20	]	]	X
fcis-30674	117	21	.	.	PUNCT
fcis-30674	118	1	communications	communication	NOUN
fcis-30674	118	2	of	of	ADP
fcis-30674	118	3	the	the	DET
fcis-30674	118	4	acm	acm	NOUN
fcis-30674	118	5	,	,	PUNCT
fcis-30674	118	6	2017	2017	NUM
fcis-30674	118	7	,	,	PUNCT
fcis-30674	118	8	60(6	60(6	NOUN
fcis-30674	118	9	):	):	PUNCT
fcis-30674	118	10	84	84	NUM
fcis-30674	118	11	-	-	SYM
fcis-30674	118	12	90	90	NUM
fcis-30674	118	13	.	.	PUNCT
fcis-30674	119	1	[	[	X
fcis-30674	119	2	7	7	NUM
fcis-30674	119	3	]	]	SYM
fcis-30674	119	4	xu	xu	PROPN
fcis-30674	119	5	,	,	PUNCT
fcis-30674	119	6	r.	r.	PROPN
fcis-30674	119	7	;	;	PUNCT
fcis-30674	119	8	cheng	cheng	PROPN
fcis-30674	119	9	,	,	PUNCT
fcis-30674	119	10	y.	y.	PROPN
fcis-30674	119	11	;	;	PUNCT
fcis-30674	119	12	liu	liu	PROPN
fcis-30674	119	13	,	,	PUNCT
fcis-30674	119	14	z.	z.	PROPN
fcis-30674	119	15	;	;	PUNCT
fcis-30674	119	16	xie	xie	PROPN
fcis-30674	119	17	,	,	PUNCT
fcis-30674	119	18	y.	y.	PROPN
fcis-30674	119	19	;	;	PUNCT
fcis-30674	119	20	yang	yang	PROPN
fcis-30674	119	21	,	,	PUNCT
fcis-30674	119	22	y.	y.	PROPN
fcis-30674	119	23	improved	improve	VERB
fcis-30674	119	24	long	long	ADJ
fcis-30674	119	25	short	short	ADJ
fcis-30674	119	26	-	-	PUNCT
fcis-30674	119	27	term	term	NOUN
fcis-30674	119	28	memory	memory	NOUN
fcis-30674	119	29	based	base	VERB
fcis-30674	119	30	anomaly	anomaly	NOUN
fcis-30674	119	31	detection	detection	NOUN
fcis-30674	119	32	with	with	ADP
fcis-30674	119	33	concept	concept	NOUN
fcis-30674	119	34	drift	drift	NOUN
fcis-30674	119	35	adaptive	adaptive	ADJ
fcis-30674	119	36	method	method	NOUN
fcis-30674	119	37	for	for	ADP
fcis-30674	119	38	supporting	support	VERB
fcis-30674	119	39	iot	iot	PROPN
fcis-30674	119	40	services	service	NOUN
fcis-30674	119	41	.	.	PUNCT
fcis-30674	120	1	futur	futur	PROPN
fcis-30674	120	2	.	.	PUNCT
fcis-30674	120	3	gener	gener	PROPN
fcis-30674	120	4	.	.	PUNCT
fcis-30674	121	1	comput	comput	PROPN
fcis-30674	121	2	.	.	PUNCT
fcis-30674	122	1	syst	syst	PROPN
fcis-30674	122	2	.	.	PUNCT
fcis-30674	123	1	2020	2020	NUM
fcis-30674	123	2	,	,	PUNCT
fcis-30674	123	3	112	112	NUM
fcis-30674	123	4	,	,	PUNCT
fcis-30674	123	5	228–242	228–242	NUM
fcis-30674	123	6	.	.	PUNCT
fcis-30674	124	1	[	[	X
fcis-30674	124	2	8	8	NUM
fcis-30674	124	3	]	]	X
fcis-30674	124	4	soleymani	soleymani	PROPN
fcis-30674	124	5	,	,	PUNCT
fcis-30674	124	6	f.	f.	PROPN
fcis-30674	124	7	;	;	PUNCT
fcis-30674	124	8	paquet	paquet	PROPN
fcis-30674	124	9	,	,	PUNCT
fcis-30674	124	10	e.	e.	PROPN
fcis-30674	124	11	financial	financial	PROPN
fcis-30674	124	12	portfolio	portfolio	PROPN
fcis-30674	124	13	optimization	optimization	NOUN
fcis-30674	124	14	with	with	ADP
fcis-30674	124	15	online	online	ADJ
fcis-30674	124	16	deep	deep	ADJ
fcis-30674	124	17	reinforcement	reinforcement	NOUN
fcis-30674	124	18	learning	learning	NOUN
fcis-30674	124	19	and	and	CCONJ
fcis-30674	124	20	restricted	restrict	VERB
fcis-30674	124	21	stacked	stack	VERB
fcis-30674	124	22	autoencoder	autoencoder	NOUN
fcis-30674	124	23	—	—	PUNCT
fcis-30674	124	24	deepbreath	deepbreath	ADJ
fcis-30674	124	25	.	.	PUNCT
fcis-30674	125	1	expert	expert	NOUN
fcis-30674	125	2	syst	syst	PROPN
fcis-30674	125	3	.	.	PUNCT
fcis-30674	126	1	appl	appl	PROPN
fcis-30674	126	2	.	.	PUNCT
fcis-30674	127	1	2020	2020	NUM
fcis-30674	127	2	,	,	PUNCT
fcis-30674	127	3	156	156	NUM
fcis-30674	127	4	,	,	PUNCT
fcis-30674	127	5	113456	113456	NUM
fcis-30674	127	6	.	.	PUNCT
fcis-30674	128	1	[	[	X
fcis-30674	128	2	9	9	NUM
fcis-30674	128	3	]	]	X
fcis-30674	128	4	wang	wang	PROPN
fcis-30674	128	5	,	,	PUNCT
fcis-30674	128	6	x.	x.	PROPN
fcis-30674	128	7	;	;	PUNCT
fcis-30674	128	8	chen	chen	PROPN
fcis-30674	128	9	,	,	PUNCT
fcis-30674	128	10	w.	w.	PROPN
fcis-30674	128	11	;	;	PUNCT
fcis-30674	128	12	xia	xia	PROPN
fcis-30674	128	13	,	,	PUNCT
fcis-30674	128	14	j.	j.	PROPN
fcis-30674	128	15	;	;	PUNCT
fcis-30674	128	16	chen	chen	PROPN
fcis-30674	128	17	,	,	PUNCT
fcis-30674	128	18	z.	z.	PROPN
fcis-30674	128	19	;	;	PUNCT
fcis-30674	128	20	xu	xu	PROPN
fcis-30674	128	21	,	,	PUNCT
fcis-30674	128	22	d.	d.	PROPN
fcis-30674	128	23	;	;	PUNCT
fcis-30674	128	24	wu	wu	PROPN
fcis-30674	128	25	,	,	PUNCT
fcis-30674	128	26	x.	x.	PROPN
fcis-30674	128	27	;	;	PUNCT
fcis-30674	128	28	xu	xu	PROPN
fcis-30674	128	29	,	,	PUNCT
fcis-30674	128	30	m.	m.	NOUN
fcis-30674	128	31	;	;	PUNCT
fcis-30674	128	32	schreck	schreck	NOUN
fcis-30674	128	33	,	,	PUNCT
fcis-30674	128	34	t.	t.	NOUN
fcis-30674	128	35	conceptexplorer	conceptexplorer	NOUN
fcis-30674	128	36	:	:	PUNCT
fcis-30674	128	37	visual	visual	ADJ
fcis-30674	128	38	analysis	analysis	NOUN
fcis-30674	128	39	of	of	ADP
fcis-30674	128	40	concept	concept	NOUN
fcis-30674	128	41	drifts	drift	NOUN
fcis-30674	128	42	in	in	ADP
fcis-30674	128	43	multi	multi	ADJ
fcis-30674	128	44	-	-	ADJ
fcis-30674	128	45	source	source	NOUN
fcis-30674	128	46	time	time	NOUN
fcis-30674	128	47	-	-	PUNCT
fcis-30674	128	48	series	series	NOUN
fcis-30674	128	49	data	datum	NOUN
fcis-30674	128	50	.	.	PUNCT
fcis-30674	129	1	in	in	ADP
fcis-30674	129	2	proceedings	proceeding	NOUN
fcis-30674	129	3	of	of	ADP
fcis-30674	129	4	the	the	DET
fcis-30674	129	5	ieee	ieee	NOUN
fcis-30674	129	6	conference	conference	NOUN
fcis-30674	129	7	on	on	ADP
fcis-30674	129	8	visual	visual	ADJ
fcis-30674	129	9	analytics	analytic	NOUN
fcis-30674	129	10	science	science	NOUN
fcis-30674	129	11	and	and	CCONJ
fcis-30674	129	12	technology	technology	NOUN
fcis-30674	129	13	(	(	PUNCT
fcis-30674	129	14	vast	vast	ADJ
fcis-30674	129	15	)	)	PUNCT
fcis-30674	129	16	,	,	PUNCT
fcis-30674	129	17	salt	salt	NOUN
fcis-30674	129	18	lake	lake	PROPN
fcis-30674	129	19	city	city	PROPN
fcis-30674	129	20	,	,	PUNCT
fcis-30674	129	21	ut	ut	PROPN
fcis-30674	129	22	,	,	PUNCT
fcis-30674	129	23	usa	usa	PROPN
fcis-30674	129	24	,	,	PUNCT
fcis-30674	129	25	25–30	25–30	PROPN
fcis-30674	129	26	october	october	PROPN
fcis-30674	129	27	2020	2020	NUM
fcis-30674	129	28	;	;	PUNCT
fcis-30674	129	29	pp	pp	X
fcis-30674	129	30	.	.	PUNCT
fcis-30674	130	1	1–11	1–11	NOUN
fcis-30674	130	2	.	.	PUNCT
fcis-30674	131	1	[	[	X
fcis-30674	131	2	crossref	crossref	X
fcis-30674	131	3	]	]	X
fcis-30674	132	1	[	[	X
fcis-30674	132	2	10	10	NUM
fcis-30674	132	3	]	]	X
fcis-30674	132	4	huang	huang	PROPN
fcis-30674	132	5	g	g	PROPN
fcis-30674	132	6	b	b	PROPN
fcis-30674	132	7	,	,	PUNCT
fcis-30674	132	8	zhu	zhu	PROPN
fcis-30674	132	9	q	q	PROPN
fcis-30674	132	10	y	y	PROPN
fcis-30674	132	11	,	,	PUNCT
fcis-30674	132	12	siew	siew	PROPN
fcis-30674	132	13	c	c	PROPN
fcis-30674	132	14	k.	k.	PROPN
fcis-30674	132	15	extreme	extreme	PROPN
fcis-30674	132	16	learning	learning	PROPN
fcis-30674	132	17	machine	machine	NOUN
fcis-30674	132	18	:	:	PUNCT
fcis-30674	132	19	theory	theory	NOUN
fcis-30674	132	20	and	and	CCONJ
fcis-30674	132	21	applications[j	applications[j	PROPN
fcis-30674	132	22	]	]	PUNCT
fcis-30674	132	23	.	.	PUNCT
fcis-30674	133	1	neurocomputing	neurocomputing	NOUN
fcis-30674	133	2	,	,	PUNCT
fcis-30674	133	3	2006	2006	NUM
fcis-30674	133	4	,	,	PUNCT
fcis-30674	133	5	70(1	70(1	NOUN
fcis-30674	133	6	-	-	SYM
fcis-30674	133	7	3	3	NUM
fcis-30674	133	8	):	):	PUNCT
fcis-30674	133	9	489	489	NUM
fcis-30674	133	10	-	-	SYM
fcis-30674	133	11	501	501	NUM
fcis-30674	133	12	.	.	PUNCT
fcis-30674	134	1	[	[	X
fcis-30674	134	2	11	11	NUM
fcis-30674	134	3	]	]	X
fcis-30674	134	4	rumelhart	rumelhart	NOUN
fcis-30674	134	5	d	d	PROPN
fcis-30674	134	6	e	e	PROPN
fcis-30674	134	7	,	,	PUNCT
fcis-30674	134	8	hinton	hinton	PROPN
fcis-30674	134	9	g	g	PROPN
fcis-30674	134	10	e	e	PROPN
fcis-30674	134	11	,	,	PUNCT
fcis-30674	134	12	williams	williams	PROPN
fcis-30674	134	13	r	r	AUX
fcis-30674	134	14	j.	j.	PROPN
fcis-30674	134	15	learning	learn	VERB
fcis-30674	134	16	representations	representation	NOUN
fcis-30674	134	17	by	by	ADP
fcis-30674	134	18	back	back	ADV
fcis-30674	134	19	-	-	PUNCT
fcis-30674	134	20	propagating	propagate	VERB
fcis-30674	134	21	errors[j	errors[j	NOUN
fcis-30674	134	22	]	]	PUNCT
fcis-30674	134	23	.	.	PUNCT
fcis-30674	135	1	nature	nature	NOUN
fcis-30674	135	2	,	,	PUNCT
fcis-30674	135	3	1986	1986	NUM
fcis-30674	135	4	,	,	PUNCT
fcis-30674	135	5	323(6088	323(6088	NUM
fcis-30674	135	6	):	):	PUNCT
fcis-30674	135	7	533	533	NUM
fcis-30674	135	8	-	-	SYM
fcis-30674	135	9	536	536	NUM
fcis-30674	135	10	.	.	PUNCT
fcis-30674	136	1	[	[	X
fcis-30674	136	2	12	12	NUM
fcis-30674	136	3	]	]	PUNCT
fcis-30674	136	4	bernardo	bernardo	NOUN
fcis-30674	136	5	a	a	X
fcis-30674	136	6	,	,	PUNCT
fcis-30674	136	7	della	della	PROPN
fcis-30674	136	8	valle	valle	PROPN
fcis-30674	136	9	e.	e.	PROPN
fcis-30674	136	10	smote	smote	PROPN
fcis-30674	136	11	-	-	PUNCT
fcis-30674	136	12	ob	ob	NOUN
fcis-30674	136	13	:	:	PUNCT
fcis-30674	136	14	combining	combine	VERB
fcis-30674	136	15	smote	smote	ADJ
fcis-30674	136	16	and	and	CCONJ
fcis-30674	136	17	online	online	ADJ
fcis-30674	136	18	bagging	bagging	NOUN
fcis-30674	136	19	for	for	ADP
fcis-30674	136	20	continuous	continuous	ADJ
fcis-30674	136	21	rebalancing	rebalancing	NOUN
fcis-30674	136	22	of	of	ADP
fcis-30674	136	23	evolving	evolve	VERB
fcis-30674	136	24	data	datum	NOUN
fcis-30674	136	25	streams[c]//2021	streams[c]//2021	PROPN
fcis-30674	136	26	ieee	ieee	PROPN
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fcis-30674	136	28	conference	conference	NOUN
fcis-30674	136	29	on	on	ADP
fcis-30674	136	30	big	big	ADJ
fcis-30674	136	31	data	datum	NOUN
fcis-30674	136	32	(	(	PUNCT
fcis-30674	136	33	big	big	ADJ
fcis-30674	136	34	data	datum	NOUN
fcis-30674	136	35	)	)	PUNCT
fcis-30674	136	36	.	.	PUNCT
fcis-30674	137	1	ieee	ieee	NOUN
fcis-30674	137	2	,	,	PUNCT
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fcis-30674	137	4	:	:	PUNCT
fcis-30674	137	5	5033	5033	NUM
fcis-30674	137	6	-	-	SYM
fcis-30674	137	7	5042	5042	NUM
fcis-30674	137	8	.	.	PUNCT
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fcis-30674	138	2	13	13	NUM
fcis-30674	138	3	]	]	X
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fcis-30674	138	5	y	y	PROPN
fcis-30674	138	6	,	,	PUNCT
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fcis-30674	138	11	y	y	PROPN
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fcis-30674	138	13	et	et	PROPN
fcis-30674	138	14	al	al	PROPN
fcis-30674	138	15	.	.	PUNCT
fcis-30674	139	1	a	a	DET
fcis-30674	139	2	classifier	classifier	NOUN
fcis-30674	139	3	using	use	VERB
fcis-30674	139	4	online	online	ADJ
fcis-30674	139	5	bagging	bag	VERB
fcis-30674	139	6	ensemble	ensemble	ADJ
fcis-30674	139	7	method	method	NOUN
fcis-30674	139	8	for	for	ADP
fcis-30674	139	9	big	big	ADJ
fcis-30674	139	10	data	datum	NOUN
fcis-30674	139	11	stream	stream	NOUN
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fcis-30674	139	13	]	]	PUNCT
fcis-30674	139	14	.	.	PUNCT
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fcis-30674	140	2	science	science	PROPN
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fcis-30674	140	4	technology	technology	NOUN
fcis-30674	140	5	,	,	PUNCT
fcis-30674	140	6	2019	2019	NUM
fcis-30674	140	7	,	,	PUNCT
fcis-30674	140	8	24(4	24(4	NUM
fcis-30674	140	9	):	):	PUNCT
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fcis-30674	140	11	-	-	SYM
fcis-30674	140	12	388	388	NUM
fcis-30674	140	13	.	.	PUNCT
fcis-30674	141	1	[	[	X
fcis-30674	141	2	14	14	NUM
fcis-30674	141	3	]	]	X
fcis-30674	141	4	bayram	bayram	PROPN
fcis-30674	141	5	b	b	PROPN
fcis-30674	141	6	,	,	PUNCT
fcis-30674	141	7	köroğlu	köroğlu	PROPN
fcis-30674	141	8	b	b	PROPN
fcis-30674	141	9	,	,	PUNCT
fcis-30674	141	10	gönen	gönen	PROPN
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fcis-30674	141	12	improving	improve	VERB
fcis-30674	141	13	fraud	fraud	NOUN
fcis-30674	141	14	detection	detection	NOUN
fcis-30674	141	15	and	and	CCONJ
fcis-30674	141	16	concept	concept	NOUN
fcis-30674	141	17	drift	drift	NOUN
fcis-30674	141	18	adaptation	adaptation	NOUN
fcis-30674	141	19	in	in	ADP
fcis-30674	141	20	credit	credit	NOUN
fcis-30674	141	21	card	card	NOUN
fcis-30674	141	22	transactions	transaction	NOUN
fcis-30674	141	23	using	use	VERB
fcis-30674	141	24	incremental	incremental	ADJ
fcis-30674	141	25	gradient	gradient	NOUN
fcis-30674	141	26	boosting	boost	VERB
fcis-30674	141	27	trees[c]//2020	trees[c]//2020	NUM
fcis-30674	141	28	19th	19th	ADJ
fcis-30674	141	29	ieee	ieee	NOUN
fcis-30674	141	30	international	international	ADJ
fcis-30674	141	31	conference	conference	NOUN
fcis-30674	141	32	on	on	ADP
fcis-30674	141	33	machine	machine	NOUN
fcis-30674	141	34	learning	learning	NOUN
fcis-30674	141	35	and	and	CCONJ
fcis-30674	141	36	applications	application	NOUN
fcis-30674	141	37	(	(	PUNCT
fcis-30674	141	38	icmla	icmla	NOUN
fcis-30674	141	39	)	)	PUNCT
fcis-30674	141	40	.	.	PUNCT
fcis-30674	142	1	ieee	ieee	PROPN
fcis-30674	142	2	,	,	PUNCT
fcis-30674	142	3	2020	2020	NUM
fcis-30674	142	4	:	:	PUNCT
fcis-30674	142	5	545	545	NUM
fcis-30674	142	6	-	-	SYM
fcis-30674	142	7	550	550	NUM
fcis-30674	142	8	.	.	PUNCT
fcis-30674	143	1	[	[	X
fcis-30674	143	2	15	15	NUM
fcis-30674	143	3	]	]	X
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fcis-30674	143	5	k	k	PROPN
fcis-30674	143	6	,	,	PUNCT
fcis-30674	143	7	lu	lu	PROPN
fcis-30674	143	8	j	j	PROPN
fcis-30674	143	9	,	,	PUNCT
fcis-30674	143	10	liu	liu	PROPN
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fcis-30674	143	13	,	,	PUNCT
fcis-30674	143	14	et	et	PROPN
fcis-30674	143	15	al	al	PROPN
fcis-30674	143	16	.	.	PUNCT
fcis-30674	143	17	evolving	evolve	VERB
fcis-30674	143	18	gradient	gradient	ADJ
fcis-30674	143	19	boost	boost	NOUN
fcis-30674	143	20	:	:	PUNCT
fcis-30674	143	21	a	a	DET
fcis-30674	143	22	pruning	prune	VERB
fcis-30674	143	23	scheme	scheme	NOUN
fcis-30674	143	24	based	base	VERB
fcis-30674	143	25	on	on	ADP
fcis-30674	143	26	loss	loss	NOUN
fcis-30674	143	27	improvement	improvement	NOUN
fcis-30674	143	28	ratio	ratio	NOUN
fcis-30674	143	29	for	for	ADP
fcis-30674	143	30	learning	learn	VERB
fcis-30674	143	31	under	under	ADP
fcis-30674	143	32	concept	concept	NOUN
fcis-30674	143	33	drift	drift	NOUN
fcis-30674	143	34	[	[	X
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fcis-30674	143	36	]	]	X
fcis-30674	143	37	.	.	PUNCT
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fcis-30674	144	2	transactions	transaction	NOUN
fcis-30674	144	3	on	on	ADP
fcis-30674	144	4	cybernetics	cybernetic	NOUN
fcis-30674	144	5	,	,	PUNCT
fcis-30674	144	6	2021	2021	NUM
fcis-30674	144	7	,	,	PUNCT
fcis-30674	144	8	151	151	NUM
fcis-30674	144	9	:	:	SYM
fcis-30674	144	10	1	1	NUM
fcis-30674	144	11	-	-	SYM
fcis-30674	144	12	14	14	NUM
fcis-30674	144	13	.	.	PUNCT
fcis-30674	145	1	215	215	NUM
fcis-30674	146	1	[	[	SYM
fcis-30674	146	2	16	16	NUM
fcis-30674	146	3	]	]	X
fcis-30674	146	4	wang	wang	PROPN
fcis-30674	146	5	k	k	PROPN
fcis-30674	146	6	,	,	PUNCT
fcis-30674	146	7	lu	lu	PROPN
fcis-30674	146	8	j	j	PROPN
fcis-30674	146	9	,	,	PUNCT
fcis-30674	146	10	liu	liu	PROPN
fcis-30674	146	11	a	a	PROPN
fcis-30674	146	12	j	j	PROPN
fcis-30674	146	13	,	,	PUNCT
fcis-30674	146	14	et	et	PROPN
fcis-30674	146	15	al	al	PROPN
fcis-30674	146	16	.	.	PUNCT
fcis-30674	147	1	elastic	elastic	ADJ
fcis-30674	147	2	gradient	gradient	NOUN
fcis-30674	147	3	boosting	boost	VERB
fcis-30674	147	4	decision	decision	NOUN
fcis-30674	147	5	tree	tree	NOUN
fcis-30674	147	6	with	with	ADP
fcis-30674	147	7	adaptiveiterations	adaptiveiteration	NOUN
fcis-30674	147	8	for	for	ADP
fcis-30674	147	9	concept	concept	NOUN
fcis-30674	147	10	drift	drift	NOUN
fcis-30674	147	11	adaptation	adaptation	NOUN
fcis-30674	147	12	[	[	X
fcis-30674	147	13	j	j	X
fcis-30674	147	14	]	]	X
fcis-30674	147	15	.	.	PUNCT
fcis-30674	148	1	neurocomputing	neurocomputing	NOUN
fcis-30674	148	2	,	,	PUNCT
fcis-30674	148	3	2022	2022	NUM
fcis-30674	148	4	,	,	PUNCT
fcis-30674	148	5	491	491	NUM
fcis-30674	148	6	:	:	PUNCT
fcis-30674	148	7	288	288	NUM
fcis-30674	148	8	-	-	SYM
fcis-30674	148	9	304	304	NUM
fcis-30674	148	10	.	.	PUNCT
fcis-30674	149	1	[	[	X
fcis-30674	149	2	17	17	NUM
fcis-30674	149	3	]	]	X
fcis-30674	149	4	kang	kang	PROPN
fcis-30674	149	5	q	q	PROPN
fcis-30674	149	6	,	,	PUNCT
fcis-30674	149	7	chen	chen	PROPN
fcis-30674	149	8	x	x	PROPN
fcis-30674	149	9	s	s	PROPN
fcis-30674	149	10	,	,	PUNCT
fcis-30674	149	11	li	li	PROPN
fcis-30674	149	12	s	s	PROPN
fcis-30674	149	13	s	s	PROPN
fcis-30674	149	14	,	,	PUNCT
fcis-30674	149	15	et	et	PROPN
fcis-30674	149	16	al	al	PROPN
fcis-30674	149	17	.	.	PUNCT
fcis-30674	150	1	a	a	DET
fcis-30674	150	2	noise	noise	NOUN
fcis-30674	150	3	-	-	PUNCT
fcis-30674	150	4	filtered	filter	VERB
fcis-30674	150	5	undersampling	undersampling	ADJ
fcis-30674	150	6	scheme	scheme	NOUN
fcis-30674	150	7	for	for	ADP
fcis-30674	150	8	imbalanced	imbalanced	ADJ
fcis-30674	150	9	classification[j	classification[j	NOUN
fcis-30674	150	10	]	]	PUNCT
fcis-30674	150	11	.	.	PUNCT
fcis-30674	151	1	ieee	ieee	NOUN
fcis-30674	151	2	transactions	transaction	NOUN
fcis-30674	151	3	on	on	ADP
fcis-30674	151	4	cybernetics	cybernetic	NOUN
fcis-30674	151	5	,	,	PUNCT
fcis-30674	151	6	2016	2016	NUM
fcis-30674	151	7	,	,	PUNCT
fcis-30674	151	8	47(12	47(12	NUM
fcis-30674	151	9	):	):	PUNCT
fcis-30674	151	10	4263	4263	NUM
fcis-30674	151	11	-	-	SYM
fcis-30674	151	12	4274	4274	NUM
fcis-30674	151	13	.	.	PUNCT
fcis-30674	152	1	[	[	X
fcis-30674	152	2	18	18	NUM
fcis-30674	152	3	]	]	SYM
fcis-30674	152	4	brodersen	brodersen	NOUN
fcis-30674	152	5	k	k	PROPN
fcis-30674	152	6	h	h	PROPN
fcis-30674	152	7	,	,	PUNCT
fcis-30674	152	8	ong	ong	PROPN
fcis-30674	152	9	c	c	PROPN
fcis-30674	152	10	s	s	PROPN
fcis-30674	152	11	,	,	PUNCT
fcis-30674	152	12	stephan	stephan	PROPN
fcis-30674	152	13	k	k	PROPN
fcis-30674	152	14	e	e	PROPN
fcis-30674	152	15	,	,	PUNCT
fcis-30674	152	16	et	et	PROPN
fcis-30674	152	17	al	al	PROPN
fcis-30674	152	18	.	.	PUNCT
fcis-30674	153	1	the	the	DET
fcis-30674	153	2	balanced	balanced	ADJ
fcis-30674	153	3	accuracy	accuracy	NOUN
fcis-30674	153	4	and	and	CCONJ
fcis-30674	153	5	its	its	PRON
fcis-30674	153	6	posterior	posterior	ADJ
fcis-30674	153	7	distribution[c]//2010	distribution[c]//2010	NUM
fcis-30674	153	8	20th	20th	ADJ
fcis-30674	153	9	international	international	ADJ
fcis-30674	153	10	conference	conference	NOUN
fcis-30674	153	11	on	on	ADP
fcis-30674	153	12	pattern	pattern	NOUN
fcis-30674	153	13	recognition	recognition	NOUN
fcis-30674	153	14	.	.	PUNCT
fcis-30674	154	1	ieee	ieee	PROPN
fcis-30674	154	2	,	,	PUNCT
fcis-30674	154	3	2010	2010	NUM
fcis-30674	154	4	:	:	PUNCT
fcis-30674	154	5	3121	3121	NUM
fcis-30674	154	6	-	-	SYM
fcis-30674	154	7	3124	3124	NUM
fcis-30674	154	8	.	.	PUNCT