id	sid	tid	token	lemma	pos
fcis-5958	1	1	frontiers	frontier	NOUN
fcis-5958	1	2	in	in	ADP
fcis-5958	1	3	computing	computing	NOUN
fcis-5958	1	4	and	and	CCONJ
fcis-5958	1	5	intelligent	intelligent	ADJ
fcis-5958	1	6	systems	system	NOUN
fcis-5958	1	7	issn	issn	VERB
fcis-5958	1	8	:	:	PUNCT
fcis-5958	1	9	2832	2832	NUM
fcis-5958	1	10	-	-	SYM
fcis-5958	1	11	6024	6024	NUM
fcis-5958	1	12	|	|	NOUN
fcis-5958	1	13	vol	vol	NOUN
fcis-5958	1	14	.	.	PROPN
fcis-5958	2	1	3	3	NUM
fcis-5958	2	2	,	,	PUNCT
fcis-5958	2	3	no	no	INTJ
fcis-5958	2	4	.	.	NOUN
fcis-5958	2	5	1	1	NUM
fcis-5958	2	6	,	,	PUNCT
fcis-5958	2	7	2023	2023	NUM
fcis-5958	2	8	1	1	NUM
fcis-5958	2	9	prediction	prediction	NOUN
fcis-5958	2	10	of	of	ADP
fcis-5958	2	11	telecom	telecom	NOUN
fcis-5958	2	12	customer	customer	NOUN
fcis-5958	2	13	churn	churn	NOUN
fcis-5958	2	14	based	base	VERB
fcis-5958	2	15	on	on	ADP
fcis-5958	2	16	mipca	mipca	PROPN
fcis-5958	2	17	-	-	PUNCT
fcis-5958	2	18	xgboost	xgboost	PROPN
fcis-5958	2	19	method	method	PROPN
fcis-5958	2	20	chen	chen	PROPN
fcis-5958	2	21	zhuo	zhuo	PROPN
fcis-5958	2	22	*	*	PROPN
fcis-5958	2	23	school	school	NOUN
fcis-5958	2	24	of	of	ADP
fcis-5958	2	25	control	control	NOUN
fcis-5958	2	26	and	and	CCONJ
fcis-5958	2	27	computer	computer	NOUN
fcis-5958	2	28	engineering	engineering	NOUN
fcis-5958	2	29	,	,	PUNCT
fcis-5958	2	30	north	north	PROPN
fcis-5958	2	31	china	china	PROPN
fcis-5958	2	32	electric	electric	PROPN
fcis-5958	2	33	power	power	PROPN
fcis-5958	2	34	university	university	PROPN
fcis-5958	2	35	,	,	PUNCT
fcis-5958	2	36	beijing	beijing	PROPN
fcis-5958	2	37	102200	102200	NUM
fcis-5958	2	38	,	,	PUNCT
fcis-5958	2	39	china	china	PROPN
fcis-5958	2	40	*	*	PUNCT
fcis-5958	2	41	corresponding	correspond	VERB
fcis-5958	2	42	author	author	NOUN
fcis-5958	2	43	email	email	NOUN
fcis-5958	2	44	:	:	PUNCT
fcis-5958	2	45	cherrybombczz@163.com	cherrybombczz@163.com	X
fcis-5958	2	46	abstract	abstract	NOUN
fcis-5958	2	47	:	:	PUNCT
fcis-5958	2	48	in	in	ADP
fcis-5958	2	49	order	order	NOUN
fcis-5958	2	50	to	to	PART
fcis-5958	2	51	solve	solve	VERB
fcis-5958	2	52	the	the	DET
fcis-5958	2	53	problem	problem	NOUN
fcis-5958	2	54	that	that	SCONJ
fcis-5958	2	55	the	the	DET
fcis-5958	2	56	nonlinear	nonlinear	ADJ
fcis-5958	2	57	information	information	NOUN
fcis-5958	2	58	of	of	ADP
fcis-5958	2	59	data	datum	NOUN
fcis-5958	2	60	in	in	ADP
fcis-5958	2	61	the	the	DET
fcis-5958	2	62	field	field	NOUN
fcis-5958	2	63	of	of	ADP
fcis-5958	2	64	telecom	telecom	NOUN
fcis-5958	2	65	customer	customer	NOUN
fcis-5958	2	66	churn	churn	NOUN
fcis-5958	2	67	prediction	prediction	NOUN
fcis-5958	2	68	is	be	AUX
fcis-5958	2	69	not	not	PART
fcis-5958	2	70	fully	fully	ADV
fcis-5958	2	71	used	use	VERB
fcis-5958	2	72	,	,	PUNCT
fcis-5958	2	73	or	or	CCONJ
fcis-5958	2	74	even	even	ADV
fcis-5958	2	75	ignored	ignore	VERB
fcis-5958	2	76	,	,	PUNCT
fcis-5958	2	77	which	which	PRON
fcis-5958	2	78	leads	lead	VERB
fcis-5958	2	79	to	to	ADP
fcis-5958	2	80	inaccurate	inaccurate	ADJ
fcis-5958	2	81	prediction	prediction	NOUN
fcis-5958	2	82	,	,	PUNCT
fcis-5958	2	83	this	this	DET
fcis-5958	2	84	paper	paper	NOUN
fcis-5958	2	85	introduces	introduce	VERB
fcis-5958	2	86	the	the	DET
fcis-5958	2	87	mutual	mutual	ADJ
fcis-5958	2	88	information	information	NOUN
fcis-5958	2	89	feature	feature	NOUN
fcis-5958	2	90	selection	selection	NOUN
fcis-5958	2	91	method	method	NOUN
fcis-5958	2	92	(	(	PUNCT
fcis-5958	2	93	mipca	mipca	ADV
fcis-5958	2	94	)	)	PUNCT
fcis-5958	2	95	to	to	PART
fcis-5958	2	96	filter	filter	VERB
fcis-5958	2	97	the	the	DET
fcis-5958	2	98	features	feature	NOUN
fcis-5958	2	99	and	and	CCONJ
fcis-5958	2	100	reduce	reduce	VERB
fcis-5958	2	101	the	the	DET
fcis-5958	2	102	dimensions	dimension	NOUN
fcis-5958	2	103	of	of	ADP
fcis-5958	2	104	customer	customer	NOUN
fcis-5958	2	105	data	datum	NOUN
fcis-5958	2	106	,	,	PUNCT
fcis-5958	2	107	and	and	CCONJ
fcis-5958	2	108	proposes	propose	VERB
fcis-5958	2	109	an	an	DET
fcis-5958	2	110	xgboost	xgboost	ADV
fcis-5958	2	111	method	method	NOUN
fcis-5958	2	112	based	base	VERB
fcis-5958	2	113	on	on	ADP
fcis-5958	2	114	the	the	DET
fcis-5958	2	115	mutual	mutual	ADJ
fcis-5958	2	116	information	information	NOUN
fcis-5958	2	117	feature	feature	NOUN
fcis-5958	2	118	selection	selection	NOUN
fcis-5958	2	119	method(mipca	method(mipca	PROPN
fcis-5958	2	120	-	-	PUNCT
fcis-5958	2	121	xgboost	xgboost	PROPN
fcis-5958	2	122	)	)	PUNCT
fcis-5958	2	123	,	,	PUNCT
fcis-5958	2	124	which	which	PRON
fcis-5958	2	125	improves	improve	VERB
fcis-5958	2	126	the	the	DET
fcis-5958	2	127	accuracy	accuracy	NOUN
fcis-5958	2	128	of	of	ADP
fcis-5958	2	129	the	the	DET
fcis-5958	2	130	prediction	prediction	NOUN
fcis-5958	2	131	results	result	NOUN
fcis-5958	2	132	.	.	PUNCT
fcis-5958	3	1	by	by	ADP
fcis-5958	3	2	using	use	VERB
fcis-5958	3	3	the	the	DET
fcis-5958	3	4	data	datum	NOUN
fcis-5958	3	5	set	set	VERB
fcis-5958	3	6	of	of	ADP
fcis-5958	3	7	telecom	telecom	NOUN
fcis-5958	3	8	industry	industry	NOUN
fcis-5958	3	9	customers	customer	NOUN
fcis-5958	3	10	published	publish	VERB
fcis-5958	3	11	on	on	ADP
fcis-5958	3	12	kaggle	kaggle	NOUN
fcis-5958	3	13	website	website	NOUN
fcis-5958	3	14	,	,	PUNCT
fcis-5958	3	15	compares	compare	VERB
fcis-5958	3	16	the	the	DET
fcis-5958	3	17	prediction	prediction	NOUN
fcis-5958	3	18	result	result	NOUN
fcis-5958	3	19	of	of	ADP
fcis-5958	3	20	this	this	DET
fcis-5958	3	21	method	method	NOUN
fcis-5958	3	22	with	with	ADP
fcis-5958	3	23	that	that	PRON
fcis-5958	3	24	of	of	ADP
fcis-5958	3	25	machine	machine	NOUN
fcis-5958	3	26	learning	learn	VERB
fcis-5958	3	27	algorithms	algorithm	NOUN
fcis-5958	3	28	commonly	commonly	ADV
fcis-5958	3	29	used	use	VERB
fcis-5958	3	30	in	in	ADP
fcis-5958	3	31	this	this	DET
fcis-5958	3	32	field	field	NOUN
fcis-5958	3	33	,	,	PUNCT
fcis-5958	3	34	and	and	CCONJ
fcis-5958	3	35	proves	prove	VERB
fcis-5958	3	36	the	the	DET
fcis-5958	3	37	accuracy	accuracy	NOUN
fcis-5958	3	38	,	,	PUNCT
fcis-5958	3	39	recall	recall	VERB
fcis-5958	3	40	and	and	CCONJ
fcis-5958	3	41	f_score	f_score	NOUN
fcis-5958	3	42	of	of	ADP
fcis-5958	3	43	mipca	mipca	ADV
fcis-5958	3	44	-	-	PUNCT
fcis-5958	3	45	xgboost	xgboost	PROPN
fcis-5958	3	46	method	method	NOUN
fcis-5958	3	47	is	be	AUX
fcis-5958	3	48	higher	high	ADJ
fcis-5958	3	49	than	than	ADP
fcis-5958	3	50	other	other	ADJ
fcis-5958	3	51	algorithms	algorithm	NOUN
fcis-5958	3	52	.	.	PUNCT
fcis-5958	4	1	keywords	keyword	NOUN
fcis-5958	4	2	:	:	PUNCT
fcis-5958	4	3	customer	customer	NOUN
fcis-5958	4	4	churn	churn	NOUN
fcis-5958	4	5	;	;	PUNCT
fcis-5958	4	6	telecom	telecom	NOUN
fcis-5958	4	7	customers	customer	NOUN
fcis-5958	4	8	;	;	PUNCT
fcis-5958	4	9	mutual	mutual	ADJ
fcis-5958	4	10	information	information	NOUN
fcis-5958	4	11	feature	feature	NOUN
fcis-5958	4	12	selection	selection	NOUN
fcis-5958	4	13	;	;	PUNCT
fcis-5958	4	14	xgboost	xgboost	X
fcis-5958	4	15	algorithm	algorithm	NOUN
fcis-5958	4	16	.	.	PUNCT
fcis-5958	5	1	1	1	X
fcis-5958	5	2	.	.	X
fcis-5958	5	3	introduction	introduction	NOUN
fcis-5958	5	4	nowadays	nowadays	ADV
fcis-5958	5	5	,	,	PUNCT
fcis-5958	5	6	the	the	DET
fcis-5958	5	7	market	market	NOUN
fcis-5958	5	8	of	of	ADP
fcis-5958	5	9	china	china	PROPN
fcis-5958	5	10	's	's	PART
fcis-5958	5	11	telecom	telecom	NOUN
fcis-5958	5	12	industry	industry	NOUN
fcis-5958	5	13	is	be	AUX
fcis-5958	5	14	becoming	becoming	AUX
fcis-5958	5	15	increasingly	increasingly	ADV
fcis-5958	5	16	saturated	saturate	VERB
fcis-5958	5	17	,	,	PUNCT
fcis-5958	5	18	the	the	DET
fcis-5958	5	19	competition	competition	NOUN
fcis-5958	5	20	between	between	ADP
fcis-5958	5	21	enterprises	enterprise	NOUN
fcis-5958	5	22	is	be	AUX
fcis-5958	5	23	becoming	become	VERB
fcis-5958	5	24	more	more	ADV
fcis-5958	5	25	and	and	CCONJ
fcis-5958	5	26	more	more	ADV
fcis-5958	5	27	fierce	fierce	ADJ
fcis-5958	5	28	,	,	PUNCT
fcis-5958	5	29	and	and	CCONJ
fcis-5958	5	30	the	the	DET
fcis-5958	5	31	customer	customer	NOUN
fcis-5958	5	32	churn	churn	NOUN
fcis-5958	5	33	rate	rate	NOUN
fcis-5958	5	34	is	be	AUX
fcis-5958	5	35	gradually	gradually	ADV
fcis-5958	5	36	rising	rise	VERB
fcis-5958	5	37	.	.	PUNCT
fcis-5958	6	1	many	many	ADJ
fcis-5958	6	2	enterprises	enterprise	NOUN
fcis-5958	6	3	focus	focus	VERB
fcis-5958	6	4	on	on	ADP
fcis-5958	6	5	how	how	SCONJ
fcis-5958	6	6	to	to	PART
fcis-5958	6	7	use	use	VERB
fcis-5958	6	8	novel	novel	ADJ
fcis-5958	6	9	marketing	marketing	NOUN
fcis-5958	6	10	models	model	NOUN
fcis-5958	6	11	to	to	PART
fcis-5958	6	12	attract	attract	VERB
fcis-5958	6	13	new	new	ADJ
fcis-5958	6	14	customers	customer	NOUN
fcis-5958	6	15	and	and	CCONJ
fcis-5958	6	16	further	far	ADV
fcis-5958	6	17	develop	develop	VERB
fcis-5958	6	18	them	they	PRON
fcis-5958	6	19	into	into	ADP
fcis-5958	6	20	loyal	loyal	ADJ
fcis-5958	6	21	customers	customer	NOUN
fcis-5958	6	22	.	.	PUNCT
fcis-5958	7	1	however	however	ADV
fcis-5958	7	2	,	,	PUNCT
fcis-5958	7	3	studies	study	NOUN
fcis-5958	7	4	show	show	VERB
fcis-5958	7	5	that	that	SCONJ
fcis-5958	7	6	the	the	DET
fcis-5958	7	7	cost	cost	NOUN
fcis-5958	7	8	of	of	ADP
fcis-5958	7	9	time	time	NOUN
fcis-5958	7	10	and	and	CCONJ
fcis-5958	7	11	money	money	NOUN
fcis-5958	7	12	required	require	VERB
fcis-5958	7	13	for	for	SCONJ
fcis-5958	7	14	a	a	DET
fcis-5958	7	15	company	company	NOUN
fcis-5958	7	16	to	to	PART
fcis-5958	7	17	develop	develop	VERB
fcis-5958	7	18	a	a	DET
fcis-5958	7	19	new	new	ADJ
fcis-5958	7	20	customer	customer	NOUN
fcis-5958	7	21	is	be	AUX
fcis-5958	7	22	far	far	ADV
fcis-5958	7	23	greater	great	ADJ
fcis-5958	7	24	than	than	ADP
fcis-5958	7	25	the	the	DET
fcis-5958	7	26	cost	cost	NOUN
fcis-5958	7	27	of	of	ADP
fcis-5958	7	28	maintaining	maintain	VERB
fcis-5958	7	29	an	an	DET
fcis-5958	7	30	existing	exist	VERB
fcis-5958	7	31	customer	customer	NOUN
fcis-5958	8	1	[	[	X
fcis-5958	8	2	1	1	NUM
fcis-5958	8	3	]	]	PUNCT
fcis-5958	8	4	.	.	PUNCT
fcis-5958	9	1	therefore	therefore	ADV
fcis-5958	9	2	,	,	PUNCT
fcis-5958	9	3	it	it	PRON
fcis-5958	9	4	is	be	AUX
fcis-5958	9	5	imperative	imperative	ADJ
fcis-5958	9	6	for	for	SCONJ
fcis-5958	9	7	enterprises	enterprise	NOUN
fcis-5958	9	8	to	to	PART
fcis-5958	9	9	build	build	VERB
fcis-5958	9	10	a	a	DET
fcis-5958	9	11	model	model	NOUN
fcis-5958	9	12	that	that	PRON
fcis-5958	9	13	can	can	AUX
fcis-5958	9	14	accurately	accurately	ADV
fcis-5958	9	15	predict	predict	VERB
fcis-5958	9	16	the	the	DET
fcis-5958	9	17	customer	customer	NOUN
fcis-5958	9	18	churn	churn	NOUN
fcis-5958	9	19	tendency	tendency	NOUN
fcis-5958	9	20	,	,	PUNCT
fcis-5958	9	21	understand	understand	VERB
fcis-5958	9	22	the	the	DET
fcis-5958	9	23	customer	customer	NOUN
fcis-5958	9	24	churn	churn	NOUN
fcis-5958	9	25	tendency	tendency	NOUN
fcis-5958	9	26	in	in	ADP
fcis-5958	9	27	time	time	NOUN
fcis-5958	9	28	and	and	CCONJ
fcis-5958	9	29	adjust	adjust	VERB
fcis-5958	9	30	the	the	DET
fcis-5958	9	31	customer	customer	NOUN
fcis-5958	9	32	maintenance	maintenance	NOUN
fcis-5958	9	33	strategy	strategy	NOUN
fcis-5958	9	34	to	to	PART
fcis-5958	9	35	reduce	reduce	VERB
fcis-5958	9	36	the	the	DET
fcis-5958	9	37	customer	customer	NOUN
fcis-5958	9	38	churn	churn	NOUN
fcis-5958	9	39	rate	rate	NOUN
fcis-5958	9	40	.	.	PUNCT
fcis-5958	10	1	customer	customer	NOUN
fcis-5958	10	2	churn	churn	NOUN
fcis-5958	10	3	is	be	AUX
fcis-5958	10	4	a	a	DET
fcis-5958	10	5	complex	complex	ADJ
fcis-5958	10	6	problem	problem	NOUN
fcis-5958	10	7	,	,	PUNCT
fcis-5958	10	8	and	and	CCONJ
fcis-5958	10	9	the	the	DET
fcis-5958	10	10	prediction	prediction	NOUN
fcis-5958	10	11	methods	method	NOUN
fcis-5958	10	12	for	for	ADP
fcis-5958	10	13	different	different	ADJ
fcis-5958	10	14	industries	industry	NOUN
fcis-5958	10	15	are	be	AUX
fcis-5958	10	16	different	different	ADJ
fcis-5958	10	17	.	.	PUNCT
fcis-5958	11	1	at	at	ADP
fcis-5958	11	2	present	present	ADJ
fcis-5958	11	3	,	,	PUNCT
fcis-5958	11	4	there	there	PRON
fcis-5958	11	5	are	be	VERB
fcis-5958	11	6	a	a	DET
fcis-5958	11	7	large	large	ADJ
fcis-5958	11	8	number	number	NOUN
fcis-5958	11	9	of	of	ADP
fcis-5958	11	10	domestic	domestic	ADJ
fcis-5958	11	11	literatures	literature	NOUN
fcis-5958	11	12	on	on	ADP
fcis-5958	11	13	customer	customer	NOUN
fcis-5958	11	14	churn	churn	NOUN
fcis-5958	11	15	,	,	PUNCT
fcis-5958	11	16	involving	involve	VERB
fcis-5958	11	17	a	a	DET
fcis-5958	11	18	wide	wide	ADJ
fcis-5958	11	19	range	range	NOUN
fcis-5958	11	20	of	of	ADP
fcis-5958	11	21	fields	field	NOUN
fcis-5958	11	22	.	.	PUNCT
fcis-5958	12	1	zhang	zhang	PROPN
fcis-5958	12	2	lili	lili	PROPN
fcis-5958	13	1	[	[	X
fcis-5958	13	2	2	2	X
fcis-5958	13	3	]	]	PUNCT
fcis-5958	13	4	and	and	CCONJ
fcis-5958	13	5	others	other	NOUN
fcis-5958	13	6	used	use	VERB
fcis-5958	13	7	decision	decision	NOUN
fcis-5958	13	8	tree	tree	NOUN
fcis-5958	13	9	algorithm	algorithm	NOUN
fcis-5958	13	10	to	to	PART
fcis-5958	13	11	predict	predict	VERB
fcis-5958	13	12	airline	airline	NOUN
fcis-5958	13	13	customer	customer	NOUN
fcis-5958	13	14	churn	churn	NOUN
fcis-5958	13	15	,	,	PUNCT
fcis-5958	13	16	and	and	CCONJ
fcis-5958	13	17	successfully	successfully	ADV
fcis-5958	13	18	improved	improve	VERB
fcis-5958	13	19	the	the	DET
fcis-5958	13	20	seating	seating	NOUN
fcis-5958	13	21	rate	rate	NOUN
fcis-5958	13	22	;	;	PUNCT
fcis-5958	13	23	yan	yan	PROPN
fcis-5958	13	24	chun	chun	PROPN
fcis-5958	14	1	[	[	X
fcis-5958	14	2	3	3	X
fcis-5958	14	3	]	]	PUNCT
fcis-5958	14	4	and	and	CCONJ
fcis-5958	14	5	others	other	NOUN
fcis-5958	14	6	used	use	VERB
fcis-5958	14	7	bp	bp	PROPN
fcis-5958	14	8	-	-	PUNCT
fcis-5958	14	9	adaboost	adaboost	ADJ
fcis-5958	14	10	algorithm	algorithm	NOUN
fcis-5958	14	11	to	to	PART
fcis-5958	14	12	predict	predict	VERB
fcis-5958	14	13	the	the	DET
fcis-5958	14	14	clustered	clustered	ADJ
fcis-5958	14	15	life	life	NOUN
fcis-5958	14	16	insurance	insurance	NOUN
fcis-5958	14	17	industry	industry	NOUN
fcis-5958	14	18	customers	customer	NOUN
fcis-5958	14	19	,	,	PUNCT
fcis-5958	14	20	providing	provide	VERB
fcis-5958	14	21	a	a	DET
fcis-5958	14	22	higher	high	ADJ
fcis-5958	14	23	prediction	prediction	NOUN
fcis-5958	14	24	accuracy	accuracy	NOUN
fcis-5958	14	25	;	;	PUNCT
fcis-5958	14	26	in	in	ADP
fcis-5958	14	27	the	the	DET
fcis-5958	14	28	field	field	NOUN
fcis-5958	14	29	of	of	ADP
fcis-5958	14	30	e	e	NOUN
fcis-5958	14	31	-	-	NOUN
fcis-5958	14	32	commerce	commerce	NOUN
fcis-5958	14	33	,	,	PUNCT
fcis-5958	14	34	wu	wu	PROPN
fcis-5958	14	35	yongchun	yongchun	NOUN
fcis-5958	15	1	[	[	X
fcis-5958	15	2	4	4	NUM
fcis-5958	15	3	]	]	PUNCT
fcis-5958	15	4	fused	fuse	VERB
fcis-5958	15	5	multiple	multiple	ADJ
fcis-5958	15	6	methods	method	NOUN
fcis-5958	15	7	to	to	PART
fcis-5958	15	8	establish	establish	VERB
fcis-5958	15	9	a	a	DET
fcis-5958	15	10	prediction	prediction	NOUN
fcis-5958	15	11	model	model	NOUN
fcis-5958	15	12	,	,	PUNCT
fcis-5958	15	13	which	which	PRON
fcis-5958	15	14	shortened	shorten	VERB
fcis-5958	15	15	the	the	DET
fcis-5958	15	16	prediction	prediction	NOUN
fcis-5958	15	17	time	time	NOUN
fcis-5958	15	18	.	.	PUNCT
fcis-5958	16	1	however	however	ADV
fcis-5958	16	2	,	,	PUNCT
fcis-5958	16	3	the	the	DET
fcis-5958	16	4	customer	customer	NOUN
fcis-5958	16	5	data	datum	NOUN
fcis-5958	16	6	in	in	ADP
fcis-5958	16	7	the	the	DET
fcis-5958	16	8	telecommunications	telecommunications	NOUN
fcis-5958	16	9	industry	industry	NOUN
fcis-5958	16	10	has	have	VERB
fcis-5958	16	11	the	the	DET
fcis-5958	16	12	characteristics	characteristic	NOUN
fcis-5958	16	13	of	of	ADP
fcis-5958	16	14	large	large	ADJ
fcis-5958	16	15	quantity	quantity	NOUN
fcis-5958	16	16	and	and	CCONJ
fcis-5958	16	17	high	high	ADJ
fcis-5958	16	18	dimension	dimension	NOUN
fcis-5958	16	19	.	.	PUNCT
fcis-5958	17	1	although	although	SCONJ
fcis-5958	17	2	the	the	DET
fcis-5958	17	3	above	above	ADJ
fcis-5958	17	4	research	research	NOUN
fcis-5958	17	5	has	have	AUX
fcis-5958	17	6	made	make	VERB
fcis-5958	17	7	important	important	ADJ
fcis-5958	17	8	contributions	contribution	NOUN
fcis-5958	17	9	to	to	ADP
fcis-5958	17	10	the	the	DET
fcis-5958	17	11	research	research	NOUN
fcis-5958	17	12	on	on	ADP
fcis-5958	17	13	customer	customer	NOUN
fcis-5958	17	14	churn	churn	NOUN
fcis-5958	17	15	prediction	prediction	NOUN
fcis-5958	17	16	in	in	ADP
fcis-5958	17	17	the	the	DET
fcis-5958	17	18	fields	field	NOUN
fcis-5958	17	19	of	of	ADP
fcis-5958	17	20	aviation	aviation	NOUN
fcis-5958	17	21	,	,	PUNCT
fcis-5958	17	22	life	life	NOUN
fcis-5958	17	23	insurance	insurance	NOUN
fcis-5958	17	24	,	,	PUNCT
fcis-5958	17	25	e	e	NOUN
fcis-5958	17	26	-	-	NOUN
fcis-5958	17	27	commerce	commerce	NOUN
fcis-5958	17	28	and	and	CCONJ
fcis-5958	17	29	so	so	ADV
fcis-5958	17	30	on	on	ADV
fcis-5958	17	31	,	,	PUNCT
fcis-5958	17	32	it	it	PRON
fcis-5958	17	33	does	do	AUX
fcis-5958	17	34	not	not	PART
fcis-5958	17	35	mine	mine	VERB
fcis-5958	17	36	the	the	DET
fcis-5958	17	37	important	important	ADJ
fcis-5958	17	38	features	feature	NOUN
fcis-5958	17	39	of	of	ADP
fcis-5958	17	40	the	the	DET
fcis-5958	17	41	data	datum	NOUN
fcis-5958	17	42	set	set	VERB
fcis-5958	17	43	,	,	PUNCT
fcis-5958	17	44	resulting	result	VERB
fcis-5958	17	45	in	in	ADP
fcis-5958	17	46	information	information	NOUN
fcis-5958	17	47	redundancy	redundancy	NOUN
fcis-5958	17	48	and	and	CCONJ
fcis-5958	17	49	even	even	ADV
fcis-5958	17	50	dimension	dimension	VERB
fcis-5958	17	51	disaster	disaster	NOUN
fcis-5958	18	1	[	[	X
fcis-5958	18	2	5	5	NUM
fcis-5958	18	3	]	]	PUNCT
fcis-5958	18	4	,	,	PUNCT
fcis-5958	18	5	which	which	PRON
fcis-5958	18	6	will	will	AUX
fcis-5958	18	7	have	have	VERB
fcis-5958	18	8	an	an	DET
fcis-5958	18	9	impact	impact	NOUN
fcis-5958	18	10	on	on	ADP
fcis-5958	18	11	the	the	DET
fcis-5958	18	12	customer	customer	NOUN
fcis-5958	18	13	churn	churn	NOUN
fcis-5958	18	14	prediction	prediction	NOUN
fcis-5958	18	15	in	in	ADP
fcis-5958	18	16	the	the	DET
fcis-5958	18	17	telecommunications	telecommunications	NOUN
fcis-5958	18	18	industry	industry	NOUN
fcis-5958	18	19	and	and	CCONJ
fcis-5958	18	20	reduce	reduce	VERB
fcis-5958	18	21	its	its	PRON
fcis-5958	18	22	prediction	prediction	NOUN
fcis-5958	18	23	efficiency	efficiency	NOUN
fcis-5958	18	24	and	and	CCONJ
fcis-5958	18	25	accuracy	accuracy	NOUN
fcis-5958	18	26	.	.	PUNCT
fcis-5958	19	1	at	at	ADP
fcis-5958	19	2	present	present	ADJ
fcis-5958	19	3	,	,	PUNCT
fcis-5958	19	4	the	the	DET
fcis-5958	19	5	data	datum	NOUN
fcis-5958	19	6	feature	feature	NOUN
fcis-5958	19	7	selection	selection	NOUN
fcis-5958	19	8	in	in	ADP
fcis-5958	19	9	the	the	DET
fcis-5958	19	10	field	field	NOUN
fcis-5958	19	11	of	of	ADP
fcis-5958	19	12	customer	customer	NOUN
fcis-5958	19	13	churn	churn	NOUN
fcis-5958	19	14	prediction	prediction	NOUN
fcis-5958	19	15	is	be	AUX
fcis-5958	19	16	generally	generally	ADV
fcis-5958	19	17	based	base	VERB
fcis-5958	19	18	on	on	ADP
fcis-5958	19	19	the	the	DET
fcis-5958	19	20	traditional	traditional	ADJ
fcis-5958	19	21	statistical	statistical	ADJ
fcis-5958	19	22	principal	principal	ADJ
fcis-5958	19	23	component	component	NOUN
fcis-5958	19	24	analysis	analysis	NOUN
fcis-5958	19	25	and	and	CCONJ
fcis-5958	19	26	linear	linear	ADJ
fcis-5958	19	27	discriminant	discriminant	ADJ
fcis-5958	19	28	method	method	NOUN
fcis-5958	19	29	[	[	X
fcis-5958	19	30	6	6	NUM
fcis-5958	19	31	]	]	PUNCT
fcis-5958	19	32	.	.	PUNCT
fcis-5958	20	1	its	its	PRON
fcis-5958	20	2	advantages	advantage	NOUN
fcis-5958	20	3	are	be	AUX
fcis-5958	20	4	that	that	SCONJ
fcis-5958	20	5	the	the	DET
fcis-5958	20	6	theoretical	theoretical	ADJ
fcis-5958	20	7	basis	basis	NOUN
fcis-5958	20	8	is	be	AUX
fcis-5958	20	9	solid	solid	ADJ
fcis-5958	20	10	and	and	CCONJ
fcis-5958	20	11	the	the	DET
fcis-5958	20	12	method	method	NOUN
fcis-5958	20	13	is	be	AUX
fcis-5958	20	14	simple	simple	ADJ
fcis-5958	20	15	and	and	CCONJ
fcis-5958	20	16	easy	easy	ADJ
fcis-5958	20	17	to	to	PART
fcis-5958	20	18	operate	operate	VERB
fcis-5958	20	19	,	,	PUNCT
fcis-5958	20	20	but	but	CCONJ
fcis-5958	20	21	the	the	DET
fcis-5958	20	22	nonlinear	nonlinear	ADJ
fcis-5958	20	23	relationship	relationship	NOUN
fcis-5958	20	24	between	between	ADP
fcis-5958	20	25	attributes	attribute	NOUN
fcis-5958	20	26	is	be	AUX
fcis-5958	20	27	ignored	ignore	VERB
fcis-5958	20	28	,	,	PUNCT
fcis-5958	20	29	the	the	DET
fcis-5958	20	30	information	information	NOUN
fcis-5958	20	31	is	be	AUX
fcis-5958	20	32	not	not	PART
fcis-5958	20	33	fully	fully	ADV
fcis-5958	20	34	utilized	utilize	VERB
fcis-5958	20	35	,	,	PUNCT
fcis-5958	20	36	and	and	CCONJ
fcis-5958	20	37	even	even	ADV
fcis-5958	20	38	important	important	ADJ
fcis-5958	20	39	information	information	NOUN
fcis-5958	20	40	is	be	AUX
fcis-5958	20	41	lost	lose	VERB
fcis-5958	20	42	.	.	PUNCT
fcis-5958	21	1	the	the	DET
fcis-5958	21	2	mutual	mutual	ADJ
fcis-5958	21	3	information	information	NOUN
fcis-5958	21	4	feature	feature	NOUN
fcis-5958	21	5	selection	selection	NOUN
fcis-5958	21	6	method	method	NOUN
fcis-5958	21	7	can	can	AUX
fcis-5958	21	8	retain	retain	VERB
fcis-5958	21	9	most	most	ADJ
fcis-5958	21	10	of	of	ADP
fcis-5958	21	11	the	the	DET
fcis-5958	21	12	original	original	ADJ
fcis-5958	21	13	information	information	NOUN
fcis-5958	21	14	while	while	SCONJ
fcis-5958	21	15	effectively	effectively	ADV
fcis-5958	21	16	reducing	reduce	VERB
fcis-5958	21	17	the	the	DET
fcis-5958	21	18	dimension	dimension	NOUN
fcis-5958	21	19	,	,	PUNCT
fcis-5958	21	20	and	and	CCONJ
fcis-5958	21	21	take	take	VERB
fcis-5958	21	22	into	into	ADP
fcis-5958	21	23	account	account	NOUN
fcis-5958	21	24	the	the	DET
fcis-5958	21	25	nonlinear	nonlinear	ADJ
fcis-5958	21	26	relationship	relationship	NOUN
fcis-5958	21	27	between	between	ADP
fcis-5958	21	28	variables	variable	NOUN
fcis-5958	21	29	[	[	X
fcis-5958	21	30	7	7	NUM
fcis-5958	21	31	]	]	PUNCT
fcis-5958	21	32	.	.	PUNCT
fcis-5958	22	1	in	in	ADP
fcis-5958	22	2	addition	addition	NOUN
fcis-5958	22	3	,	,	PUNCT
fcis-5958	22	4	xgboost	xgboost	X
fcis-5958	22	5	algorithm	algorithm	PROPN
fcis-5958	22	6	has	have	VERB
fcis-5958	22	7	full	full	ADJ
fcis-5958	22	8	application	application	NOUN
fcis-5958	22	9	in	in	ADP
fcis-5958	22	10	shortterm	shortterm	PROPN
fcis-5958	22	11	photovoltaic	photovoltaic	NOUN
fcis-5958	22	12	power	power	NOUN
fcis-5958	22	13	generation	generation	NOUN
fcis-5958	22	14	prediction	prediction	NOUN
fcis-5958	22	15	[	[	X
fcis-5958	22	16	8	8	NUM
fcis-5958	22	17	-	-	SYM
fcis-5958	22	18	11	11	NUM
fcis-5958	22	19	]	]	PUNCT
fcis-5958	22	20	,	,	PUNCT
fcis-5958	22	21	flight	flight	NOUN
fcis-5958	22	22	delay	delay	NOUN
fcis-5958	22	23	prediction	prediction	NOUN
fcis-5958	23	1	[	[	X
fcis-5958	23	2	12	12	NUM
fcis-5958	23	3	-	-	SYM
fcis-5958	23	4	14	14	NUM
fcis-5958	23	5	]	]	PUNCT
fcis-5958	23	6	,	,	PUNCT
fcis-5958	23	7	food	food	NOUN
fcis-5958	23	8	safety	safety	NOUN
fcis-5958	23	9	risk	risk	NOUN
fcis-5958	23	10	prediction	prediction	NOUN
fcis-5958	23	11	[	[	X
fcis-5958	23	12	15	15	NUM
fcis-5958	23	13	-	-	SYM
fcis-5958	23	14	18	18	NUM
fcis-5958	23	15	]	]	PUNCT
fcis-5958	23	16	and	and	CCONJ
fcis-5958	23	17	other	other	ADJ
fcis-5958	23	18	prediction	prediction	NOUN
fcis-5958	23	19	fields	field	NOUN
fcis-5958	23	20	.	.	PUNCT
fcis-5958	24	1	these	these	DET
fcis-5958	24	2	studies	study	NOUN
fcis-5958	24	3	show	show	VERB
fcis-5958	24	4	that	that	SCONJ
fcis-5958	24	5	xgboost	xgboost	PROPN
fcis-5958	24	6	has	have	VERB
fcis-5958	24	7	the	the	DET
fcis-5958	24	8	advantages	advantage	NOUN
fcis-5958	24	9	of	of	ADP
fcis-5958	24	10	high	high	ADJ
fcis-5958	24	11	flexibility	flexibility	NOUN
fcis-5958	24	12	,	,	PUNCT
fcis-5958	24	13	fast	fast	ADJ
fcis-5958	24	14	execution	execution	NOUN
fcis-5958	24	15	speed	speed	NOUN
fcis-5958	24	16	,	,	PUNCT
fcis-5958	24	17	and	and	CCONJ
fcis-5958	24	18	high	high	ADJ
fcis-5958	24	19	accuracy	accuracy	NOUN
fcis-5958	24	20	of	of	ADP
fcis-5958	24	21	prediction	prediction	NOUN
fcis-5958	24	22	results	result	NOUN
fcis-5958	24	23	.	.	PUNCT
fcis-5958	25	1	based	base	VERB
fcis-5958	25	2	on	on	ADP
fcis-5958	25	3	this	this	PRON
fcis-5958	25	4	,	,	PUNCT
fcis-5958	25	5	this	this	DET
fcis-5958	25	6	paper	paper	NOUN
fcis-5958	25	7	proposes	propose	VERB
fcis-5958	25	8	an	an	DET
fcis-5958	25	9	xgboost	xgboost	ADJ
fcis-5958	25	10	prediction	prediction	NOUN
fcis-5958	25	11	method	method	NOUN
fcis-5958	25	12	based	base	VERB
fcis-5958	25	13	on	on	ADP
fcis-5958	25	14	mutual	mutual	ADJ
fcis-5958	25	15	information	information	NOUN
fcis-5958	25	16	feature	feature	NOUN
fcis-5958	25	17	selection	selection	NOUN
fcis-5958	25	18	to	to	PART
fcis-5958	25	19	solve	solve	VERB
fcis-5958	25	20	the	the	DET
fcis-5958	25	21	problem	problem	NOUN
fcis-5958	25	22	of	of	ADP
fcis-5958	25	23	customer	customer	NOUN
fcis-5958	25	24	churn	churn	NOUN
fcis-5958	25	25	in	in	ADP
fcis-5958	25	26	the	the	DET
fcis-5958	25	27	telecommunications	telecommunications	NOUN
fcis-5958	25	28	industry	industry	NOUN
fcis-5958	25	29	,	,	PUNCT
fcis-5958	25	30	and	and	CCONJ
fcis-5958	25	31	compares	compare	VERB
fcis-5958	25	32	the	the	DET
fcis-5958	25	33	accuracy	accuracy	NOUN
fcis-5958	25	34	,	,	PUNCT
fcis-5958	25	35	recall	recall	NOUN
fcis-5958	25	36	,	,	PUNCT
fcis-5958	25	37	and	and	CCONJ
fcis-5958	25	38	f	f	X
fcis-5958	25	39	_	_	NOUN
fcis-5958	25	40	score	score	NOUN
fcis-5958	25	41	and	and	CCONJ
fcis-5958	25	42	other	other	ADJ
fcis-5958	25	43	indicators	indicator	NOUN
fcis-5958	25	44	,	,	PUNCT
fcis-5958	25	45	and	and	CCONJ
fcis-5958	25	46	the	the	DET
fcis-5958	25	47	analysis	analysis	NOUN
fcis-5958	25	48	proves	prove	VERB
fcis-5958	25	49	the	the	DET
fcis-5958	25	50	effectiveness	effectiveness	NOUN
fcis-5958	25	51	of	of	ADP
fcis-5958	25	52	the	the	DET
fcis-5958	25	53	method	method	NOUN
fcis-5958	25	54	proposed	propose	VERB
fcis-5958	25	55	in	in	ADP
fcis-5958	25	56	this	this	DET
fcis-5958	25	57	paper	paper	NOUN
fcis-5958	25	58	in	in	ADP
fcis-5958	25	59	telecom	telecom	NOUN
fcis-5958	25	60	customer	customer	NOUN
fcis-5958	25	61	churn	churn	NOUN
fcis-5958	25	62	prediction	prediction	NOUN
fcis-5958	25	63	.	.	PUNCT
fcis-5958	26	1	2	2	X
fcis-5958	26	2	.	.	X
fcis-5958	26	3	mipca	mipca	PROPN
fcis-5958	26	4	-	-	PUNCT
fcis-5958	26	5	xgboost	xgboost	PROPN
fcis-5958	26	6	customer	customer	NOUN
fcis-5958	26	7	churn	churn	NOUN
fcis-5958	26	8	prediction	prediction	NOUN
fcis-5958	26	9	method	method	NOUN
fcis-5958	26	10	2.1	2.1	NUM
fcis-5958	26	11	.	.	PUNCT
fcis-5958	27	1	mipca	mipca	PROPN
fcis-5958	27	2	method	method	PROPN
fcis-5958	27	3	in	in	ADP
fcis-5958	27	4	information	information	NOUN
fcis-5958	27	5	theory	theory	NOUN
fcis-5958	27	6	,	,	PUNCT
fcis-5958	27	7	mutual	mutual	ADJ
fcis-5958	27	8	information	information	NOUN
fcis-5958	27	9	is	be	AUX
fcis-5958	27	10	a	a	DET
fcis-5958	27	11	measure	measure	NOUN
fcis-5958	27	12	of	of	ADP
fcis-5958	27	13	interdependence	interdependence	NOUN
fcis-5958	27	14	between	between	ADP
fcis-5958	27	15	random	random	ADJ
fcis-5958	27	16	variables	variable	NOUN
fcis-5958	27	17	,	,	PUNCT
fcis-5958	27	18	which	which	PRON
fcis-5958	27	19	can	can	AUX
fcis-5958	27	20	also	also	ADV
fcis-5958	27	21	be	be	AUX
fcis-5958	27	22	understood	understand	VERB
fcis-5958	27	23	as	as	ADP
fcis-5958	27	24	the	the	DET
fcis-5958	27	25	amount	amount	NOUN
fcis-5958	27	26	of	of	ADP
fcis-5958	27	27	information	information	NOUN
fcis-5958	27	28	of	of	ADP
fcis-5958	27	29	another	another	DET
fcis-5958	27	30	random	random	ADJ
fcis-5958	27	31	variable	variable	NOUN
fcis-5958	27	32	contained	contain	VERB
fcis-5958	27	33	in	in	ADP
fcis-5958	27	34	one	one	NUM
fcis-5958	27	35	random	random	ADJ
fcis-5958	27	36	variable	variable	NOUN
fcis-5958	27	37	[	[	X
fcis-5958	27	38	11	11	NUM
fcis-5958	27	39	]	]	PUNCT
fcis-5958	27	40	.	.	PUNCT
fcis-5958	28	1	in	in	ADP
fcis-5958	28	2	feature	feature	NOUN
fcis-5958	28	3	selection	selection	NOUN
fcis-5958	28	4	,	,	PUNCT
fcis-5958	28	5	the	the	DET
fcis-5958	28	6	principal	principal	ADJ
fcis-5958	28	7	component	component	NOUN
fcis-5958	28	8	analysis	analysis	NOUN
fcis-5958	28	9	method	method	NOUN
fcis-5958	28	10	in	in	ADP
fcis-5958	28	11	traditional	traditional	ADJ
fcis-5958	28	12	statistics	statistic	NOUN
fcis-5958	28	13	is	be	AUX
fcis-5958	28	14	to	to	PART
fcis-5958	28	15	analyze	analyze	VERB
fcis-5958	28	16	the	the	DET
fcis-5958	28	17	linear	linear	ADJ
fcis-5958	28	18	relationship	relationship	NOUN
fcis-5958	28	19	between	between	ADP
fcis-5958	28	20	two	two	NUM
fcis-5958	28	21	variables	variable	NOUN
fcis-5958	28	22	,	,	PUNCT
fcis-5958	28	23	but	but	CCONJ
fcis-5958	28	24	can	can	AUX
fcis-5958	28	25	not	not	PART
fcis-5958	28	26	reflect	reflect	VERB
fcis-5958	28	27	the	the	DET
fcis-5958	28	28	nonlinear	nonlinear	ADJ
fcis-5958	28	29	relationship	relationship	NOUN
fcis-5958	28	30	in	in	ADP
fcis-5958	28	31	the	the	DET
fcis-5958	28	32	data	datum	NOUN
fcis-5958	28	33	.	.	PUNCT
fcis-5958	29	1	therefore	therefore	ADV
fcis-5958	29	2	,	,	PUNCT
fcis-5958	29	3	this	this	DET
fcis-5958	29	4	paper	paper	NOUN
fcis-5958	29	5	considers	consider	VERB
fcis-5958	29	6	introducing	introduce	VERB
fcis-5958	29	7	mutual	mutual	ADJ
fcis-5958	29	8	information	information	NOUN
fcis-5958	29	9	to	to	PART
fcis-5958	29	10	feature	feature	VERB
fcis-5958	29	11	selection	selection	NOUN
fcis-5958	29	12	,	,	PUNCT
fcis-5958	29	13	which	which	PRON
fcis-5958	29	14	is	be	AUX
fcis-5958	29	15	very	very	ADV
fcis-5958	29	16	helpful	helpful	ADJ
fcis-5958	29	17	for	for	ADP
fcis-5958	29	18	evaluating	evaluate	VERB
fcis-5958	29	19	the	the	DET
fcis-5958	29	20	interdependence	interdependence	NOUN
fcis-5958	29	21	between	between	ADP
fcis-5958	29	22	variables	variable	NOUN
fcis-5958	29	23	,	,	PUNCT
fcis-5958	29	24	and	and	CCONJ
fcis-5958	29	25	is	be	AUX
fcis-5958	29	26	no	no	ADV
fcis-5958	29	27	longer	long	ADV
fcis-5958	29	28	limited	limit	VERB
fcis-5958	29	29	to	to	ADP
fcis-5958	29	30	the	the	DET
fcis-5958	29	31	linear	linear	PROPN
fcis-5958	29	32	relationship	relationship	NOUN
fcis-5958	29	33	.	.	PUNCT
fcis-5958	30	1	here	here	ADV
fcis-5958	30	2	we	we	PRON
fcis-5958	30	3	introduce	introduce	VERB
fcis-5958	30	4	information	information	NOUN
fcis-5958	30	5	entropy	entropy	PROPN
fcis-5958	30	6	h	h	PROPN
fcis-5958	30	7	,	,	PUNCT
fcis-5958	30	8	which	which	PRON
fcis-5958	30	9	is	be	AUX
fcis-5958	30	10	used	use	VERB
fcis-5958	30	11	to	to	PART
fcis-5958	30	12	represent	represent	VERB
fcis-5958	30	13	the	the	DET
fcis-5958	30	14	uncertainty	uncertainty	NOUN
fcis-5958	30	15	of	of	ADP
fcis-5958	30	16	a	a	DET
fcis-5958	30	17	random	random	ADJ
fcis-5958	30	18	variable	variable	NOUN
fcis-5958	30	19	.	.	PUNCT
fcis-5958	31	1	suppose	suppose	VERB
fcis-5958	31	2	there	there	PRON
fcis-5958	31	3	are	be	VERB
fcis-5958	31	4	random	random	ADJ
fcis-5958	31	5	variables	variable	NOUN
fcis-5958	31	6	x	x	PUNCT
fcis-5958	31	7	and	and	CCONJ
fcis-5958	31	8	y	y	PROPN
fcis-5958	31	9	in	in	ADP
fcis-5958	31	10	data	datum	NOUN
fcis-5958	31	11	set	set	VERB
fcis-5958	31	12	m	m	PROPN
fcis-5958	31	13	,	,	PUNCT
fcis-5958	31	14	and	and	CCONJ
fcis-5958	31	15	there	there	PRON
fcis-5958	31	16	are	be	VERB
fcis-5958	31	17	joint	joint	ADJ
fcis-5958	31	18	distributions	distribution	NOUN
fcis-5958	31	19	𝑝(𝑥	𝑝(𝑥	ADP
fcis-5958	31	20	,	,	PUNCT
fcis-5958	31	21	𝑦	𝑦	NOUN
fcis-5958	31	22	)	)	PUNCT
fcis-5958	31	23	and	and	CCONJ
fcis-5958	31	24	marginal	marginal	ADJ
fcis-5958	31	25	distributions	distribution	NOUN
fcis-5958	31	26	𝑝(𝑥	𝑝(𝑥	NUM
fcis-5958	31	27	)	)	PUNCT
fcis-5958	31	28	and	and	CCONJ
fcis-5958	31	29	𝑝(𝑦	𝑝(𝑦	PROPN
fcis-5958	31	30	)	)	PUNCT
fcis-5958	31	31	,	,	PUNCT
fcis-5958	31	32	then	then	ADV
fcis-5958	31	33	there	there	PRON
fcis-5958	31	34	are	be	VERB
fcis-5958	31	35	2	2	NUM
fcis-5958	31	36	𝐻(𝑋	𝐻(𝑋	NOUN
fcis-5958	31	37	,	,	PUNCT
fcis-5958	31	38	𝑌	𝑌	PROPN
fcis-5958	31	39	)	)	PUNCT
fcis-5958	31	40	=	=	PUNCT
fcis-5958	31	41	𝐻(𝑋	𝐻(𝑋	NOUN
fcis-5958	31	42	)	)	PUNCT
fcis-5958	31	43	+	+	CCONJ
fcis-5958	31	44	𝐻(𝑌|𝑋	𝐻(𝑌|𝑋	X
fcis-5958	31	45	)	)	PUNCT
fcis-5958	31	46	=	=	PUNCT
fcis-5958	31	47	𝐻(𝑌	𝐻(𝑌	NOUN
fcis-5958	31	48	)	)	PUNCT
fcis-5958	31	49	+	+	NUM
fcis-5958	31	50	𝐻(𝑋|𝑌	𝐻(𝑋|𝑌	NOUN
fcis-5958	31	51	)	)	PUNCT
fcis-5958	31	52	(	(	PUNCT
fcis-5958	31	53	1	1	X
fcis-5958	31	54	)	)	PUNCT
fcis-5958	31	55	where	where	SCONJ
fcis-5958	31	56	,	,	PUNCT
fcis-5958	31	57	𝐻(𝑋|𝑌	𝐻(𝑋|𝑌	NOUN
fcis-5958	31	58	)	)	PUNCT
fcis-5958	31	59	and	and	CCONJ
fcis-5958	31	60	𝐻(𝑌|𝑋	𝐻(𝑌|𝑋	NOUN
fcis-5958	31	61	)	)	PUNCT
fcis-5958	31	62	are	be	AUX
fcis-5958	31	63	conditional	conditional	ADJ
fcis-5958	31	64	information	information	NOUN
fcis-5958	31	65	entropy	entropy	PROPN
fcis-5958	31	66	,	,	PUNCT
fcis-5958	31	67	which	which	PRON
fcis-5958	31	68	represents	represent	VERB
fcis-5958	31	69	the	the	DET
fcis-5958	31	70	information	information	NOUN
fcis-5958	31	71	entropy	entropy	NOUN
fcis-5958	31	72	of	of	ADP
fcis-5958	31	73	its	its	PRON
fcis-5958	31	74	own	own	ADJ
fcis-5958	31	75	information	information	NOUN
fcis-5958	31	76	after	after	ADP
fcis-5958	31	77	deducting	deduct	VERB
fcis-5958	31	78	other	other	ADJ
fcis-5958	31	79	conditions	condition	NOUN
fcis-5958	31	80	,	,	PUNCT
fcis-5958	31	81	which	which	PRON
fcis-5958	31	82	can	can	AUX
fcis-5958	31	83	be	be	AUX
fcis-5958	31	84	expressed	express	VERB
fcis-5958	31	85	as	as	ADP
fcis-5958	31	86	𝐻(𝑋|𝑌	𝐻(𝑋|𝑌	NOUN
fcis-5958	31	87	)	)	PUNCT
fcis-5958	31	88	=	=	SYM
fcis-5958	32	1	−	−	PROPN
fcis-5958	32	2	∑	∑	PUNCT
fcis-5958	32	3	𝑝(𝑥	𝑝(𝑥	PROPN
fcis-5958	32	4	,	,	PUNCT
fcis-5958	32	5	𝑦)𝑙𝑜𝑔𝑝(𝑥|𝑦)𝑥∈𝑋	𝑦)𝑙𝑜𝑔𝑝(𝑥|𝑦)𝑥∈𝑋	X
fcis-5958	32	6	(	(	PUNCT
fcis-5958	32	7	2	2	NUM
fcis-5958	32	8	)	)	PUNCT
fcis-5958	32	9	𝐻(𝑌|𝑋	𝐻(𝑌|𝑋	X
fcis-5958	32	10	)	)	PUNCT
fcis-5958	32	11	=	=	SYM
fcis-5958	32	12	−	−	PROPN
fcis-5958	32	13	∑	∑	PUNCT
fcis-5958	32	14	𝑝(𝑥	𝑝(𝑥	PROPN
fcis-5958	32	15	,	,	PUNCT
fcis-5958	32	16	𝑦)𝑙𝑜𝑔𝑝(𝑦|𝑥)𝑦∈𝑌	𝑦)𝑙𝑜𝑔𝑝(𝑦|𝑥)𝑦∈𝑌	NOUN
fcis-5958	32	17	(	(	PUNCT
fcis-5958	32	18	3	3	NUM
fcis-5958	32	19	)	)	PUNCT
fcis-5958	32	20	for	for	ADP
fcis-5958	32	21	random	random	ADJ
fcis-5958	32	22	variables	variable	NOUN
fcis-5958	32	23	x	x	PUNCT
fcis-5958	32	24	and	and	CCONJ
fcis-5958	32	25	y	y	PROPN
fcis-5958	32	26	,	,	PUNCT
fcis-5958	32	27	the	the	DET
fcis-5958	32	28	amount	amount	NOUN
fcis-5958	32	29	of	of	ADP
fcis-5958	32	30	information	information	NOUN
fcis-5958	32	31	of	of	ADP
fcis-5958	32	32	another	another	DET
fcis-5958	32	33	variable	variable	NOUN
fcis-5958	32	34	contained	contain	VERB
fcis-5958	32	35	in	in	ADP
fcis-5958	32	36	one	one	NUM
fcis-5958	32	37	variable	variable	NOUN
fcis-5958	32	38	can	can	AUX
fcis-5958	32	39	be	be	AUX
fcis-5958	32	40	expressed	express	VERB
fcis-5958	32	41	by	by	ADP
fcis-5958	32	42	mutual	mutual	ADJ
fcis-5958	32	43	information	information	NOUN
fcis-5958	32	44	[	[	X
fcis-5958	32	45	11	11	NUM
fcis-5958	32	46	]	]	PUNCT
fcis-5958	32	47	,	,	PUNCT
fcis-5958	32	48	which	which	PRON
fcis-5958	32	49	is	be	AUX
fcis-5958	32	50	defined	define	VERB
fcis-5958	32	51	as	as	ADP
fcis-5958	32	52	𝐼(𝑋	𝐼(𝑋	ADJ
fcis-5958	32	53	,	,	PUNCT
fcis-5958	32	54	𝑌	𝑌	PROPN
fcis-5958	32	55	)	)	PUNCT
fcis-5958	32	56	=	=	PUNCT
fcis-5958	32	57	𝐻(𝑋	𝐻(𝑋	NOUN
fcis-5958	32	58	)	)	PUNCT
fcis-5958	32	59	−	−	PROPN
fcis-5958	32	60	𝐻(𝑋|𝑌	𝐻(𝑋|𝑌	NOUN
fcis-5958	32	61	)	)	PUNCT
fcis-5958	32	62	=	=	SYM
fcis-5958	32	63	𝐻(𝑌	𝐻(𝑌	PROPN
fcis-5958	32	64	)	)	PUNCT
fcis-5958	32	65	−	−	PRON
fcis-5958	32	66	𝐻(𝑌|𝑋	𝐻(𝑌|𝑋	X
fcis-5958	32	67	)	)	PUNCT
fcis-5958	32	68	(	(	PUNCT
fcis-5958	32	69	4	4	X
fcis-5958	32	70	)	)	PUNCT
fcis-5958	32	71	the	the	DET
fcis-5958	32	72	information	information	NOUN
fcis-5958	32	73	entropy	entropy	NOUN
fcis-5958	32	74	of	of	ADP
fcis-5958	32	75	random	random	ADJ
fcis-5958	32	76	variables	variable	NOUN
fcis-5958	32	77	x	x	PUNCT
fcis-5958	32	78	and	and	CCONJ
fcis-5958	32	79	y	y	PROPN
fcis-5958	32	80	can	can	AUX
fcis-5958	32	81	be	be	AUX
fcis-5958	32	82	expressed	express	VERB
fcis-5958	32	83	as	as	ADP
fcis-5958	32	84	𝐻(𝑋	𝐻(𝑋	NOUN
fcis-5958	32	85	)	)	PUNCT
fcis-5958	32	86	=	=	SYM
fcis-5958	33	1	−	−	PROPN
fcis-5958	33	2	∑	∑	PUNCT
fcis-5958	33	3	𝑝(𝑥)𝑙𝑜𝑔𝑝(𝑥)𝑥∈𝑋	𝑝(𝑥)𝑙𝑜𝑔𝑝(𝑥)𝑥∈𝑋	PROPN
fcis-5958	33	4	(	(	PUNCT
fcis-5958	33	5	5	5	NUM
fcis-5958	33	6	)	)	PUNCT
fcis-5958	33	7	𝐻(𝑌	𝐻(𝑌	NOUN
fcis-5958	33	8	)	)	PUNCT
fcis-5958	33	9	=	=	SYM
fcis-5958	34	1	−	−	PROPN
fcis-5958	34	2	∑	∑	PUNCT
fcis-5958	34	3	𝑝(𝑦)𝑙𝑜𝑔𝑝(𝑦)𝑦∈𝑌	𝑝(𝑦)𝑙𝑜𝑔𝑝(𝑦)𝑦∈𝑌	NOUN
fcis-5958	34	4	(	(	PUNCT
fcis-5958	34	5	6	6	NUM
fcis-5958	34	6	)	)	PUNCT
fcis-5958	34	7	therefore	therefore	ADV
fcis-5958	34	8	,	,	PUNCT
fcis-5958	34	9	we	we	PRON
fcis-5958	34	10	can	can	AUX
fcis-5958	34	11	further	far	ADV
fcis-5958	34	12	obtain	obtain	VERB
fcis-5958	34	13	the	the	DET
fcis-5958	34	14	mutual	mutual	ADJ
fcis-5958	34	15	information	information	NOUN
fcis-5958	34	16	𝐼(𝑋	𝐼(𝑋	PROPN
fcis-5958	34	17	,	,	PUNCT
fcis-5958	34	18	𝑌	𝑌	PROPN
fcis-5958	34	19	)	)	PUNCT
fcis-5958	34	20	of	of	ADP
fcis-5958	34	21	random	random	ADJ
fcis-5958	34	22	variables	variable	NOUN
fcis-5958	34	23	x	x	PUNCT
fcis-5958	34	24	and	and	CCONJ
fcis-5958	34	25	y	y	PROPN
fcis-5958	34	26	as	as	ADP
fcis-5958	34	27	𝐼(𝑋	𝐼(𝑋	ADJ
fcis-5958	34	28	,	,	PUNCT
fcis-5958	34	29	𝑌	𝑌	PROPN
fcis-5958	34	30	)	)	PUNCT
fcis-5958	34	31	=	=	SYM
fcis-5958	34	32	∑	∑	PUNCT
fcis-5958	34	33	∑	∑	PUNCT
fcis-5958	34	34	𝑝(𝑥	𝑝(𝑥	PROPN
fcis-5958	34	35	,	,	PUNCT
fcis-5958	34	36	𝑦)𝑙𝑜𝑔	𝑦)𝑙𝑜𝑔	NOUN
fcis-5958	34	37	𝑝(𝑥,𝑦	𝑝(𝑥,𝑦	NUM
fcis-5958	34	38	)	)	PUNCT
fcis-5958	34	39	𝑝(𝑥)𝑝(𝑦)𝑦∈𝑌𝑥∈𝑋	𝑝(𝑥)𝑝(𝑦)𝑦∈𝑌𝑥∈𝑋	NOUN
fcis-5958	34	40	(	(	PUNCT
fcis-5958	34	41	7	7	NUM
fcis-5958	34	42	)	)	PUNCT
fcis-5958	34	43	in	in	ADP
fcis-5958	34	44	the	the	DET
fcis-5958	34	45	principal	principal	ADJ
fcis-5958	34	46	component	component	NOUN
fcis-5958	34	47	analysis	analysis	NOUN
fcis-5958	34	48	method	method	NOUN
fcis-5958	34	49	,	,	PUNCT
fcis-5958	34	50	the	the	DET
fcis-5958	34	51	mutual	mutual	ADJ
fcis-5958	34	52	information	information	NOUN
fcis-5958	34	53	matrix	matrix	NOUN
fcis-5958	34	54	is	be	AUX
fcis-5958	34	55	used	use	VERB
fcis-5958	34	56	to	to	PART
fcis-5958	34	57	replace	replace	VERB
fcis-5958	34	58	the	the	DET
fcis-5958	34	59	covariance	covariance	NOUN
fcis-5958	34	60	matrix	matrix	NOUN
fcis-5958	34	61	,	,	PUNCT
fcis-5958	34	62	and	and	CCONJ
fcis-5958	34	63	the	the	DET
fcis-5958	34	64	relationship	relationship	NOUN
fcis-5958	34	65	between	between	ADP
fcis-5958	34	66	the	the	DET
fcis-5958	34	67	eigenvector	eigenvector	NOUN
fcis-5958	34	68	and	and	CCONJ
fcis-5958	34	69	the	the	DET
fcis-5958	34	70	eigenvalues	eigenvalue	NOUN
fcis-5958	34	71	of	of	ADP
fcis-5958	34	72	the	the	DET
fcis-5958	34	73	mutual	mutual	ADJ
fcis-5958	34	74	information	information	NOUN
fcis-5958	34	75	feature	feature	NOUN
fcis-5958	34	76	selection	selection	NOUN
fcis-5958	34	77	method	method	NOUN
fcis-5958	34	78	is	be	AUX
fcis-5958	34	79	obtained	obtain	VERB
fcis-5958	34	80	as	as	ADP
fcis-5958	34	81	𝐴𝑇	𝐴𝑇	PROPN
fcis-5958	34	82	𝛴𝐼	𝛴𝐼	PROPN
fcis-5958	34	83	𝐴=	𝐴=	PROPN
fcis-5958	34	84	𝐵	𝐵	PROPN
fcis-5958	34	85	(	(	PUNCT
fcis-5958	34	86	8)	8)	NUM
fcis-5958	34	87	among	among	ADP
fcis-5958	34	88	them	they	PRON
fcis-5958	34	89	,	,	PUNCT
fcis-5958	34	90	𝛴𝐼	𝛴𝐼	PROPN
fcis-5958	34	91	is	be	AUX
fcis-5958	34	92	the	the	DET
fcis-5958	34	93	mutual	mutual	ADJ
fcis-5958	34	94	information	information	NOUN
fcis-5958	34	95	matrix	matrix	NOUN
fcis-5958	34	96	corresponding	correspond	VERB
fcis-5958	34	97	to	to	ADP
fcis-5958	34	98	the	the	DET
fcis-5958	34	99	data	datum	NOUN
fcis-5958	34	100	set	set	VERB
fcis-5958	34	101	m	m	PROPN
fcis-5958	34	102	,	,	PUNCT
fcis-5958	34	103	𝐴	𝐴	PROPN
fcis-5958	34	104	is	be	AUX
fcis-5958	34	105	the	the	DET
fcis-5958	34	106	matrix	matrix	NOUN
fcis-5958	34	107	formed	form	VERB
fcis-5958	34	108	when	when	SCONJ
fcis-5958	34	109	the	the	DET
fcis-5958	34	110	eigenvector	eigenvector	NOUN
fcis-5958	34	111	of	of	ADP
fcis-5958	34	112	the	the	DET
fcis-5958	34	113	mutual	mutual	ADJ
fcis-5958	34	114	information	information	NOUN
fcis-5958	34	115	matrix	matrix	NOUN
fcis-5958	34	116	is	be	AUX
fcis-5958	34	117	a	a	DET
fcis-5958	34	118	column	column	NOUN
fcis-5958	34	119	vector	vector	NOUN
fcis-5958	34	120	,	,	PUNCT
fcis-5958	34	121	and	and	CCONJ
fcis-5958	34	122	𝐵	𝐵	NOUN
fcis-5958	34	123	is	be	AUX
fcis-5958	34	124	the	the	DET
fcis-5958	34	125	eigenvalue	eigenvalue	ADJ
fcis-5958	34	126	matrix	matrix	NOUN
fcis-5958	34	127	of	of	ADP
fcis-5958	34	128	the	the	DET
fcis-5958	34	129	mutual	mutual	ADJ
fcis-5958	34	130	information	information	NOUN
fcis-5958	34	131	matrix	matrix	NOUN
fcis-5958	34	132	.	.	PUNCT
fcis-5958	35	1	in	in	ADP
fcis-5958	35	2	𝛴𝐼	𝛴𝐼	PROPN
fcis-5958	35	3	,	,	PUNCT
fcis-5958	35	4	the	the	DET
fcis-5958	35	5	elements	element	NOUN
fcis-5958	35	6	on	on	ADP
fcis-5958	35	7	the	the	DET
fcis-5958	35	8	diagonal	diagonal	ADJ
fcis-5958	35	9	represent	represent	VERB
fcis-5958	35	10	the	the	DET
fcis-5958	35	11	self	self	NOUN
fcis-5958	35	12	-	-	PUNCT
fcis-5958	35	13	information	information	NOUN
fcis-5958	35	14	of	of	ADP
fcis-5958	35	15	variables	variable	NOUN
fcis-5958	35	16	,	,	PUNCT
fcis-5958	35	17	while	while	SCONJ
fcis-5958	35	18	the	the	DET
fcis-5958	35	19	elements	element	NOUN
fcis-5958	35	20	on	on	ADP
fcis-5958	35	21	the	the	DET
fcis-5958	35	22	non	non	ADJ
fcis-5958	35	23	-	-	ADJ
fcis-5958	35	24	diagonal	diagonal	ADJ
fcis-5958	35	25	represent	represent	VERB
fcis-5958	35	26	the	the	DET
fcis-5958	35	27	mutual	mutual	ADJ
fcis-5958	35	28	information	information	NOUN
fcis-5958	35	29	of	of	ADP
fcis-5958	35	30	variables	variable	NOUN
fcis-5958	35	31	.	.	PUNCT
fcis-5958	36	1	when	when	SCONJ
fcis-5958	36	2	the	the	DET
fcis-5958	36	3	two	two	NUM
fcis-5958	36	4	variables	variable	NOUN
fcis-5958	36	5	are	be	AUX
fcis-5958	36	6	not	not	PART
fcis-5958	36	7	related	relate	VERB
fcis-5958	36	8	,	,	PUNCT
fcis-5958	36	9	the	the	DET
fcis-5958	36	10	mutual	mutual	ADJ
fcis-5958	36	11	information	information	NOUN
fcis-5958	36	12	is	be	AUX
fcis-5958	36	13	0	0	NUM
fcis-5958	36	14	.	.	PUNCT
fcis-5958	37	1	the	the	DET
fcis-5958	37	2	principal	principal	ADJ
fcis-5958	37	3	component	component	NOUN
fcis-5958	37	4	𝑐	𝑐	PROPN
fcis-5958	37	5	of	of	ADP
fcis-5958	37	6	mutual	mutual	ADJ
fcis-5958	37	7	information	information	NOUN
fcis-5958	37	8	matrix	matrix	NOUN
fcis-5958	37	9	𝛴𝐼	𝛴𝐼	NOUN
fcis-5958	37	10	is	be	AUX
fcis-5958	37	11	𝑐𝑘	𝑐𝑘	NOUN
fcis-5958	37	12	=	=	PUNCT
fcis-5958	37	13	𝛼𝑘	𝛼𝑘	NOUN
fcis-5958	37	14	𝑇𝑥	𝑇𝑥	PROPN
fcis-5958	37	15	(	(	PUNCT
fcis-5958	37	16	9	9	NUM
fcis-5958	37	17	)	)	PUNCT
fcis-5958	37	18	among	among	ADP
fcis-5958	37	19	them	they	PRON
fcis-5958	37	20	,	,	PUNCT
fcis-5958	37	21	𝛼𝑘	𝛼𝑘	ADV
fcis-5958	37	22	𝑇(𝑘	𝑇(𝑘	NOUN
fcis-5958	37	23	=	=	SYM
fcis-5958	37	24	1,2	1,2	NUM
fcis-5958	37	25	,	,	PUNCT
fcis-5958	37	26	.	.	PUNCT
fcis-5958	37	27	.	.	PUNCT
fcis-5958	38	1	.	.	PUNCT
fcis-5958	39	1	,	,	PUNCT
fcis-5958	39	2	𝑛	𝑛	X
fcis-5958	39	3	)	)	PUNCT
fcis-5958	39	4	∈𝐴𝑇	∈𝐴𝑇	NOUN
fcis-5958	39	5	,	,	PUNCT
fcis-5958	39	6	is	be	AUX
fcis-5958	39	7	conversion	conversion	NOUN
fcis-5958	39	8	coefficient	coefficient	NOUN
fcis-5958	39	9	of	of	ADP
fcis-5958	39	10	𝑐𝑘	𝑐𝑘	NOUN
fcis-5958	39	11	.	.	PUNCT
fcis-5958	40	1	define	define	VERB
fcis-5958	40	2	the	the	DET
fcis-5958	40	3	contribution	contribution	NOUN
fcis-5958	40	4	rate	rate	NOUN
fcis-5958	40	5	of	of	ADP
fcis-5958	40	6	the	the	DET
fcis-5958	40	7	kth	kth	PROPN
fcis-5958	40	8	feature	feature	NOUN
fcis-5958	40	9	as	as	ADP
fcis-5958	40	10	𝜎𝑘.	𝜎𝑘.	NOUN
fcis-5958	40	11	the	the	DET
fcis-5958	40	12	formula	formula	NOUN
fcis-5958	40	13	𝜎𝑘	𝜎𝑘	PRON
fcis-5958	40	14	=	=	PUNCT
fcis-5958	40	15	𝜇𝑘	𝜇𝑘	NOUN
fcis-5958	40	16	∑	∑	PUNCT
fcis-5958	40	17	𝜇𝑘	𝜇𝑘	ADP
fcis-5958	40	18	𝑛	𝑛	DET
fcis-5958	40	19	𝑘=1	𝑘=1	X
fcis-5958	40	20	(	(	PUNCT
fcis-5958	40	21	10	10	NUM
fcis-5958	40	22	)	)	PUNCT
fcis-5958	40	23	𝜇𝑘	𝜇𝑘	NOUN
fcis-5958	40	24	is	be	AUX
fcis-5958	40	25	the	the	DET
fcis-5958	40	26	𝑘	𝑘	ADV
fcis-5958	40	27	th	th	X
fcis-5958	40	28	largest	large	ADJ
fcis-5958	40	29	eigenvalue	eigenvalue	NOUN
fcis-5958	40	30	of	of	ADP
fcis-5958	40	31	the	the	DET
fcis-5958	40	32	mutual	mutual	ADJ
fcis-5958	40	33	information	information	NOUN
fcis-5958	40	34	matrix	matrix	NOUN
fcis-5958	40	35	.	.	PUNCT
fcis-5958	41	1	in	in	ADP
fcis-5958	41	2	this	this	DET
fcis-5958	41	3	paper	paper	NOUN
fcis-5958	41	4	,	,	PUNCT
fcis-5958	41	5	the	the	DET
fcis-5958	41	6	first	first	ADJ
fcis-5958	41	7	m	m	NOUN
fcis-5958	41	8	principal	principal	ADJ
fcis-5958	41	9	components	component	NOUN
fcis-5958	41	10	whose	whose	DET
fcis-5958	41	11	cumulative	cumulative	ADJ
fcis-5958	41	12	contribution	contribution	NOUN
fcis-5958	41	13	rate	rate	NOUN
fcis-5958	41	14	of	of	ADP
fcis-5958	41	15	𝛽	𝛽	NOUN
fcis-5958	41	16	is	be	AUX
fcis-5958	41	17	about	about	ADV
fcis-5958	41	18	90	90	NUM
fcis-5958	41	19	%	%	NOUN
fcis-5958	41	20	are	be	AUX
fcis-5958	41	21	selected	select	VERB
fcis-5958	41	22	as	as	ADP
fcis-5958	41	23	the	the	DET
fcis-5958	41	24	output	output	NOUN
fcis-5958	41	25	of	of	ADP
fcis-5958	41	26	the	the	DET
fcis-5958	41	27	feature	feature	NOUN
fcis-5958	41	28	selection	selection	NOUN
fcis-5958	41	29	results	result	NOUN
fcis-5958	41	30	,	,	PUNCT
fcis-5958	41	31	and	and	CCONJ
fcis-5958	41	32	the	the	DET
fcis-5958	41	33	dimensionality	dimensionality	NOUN
fcis-5958	41	34	reduction	reduction	NOUN
fcis-5958	41	35	of	of	ADP
fcis-5958	41	36	the	the	DET
fcis-5958	41	37	data	datum	NOUN
fcis-5958	41	38	set	set	VERB
fcis-5958	41	39	is	be	AUX
fcis-5958	41	40	realized	realize	VERB
fcis-5958	41	41	.	.	PUNCT
fcis-5958	42	1	2.2	2.2	NUM
fcis-5958	42	2	.	.	PUNCT
fcis-5958	42	3	principles	principle	NOUN
fcis-5958	42	4	of	of	ADP
fcis-5958	42	5	xgboost	xgboost	PROPN
fcis-5958	42	6	model	model	PROPN
fcis-5958	42	7	xgboost	xgboost	PROPN
fcis-5958	42	8	algorithm	algorithm	PROPN
fcis-5958	42	9	is	be	AUX
fcis-5958	42	10	a	a	DET
fcis-5958	42	11	model	model	NOUN
fcis-5958	42	12	that	that	PRON
fcis-5958	42	13	uses	use	VERB
fcis-5958	42	14	cart	cart	NOUN
fcis-5958	42	15	tree	tree	NOUN
fcis-5958	42	16	as	as	ADP
fcis-5958	42	17	the	the	DET
fcis-5958	42	18	base	base	ADJ
fcis-5958	42	19	learner	learner	NOUN
fcis-5958	42	20	for	for	ADP
fcis-5958	42	21	training	training	NOUN
fcis-5958	42	22	and	and	CCONJ
fcis-5958	42	23	combines	combine	VERB
fcis-5958	42	24	multiple	multiple	ADJ
fcis-5958	42	25	base	base	NOUN
fcis-5958	42	26	learners	learner	NOUN
fcis-5958	42	27	to	to	PART
fcis-5958	42	28	construct	construct	VERB
fcis-5958	42	29	a	a	DET
fcis-5958	42	30	strong	strong	ADJ
fcis-5958	42	31	classifier	classifier	NOUN
fcis-5958	42	32	.	.	PUNCT
fcis-5958	43	1	xgboost	xgboost	PROPN
fcis-5958	43	2	is	be	AUX
fcis-5958	43	3	used	use	VERB
fcis-5958	43	4	to	to	PART
fcis-5958	43	5	train	train	VERB
fcis-5958	43	6	the	the	DET
fcis-5958	43	7	data	datum	NOUN
fcis-5958	43	8	set	set	VERB
fcis-5958	43	9	,	,	PUNCT
fcis-5958	43	10	divide	divide	VERB
fcis-5958	43	11	the	the	DET
fcis-5958	43	12	sample	sample	NOUN
fcis-5958	43	13	data	datum	NOUN
fcis-5958	43	14	into	into	ADP
fcis-5958	43	15	each	each	DET
fcis-5958	43	16	leaf	leaf	NOUN
fcis-5958	43	17	node	node	NOUN
fcis-5958	43	18	according	accord	VERB
fcis-5958	43	19	to	to	ADP
fcis-5958	43	20	different	different	ADJ
fcis-5958	43	21	classification	classification	NOUN
fcis-5958	43	22	characteristics	characteristic	NOUN
fcis-5958	43	23	,	,	PUNCT
fcis-5958	43	24	calculate	calculate	VERB
fcis-5958	43	25	the	the	DET
fcis-5958	43	26	gain	gain	NOUN
fcis-5958	43	27	value	value	NOUN
fcis-5958	43	28	of	of	ADP
fcis-5958	43	29	the	the	DET
fcis-5958	43	30	tree	tree	NOUN
fcis-5958	43	31	model	model	NOUN
fcis-5958	43	32	before	before	ADP
fcis-5958	43	33	and	and	CCONJ
fcis-5958	43	34	after	after	ADP
fcis-5958	43	35	classification	classification	NOUN
fcis-5958	43	36	,	,	PUNCT
fcis-5958	43	37	and	and	CCONJ
fcis-5958	43	38	finally	finally	ADV
fcis-5958	43	39	obtain	obtain	VERB
fcis-5958	43	40	a	a	DET
fcis-5958	43	41	training	training	NOUN
fcis-5958	43	42	model	model	NOUN
fcis-5958	43	43	with	with	ADP
fcis-5958	43	44	the	the	DET
fcis-5958	43	45	minimum	minimum	ADJ
fcis-5958	43	46	loss	loss	NOUN
fcis-5958	43	47	value	value	NOUN
fcis-5958	43	48	.	.	PUNCT
fcis-5958	44	1	the	the	DET
fcis-5958	44	2	objective	objective	ADJ
fcis-5958	44	3	function	function	NOUN
fcis-5958	44	4	can	can	AUX
fcis-5958	44	5	be	be	AUX
fcis-5958	44	6	expressed	express	VERB
fcis-5958	44	7	as	as	ADP
fcis-5958	44	8	𝑂𝑏𝑗	𝑂𝑏𝑗	PROPN
fcis-5958	44	9	=	=	SYM
fcis-5958	44	10	∑	∑	PUNCT
fcis-5958	44	11	𝑙(𝑥	𝑙(𝑥	PROPN
fcis-5958	44	12	,	,	PUNCT
fcis-5958	44	13	𝑥	𝑥	NOUN
fcis-5958	44	14	�	�	PROPN
fcis-5958	44	15	̂	̂	NOUN
fcis-5958	44	16	�	�	NOUN
fcis-5958	44	17	)	)	PUNCT
fcis-5958	45	1	+	+	CCONJ
fcis-5958	45	2	∑	∑	SYM
fcis-5958	45	3	ω(𝑓𝑡)𝑇	ω(𝑓𝑡)𝑇	NOUN
fcis-5958	45	4	𝑡=1	𝑡=1	NOUN
fcis-5958	45	5	𝑛	𝑛	PRON
fcis-5958	45	6	𝑖=1	𝑖=1	PROPN
fcis-5958	45	7	(	(	PUNCT
fcis-5958	45	8	11	11	NUM
fcis-5958	45	9	)	)	PUNCT
fcis-5958	45	10	where	where	SCONJ
fcis-5958	45	11	𝑙	𝑙	PROPN
fcis-5958	45	12	is	be	AUX
fcis-5958	45	13	the	the	DET
fcis-5958	45	14	loss	loss	NOUN
fcis-5958	45	15	function	function	NOUN
fcis-5958	45	16	,	,	PUNCT
fcis-5958	45	17	𝑥𝑖	𝑥𝑖	PROPN
fcis-5958	45	18	is	be	AUX
fcis-5958	45	19	the	the	DET
fcis-5958	45	20	true	true	ADJ
fcis-5958	45	21	value	value	NOUN
fcis-5958	45	22	of	of	ADP
fcis-5958	45	23	the	the	DET
fcis-5958	45	24	ith	ith	PROPN
fcis-5958	45	25	sample	sample	PROPN
fcis-5958	45	26	data	data	PROPN
fcis-5958	45	27	,	,	PUNCT
fcis-5958	45	28	𝑥	𝑥	NOUN
fcis-5958	45	29	�	�	PROPN
fcis-5958	45	30	̂	̂	VERB
fcis-5958	45	31	�	�	NOUN
fcis-5958	45	32	is	be	AUX
fcis-5958	45	33	the	the	DET
fcis-5958	45	34	predicted	predict	VERB
fcis-5958	45	35	value	value	NOUN
fcis-5958	45	36	of	of	ADP
fcis-5958	45	37	the	the	DET
fcis-5958	45	38	ith	ith	PROPN
fcis-5958	45	39	sample	sample	NOUN
fcis-5958	45	40	,	,	PUNCT
fcis-5958	45	41	n	n	X
fcis-5958	45	42	is	be	AUX
fcis-5958	45	43	the	the	DET
fcis-5958	45	44	total	total	ADJ
fcis-5958	45	45	number	number	NOUN
fcis-5958	45	46	of	of	ADP
fcis-5958	45	47	samples	sample	NOUN
fcis-5958	45	48	,	,	PUNCT
fcis-5958	45	49	ω(𝑓𝑡	ω(𝑓𝑡	NUM
fcis-5958	45	50	)	)	PUNCT
fcis-5958	45	51	is	be	AUX
fcis-5958	45	52	the	the	DET
fcis-5958	45	53	penalty	penalty	NOUN
fcis-5958	45	54	term	term	NOUN
fcis-5958	45	55	controlling	control	VERB
fcis-5958	45	56	the	the	DET
fcis-5958	45	57	complexity	complexity	NOUN
fcis-5958	45	58	of	of	ADP
fcis-5958	45	59	the	the	DET
fcis-5958	45	60	model	model	NOUN
fcis-5958	45	61	in	in	ADP
fcis-5958	45	62	the	the	DET
fcis-5958	45	63	t	t	PROPN
fcis-5958	45	64	th	th	X
fcis-5958	45	65	tree	tree	NOUN
fcis-5958	45	66	,	,	PUNCT
fcis-5958	45	67	and	and	CCONJ
fcis-5958	45	68	t	t	PROPN
fcis-5958	45	69	is	be	AUX
fcis-5958	45	70	the	the	DET
fcis-5958	45	71	number	number	NOUN
fcis-5958	45	72	of	of	ADP
fcis-5958	45	73	training	training	NOUN
fcis-5958	45	74	trees	tree	NOUN
fcis-5958	45	75	.	.	PUNCT
fcis-5958	46	1	xgboost	xgboost	PROPN
fcis-5958	46	2	is	be	AUX
fcis-5958	46	3	a	a	DET
fcis-5958	46	4	superposition	superposition	NOUN
fcis-5958	46	5	training	training	NOUN
fcis-5958	46	6	model	model	NOUN
fcis-5958	46	7	.	.	PUNCT
fcis-5958	47	1	when	when	SCONJ
fcis-5958	47	2	training	train	VERB
fcis-5958	47	3	the	the	DET
fcis-5958	47	4	t	t	PROPN
fcis-5958	47	5	-	-	PUNCT
fcis-5958	47	6	th	th	X
fcis-5958	47	7	tree	tree	NOUN
fcis-5958	47	8	,	,	PUNCT
fcis-5958	47	9	the	the	DET
fcis-5958	47	10	objective	objective	ADJ
fcis-5958	47	11	function	function	NOUN
fcis-5958	47	12	of	of	ADP
fcis-5958	47	13	the	the	DET
fcis-5958	47	14	t	t	PROPN
fcis-5958	47	15	-	-	PUNCT
fcis-5958	47	16	th	th	VERB
fcis-5958	47	17	tree	tree	NOUN
fcis-5958	47	18	is	be	AUX
fcis-5958	47	19	𝑂𝑏𝑗𝑡	𝑂𝑏𝑗𝑡	PROPN
fcis-5958	47	20	=	=	SYM
fcis-5958	47	21	∑	∑	PUNCT
fcis-5958	47	22	𝑙	𝑙	PROPN
fcis-5958	47	23	(	(	PUNCT
fcis-5958	47	24	𝑥𝑖	𝑥𝑖	PROPN
fcis-5958	47	25	,	,	PUNCT
fcis-5958	47	26	𝑥	𝑥	NOUN
fcis-5958	47	27	�	�	PROPN
fcis-5958	47	28	̂	̂	SYM
fcis-5958	47	29	�	�	PROPN
fcis-5958	47	30	(	(	PUNCT
fcis-5958	47	31	𝑡−1	𝑡−1	PROPN
fcis-5958	47	32	)	)	PUNCT
fcis-5958	47	33	+	+	CCONJ
fcis-5958	47	34	𝑓𝑡(𝑥𝑖	𝑓𝑡(𝑥𝑖	ADJ
fcis-5958	47	35	)	)	PUNCT
fcis-5958	47	36	)	)	PUNCT
fcis-5958	48	1	+	+	CCONJ
fcis-5958	48	2	ω(𝑓𝑡	ω(𝑓𝑡	NUM
fcis-5958	48	3	)	)	PUNCT
fcis-5958	48	4	+	+	NUM
fcis-5958	48	5	𝑐𝑛	𝑐𝑛	X
fcis-5958	48	6	𝑖=1	𝑖=1	PUNCT
fcis-5958	48	7	(	(	PUNCT
fcis-5958	48	8	12	12	NUM
fcis-5958	48	9	)	)	PUNCT
fcis-5958	48	10	𝑓𝑡(𝑥𝑖	𝑓𝑡(𝑥𝑖	ADJ
fcis-5958	48	11	)	)	PUNCT
fcis-5958	48	12	=	=	SYM
fcis-5958	48	13	𝑤𝑞(𝑥𝑖	𝑤𝑞(𝑥𝑖	VERB
fcis-5958	48	14	)	)	PUNCT
fcis-5958	48	15	(	(	PUNCT
fcis-5958	48	16	13	13	NUM
fcis-5958	48	17	)	)	PUNCT
fcis-5958	48	18	where	where	SCONJ
fcis-5958	48	19	,	,	PUNCT
fcis-5958	48	20	𝑓𝑡(𝑥𝑖	𝑓𝑡(𝑥𝑖	PROPN
fcis-5958	48	21	)	)	PUNCT
fcis-5958	48	22	is	be	AUX
fcis-5958	48	23	the	the	DET
fcis-5958	48	24	weight	weight	NOUN
fcis-5958	48	25	of	of	ADP
fcis-5958	48	26	sample	sample	NOUN
fcis-5958	48	27	𝑥𝑖	𝑥𝑖	PROPN
fcis-5958	48	28	in	in	ADP
fcis-5958	48	29	the	the	DET
fcis-5958	48	30	t	t	PROPN
fcis-5958	48	31	-	-	PUNCT
fcis-5958	48	32	th	th	X
fcis-5958	48	33	tree	tree	NOUN
fcis-5958	48	34	,	,	PUNCT
fcis-5958	48	35	and	and	CCONJ
fcis-5958	48	36	its	its	PRON
fcis-5958	48	37	formula	formula	NOUN
fcis-5958	48	38	is	be	AUX
fcis-5958	48	39	shown	show	VERB
fcis-5958	48	40	in	in	ADP
fcis-5958	48	41	equation	equation	NOUN
fcis-5958	48	42	(	(	PUNCT
fcis-5958	48	43	13	13	NUM
fcis-5958	48	44	)	)	PUNCT
fcis-5958	48	45	,	,	PUNCT
fcis-5958	48	46	c	c	PROPN
fcis-5958	48	47	is	be	AUX
fcis-5958	48	48	a	a	DET
fcis-5958	48	49	constant	constant	ADJ
fcis-5958	48	50	,	,	PUNCT
fcis-5958	48	51	𝑞(𝑥𝑖	𝑞(𝑥𝑖	NUM
fcis-5958	48	52	)	)	PUNCT
fcis-5958	48	53	is	be	AUX
fcis-5958	48	54	the	the	DET
fcis-5958	48	55	position	position	NOUN
fcis-5958	48	56	of	of	ADP
fcis-5958	48	57	the	the	DET
fcis-5958	48	58	ith	ith	PROPN
fcis-5958	48	59	sample	sample	NOUN
fcis-5958	48	60	on	on	ADP
fcis-5958	48	61	the	the	DET
fcis-5958	48	62	t	t	PROPN
fcis-5958	48	63	-	-	PUNCT
fcis-5958	48	64	th	th	X
fcis-5958	48	65	tree	tree	NOUN
fcis-5958	48	66	,	,	PUNCT
fcis-5958	48	67	which	which	PRON
fcis-5958	48	68	is	be	AUX
fcis-5958	48	69	specifically	specifically	ADV
fcis-5958	48	70	represented	represent	VERB
fcis-5958	48	71	by	by	ADP
fcis-5958	48	72	the	the	DET
fcis-5958	48	73	number	number	NOUN
fcis-5958	48	74	of	of	ADP
fcis-5958	48	75	leaf	leaf	NOUN
fcis-5958	48	76	nodes	node	NOUN
fcis-5958	48	77	it	it	PRON
fcis-5958	48	78	falls	fall	VERB
fcis-5958	48	79	on	on	ADP
fcis-5958	48	80	,	,	PUNCT
fcis-5958	48	81	and	and	CCONJ
fcis-5958	48	82	𝑤𝑞(𝑥𝑖	𝑤𝑞(𝑥𝑖	VERB
fcis-5958	48	83	)	)	PUNCT
fcis-5958	48	84	represents	represent	VERB
fcis-5958	48	85	the	the	DET
fcis-5958	48	86	weight	weight	NOUN
fcis-5958	48	87	of	of	ADP
fcis-5958	48	88	the	the	DET
fcis-5958	48	89	leaf	leaf	NOUN
fcis-5958	48	90	node	node	NOUN
fcis-5958	48	91	.	.	PUNCT
fcis-5958	49	1	the	the	DET
fcis-5958	49	2	second	second	ADJ
fcis-5958	49	3	-	-	PUNCT
fcis-5958	49	4	order	order	NOUN
fcis-5958	49	5	taylor	taylor	PROPN
fcis-5958	49	6	series	series	PROPN
fcis-5958	49	7	is	be	AUX
fcis-5958	49	8	used	use	VERB
fcis-5958	49	9	to	to	PART
fcis-5958	49	10	expand	expand	VERB
fcis-5958	49	11	the	the	DET
fcis-5958	49	12	loss	loss	NOUN
fcis-5958	49	13	function	function	NOUN
fcis-5958	49	14	,	,	PUNCT
fcis-5958	49	15	and	and	CCONJ
fcis-5958	49	16	the	the	DET
fcis-5958	49	17	objective	objective	ADJ
fcis-5958	49	18	function	function	NOUN
fcis-5958	49	19	after	after	ADP
fcis-5958	49	20	processing	processing	NOUN
fcis-5958	49	21	is	be	AUX
fcis-5958	49	22	𝑂𝑏𝑗𝑡	𝑂𝑏𝑗𝑡	NOUN
fcis-5958	49	23	=	=	PUNCT
fcis-5958	49	24	∑	∑	PUNCT
fcis-5958	50	1	[	[	X
fcis-5958	50	2	𝐺𝑘𝑤𝑘	𝐺𝑘𝑤𝑘	PROPN
fcis-5958	50	3	+	+	CCONJ
fcis-5958	50	4	1	1	NUM
fcis-5958	50	5	2	2	NUM
fcis-5958	50	6	(	(	PUNCT
fcis-5958	50	7	𝐻𝑘	𝐻𝑘	PROPN
fcis-5958	50	8	+	+	CCONJ
fcis-5958	50	9	𝜆)𝑤𝑘	𝜆)𝑤𝑘	PROPN
fcis-5958	50	10	2	2	NUM
fcis-5958	50	11	]	]	PUNCT
fcis-5958	50	12	+	+	NUM
fcis-5958	50	13	𝛾𝐾𝐾	𝛾𝐾𝐾	NOUN
fcis-5958	50	14	𝑘=1	𝑘=1	NOUN
fcis-5958	50	15	(	(	PUNCT
fcis-5958	50	16	14	14	NUM
fcis-5958	50	17	)	)	PUNCT
fcis-5958	50	18	∑	∑	PUNCT
fcis-5958	50	19	𝑔𝑖	𝑔𝑖	NOUN
fcis-5958	50	20	=	=	SYM
fcis-5958	50	21	𝐺𝑘𝑖∈𝐼𝑘	𝐺𝑘𝑖∈𝐼𝑘	NOUN
fcis-5958	50	22	(	(	PUNCT
fcis-5958	50	23	15	15	NUM
fcis-5958	50	24	)	)	PUNCT
fcis-5958	50	25	∑	∑	ADV
fcis-5958	50	26	ℎ𝑖𝑖∈𝐼𝑘	ℎ𝑖𝑖∈𝐼𝑘	PROPN
fcis-5958	50	27	=	=	SYM
fcis-5958	50	28	𝐻𝑘	𝐻𝑘	PROPN
fcis-5958	50	29	(	(	PUNCT
fcis-5958	50	30	16	16	NUM
fcis-5958	50	31	)	)	PUNCT
fcis-5958	50	32	where	where	SCONJ
fcis-5958	50	33	,	,	PUNCT
fcis-5958	50	34	𝐼𝑘	𝐼𝑘	PROPN
fcis-5958	50	35	represents	represent	VERB
fcis-5958	50	36	all	all	DET
fcis-5958	50	37	samples	sample	NOUN
fcis-5958	50	38	falling	fall	VERB
fcis-5958	50	39	on	on	ADP
fcis-5958	50	40	the	the	DET
fcis-5958	50	41	kth	kth	PROPN
fcis-5958	50	42	leaf	leaf	NOUN
fcis-5958	50	43	node	node	NOUN
fcis-5958	50	44	,	,	PUNCT
fcis-5958	50	45	and	and	CCONJ
fcis-5958	50	46	𝑔𝑖	𝑔𝑖	NOUN
fcis-5958	50	47	and	and	CCONJ
fcis-5958	50	48	ℎ𝑖	ℎ𝑖	NOUN
fcis-5958	50	49	are	be	AUX
fcis-5958	50	50	the	the	DET
fcis-5958	50	51	replacements	replacement	NOUN
fcis-5958	50	52	of	of	ADP
fcis-5958	50	53	the	the	DET
fcis-5958	50	54	first	first	ADJ
fcis-5958	50	55	and	and	CCONJ
fcis-5958	50	56	second	second	ADJ
fcis-5958	50	57	derivatives	derivative	NOUN
fcis-5958	50	58	of	of	ADP
fcis-5958	50	59	the	the	DET
fcis-5958	50	60	loss	loss	NOUN
fcis-5958	50	61	function	function	NOUN
fcis-5958	50	62	respectively	respectively	ADV
fcis-5958	50	63	.	.	PUNCT
fcis-5958	51	1	for	for	ADP
fcis-5958	51	2	each	each	DET
fcis-5958	51	3	sample	sample	NOUN
fcis-5958	51	4	that	that	PRON
fcis-5958	51	5	has	have	AUX
fcis-5958	51	6	been	be	AUX
fcis-5958	51	7	trained	train	VERB
fcis-5958	51	8	t-1	t-1	PROPN
fcis-5958	51	9	times	time	NOUN
fcis-5958	51	10	,	,	PUNCT
fcis-5958	51	11	its	its	PRON
fcis-5958	51	12	g	g	NOUN
fcis-5958	51	13	and	and	CCONJ
fcis-5958	51	14	h	h	NOUN
fcis-5958	51	15	are	be	AUX
fcis-5958	51	16	known	know	VERB
fcis-5958	51	17	.	.	PUNCT
fcis-5958	52	1	𝐺𝑘	𝐺𝑘	DET
fcis-5958	52	2	and𝐻𝑘	and𝐻𝑘	NOUN
fcis-5958	52	3	are	be	AUX
fcis-5958	52	4	the	the	DET
fcis-5958	52	5	sum	sum	NOUN
fcis-5958	52	6	of	of	ADP
fcis-5958	52	7	g	g	PROPN
fcis-5958	52	8	and	and	CCONJ
fcis-5958	52	9	h	h	NOUN
fcis-5958	52	10	of	of	ADP
fcis-5958	52	11	all	all	DET
fcis-5958	52	12	samples	sample	NOUN
fcis-5958	52	13	on	on	ADP
fcis-5958	52	14	k	k	ADJ
fcis-5958	52	15	leaf	leaf	NOUN
fcis-5958	52	16	nodes	node	NOUN
fcis-5958	52	17	respectively	respectively	ADV
fcis-5958	52	18	.	.	PUNCT
fcis-5958	53	1	by	by	ADP
fcis-5958	53	2	observing	observe	VERB
fcis-5958	53	3	the	the	DET
fcis-5958	53	4	simplified	simplified	ADJ
fcis-5958	53	5	objective	objective	ADJ
fcis-5958	53	6	function	function	NOUN
fcis-5958	53	7	formula	formula	NOUN
fcis-5958	53	8	,	,	PUNCT
fcis-5958	53	9	it	it	PRON
fcis-5958	53	10	can	can	AUX
fcis-5958	53	11	be	be	AUX
fcis-5958	53	12	seen	see	VERB
fcis-5958	53	13	that	that	SCONJ
fcis-5958	53	14	this	this	PRON
fcis-5958	53	15	is	be	AUX
fcis-5958	53	16	a	a	DET
fcis-5958	53	17	quadratic	quadratic	ADJ
fcis-5958	53	18	equation	equation	NOUN
fcis-5958	53	19	with	with	ADP
fcis-5958	53	20	one	one	NUM
fcis-5958	53	21	variable	variable	NOUN
fcis-5958	53	22	,	,	PUNCT
fcis-5958	53	23	and	and	CCONJ
fcis-5958	53	24	the	the	DET
fcis-5958	53	25	extreme	extreme	ADJ
fcis-5958	53	26	value	value	NOUN
fcis-5958	53	27	is	be	AUX
fcis-5958	53	28	𝑤∗	𝑤∗	NOUN
fcis-5958	53	29	=	=	SYM
fcis-5958	53	30	−	−	PUNCT
fcis-5958	54	1	𝐺𝑘	𝐺𝑘	NOUN
fcis-5958	54	2	𝐻𝑘	𝐻𝑘	PROPN
fcis-5958	54	3	+	+	CCONJ
fcis-5958	54	4	𝜆	𝜆	X
fcis-5958	54	5	(	(	PUNCT
fcis-5958	54	6	17	17	NUM
fcis-5958	54	7	)	)	PUNCT
fcis-5958	54	8	by	by	ADP
fcis-5958	54	9	substituting	substitute	VERB
fcis-5958	54	10	the	the	DET
fcis-5958	54	11	extreme	extreme	ADJ
fcis-5958	54	12	value	value	NOUN
fcis-5958	54	13	into	into	ADP
fcis-5958	54	14	equation	equation	NOUN
fcis-5958	54	15	(	(	PUNCT
fcis-5958	54	16	14	14	NUM
fcis-5958	54	17	)	)	PUNCT
fcis-5958	54	18	,	,	PUNCT
fcis-5958	54	19	the	the	DET
fcis-5958	54	20	expression	expression	NOUN
fcis-5958	54	21	of	of	ADP
fcis-5958	54	22	leaf	leaf	NOUN
fcis-5958	54	23	nodes	node	NOUN
fcis-5958	54	24	in	in	ADP
fcis-5958	54	25	the	the	DET
fcis-5958	54	26	tree	tree	NOUN
fcis-5958	54	27	to	to	PART
fcis-5958	54	28	be	be	AUX
fcis-5958	54	29	trained	train	VERB
fcis-5958	54	30	can	can	AUX
fcis-5958	54	31	be	be	AUX
fcis-5958	54	32	written	write	VERB
fcis-5958	54	33	as	as	ADP
fcis-5958	54	34	𝑂𝑏𝑗	𝑂𝑏𝑗	PROPN
fcis-5958	54	35	=	=	SYM
fcis-5958	54	36	−	−	PROPN
fcis-5958	54	37	1	1	NUM
fcis-5958	54	38	2	2	NUM
fcis-5958	54	39	∑	∑	PUNCT
fcis-5958	54	40	𝐺𝑘	𝐺𝑘	DET
fcis-5958	54	41	2	2	NUM
fcis-5958	54	42	𝐻𝑘	𝐻𝑘	NOUN
fcis-5958	54	43	+	+	CCONJ
fcis-5958	54	44	𝜆	𝜆	PROPN
fcis-5958	54	45	𝐾	𝐾	PROPN
fcis-5958	54	46	𝑘=1	𝑘=1	PROPN
fcis-5958	54	47	+	+	CCONJ
fcis-5958	54	48	𝛾𝐾	𝛾𝐾	NOUN
fcis-5958	54	49	(	(	PUNCT
fcis-5958	54	50	18	18	NUM
fcis-5958	54	51	)	)	PUNCT
fcis-5958	54	52	xgboost	xgboost	NOUN
fcis-5958	55	1	model	model	PROPN
fcis-5958	55	2	calculates	calculate	VERB
fcis-5958	55	3	the	the	DET
fcis-5958	55	4	gain	gain	NOUN
fcis-5958	55	5	value	value	NOUN
fcis-5958	55	6	by	by	ADP
fcis-5958	55	7	calculating	calculate	VERB
fcis-5958	55	8	𝑂𝑏𝑗𝑂𝐿𝐷	𝑂𝑏𝑗𝑂𝐿𝐷	PROPN
fcis-5958	55	9	−	−	PROPN
fcis-5958	55	10	𝑂𝑏𝑗𝑁𝐸𝑊	𝑂𝑏𝑗𝑁𝐸𝑊	NOUN
fcis-5958	55	11	:	:	PUNCT
fcis-5958	55	12	𝐺𝑎𝑖𝑛	𝐺𝑎𝑖𝑛	PROPN
fcis-5958	55	13	=	=	SYM
fcis-5958	55	14	1	1	NUM
fcis-5958	55	15	2	2	NUM
fcis-5958	55	16	[	[	PUNCT
fcis-5958	55	17	𝐺𝐿	𝐺𝐿	PROPN
fcis-5958	55	18	2	2	NUM
fcis-5958	55	19	𝐻𝐿	𝐻𝐿	PROPN
fcis-5958	55	20	+	+	CCONJ
fcis-5958	55	21	𝜆	𝜆	PROPN
fcis-5958	55	22	+	+	CCONJ
fcis-5958	55	23	𝐺𝑅	𝐺𝑅	PROPN
fcis-5958	55	24	2	2	NUM
fcis-5958	55	25	𝐻𝑅	𝐻𝑅	NOUN
fcis-5958	55	26	+	+	NOUN
fcis-5958	55	27	𝜆	𝜆	NOUN
fcis-5958	55	28	−	−	PROPN
fcis-5958	55	29	(	(	PUNCT
fcis-5958	55	30	𝐺𝐿	𝐺𝐿	PROPN
fcis-5958	55	31	+	+	CCONJ
fcis-5958	55	32	𝐺𝑅)2	𝐺𝑅)2	ADJ
fcis-5958	55	33	𝐻𝐿	𝐻𝐿	PROPN
fcis-5958	55	34	+	+	SYM
fcis-5958	55	35	𝐻𝑅	𝐻𝑅	PROPN
fcis-5958	55	36	+	+	CCONJ
fcis-5958	55	37	𝜆	𝜆	NOUN
fcis-5958	55	38	]	]	X
fcis-5958	55	39	–	–	PUNCT
fcis-5958	55	40	𝛾	𝛾	X
fcis-5958	55	41	(	(	PUNCT
fcis-5958	55	42	19	19	NUM
fcis-5958	55	43	)	)	PUNCT
fcis-5958	55	44	the	the	PRON
fcis-5958	55	45	higher	high	ADJ
fcis-5958	55	46	the	the	DET
fcis-5958	55	47	gain	gain	NOUN
fcis-5958	55	48	score	score	NOUN
fcis-5958	55	49	,	,	PUNCT
fcis-5958	55	50	the	the	PRON
fcis-5958	55	51	higher	high	ADJ
fcis-5958	55	52	the	the	DET
fcis-5958	55	53	feature	feature	NOUN
fcis-5958	55	54	importance	importance	NOUN
fcis-5958	55	55	score	score	NOUN
fcis-5958	55	56	of	of	ADP
fcis-5958	55	57	the	the	DET
fcis-5958	55	58	split	split	NOUN
fcis-5958	55	59	node	node	NOUN
fcis-5958	55	60	,	,	PUNCT
fcis-5958	55	61	and	and	CCONJ
fcis-5958	55	62	the	the	DET
fcis-5958	55	63	more	more	ADV
fcis-5958	55	64	important	important	ADJ
fcis-5958	55	65	its	its	PRON
fcis-5958	55	66	corresponding	corresponding	ADJ
fcis-5958	55	67	feature	feature	NOUN
fcis-5958	55	68	.	.	PUNCT
fcis-5958	56	1	2.3	2.3	NUM
fcis-5958	56	2	.	.	PUNCT
fcis-5958	56	3	mipca	mipca	PROPN
fcis-5958	56	4	-	-	PUNCT
fcis-5958	56	5	xgboost	xgboost	VERB
fcis-5958	56	6	step	step	VERB
fcis-5958	56	7	the	the	DET
fcis-5958	56	8	procedure	procedure	NOUN
fcis-5958	56	9	is	be	AUX
fcis-5958	56	10	as	as	SCONJ
fcis-5958	56	11	follows	follow	VERB
fcis-5958	56	12	:	:	PUNCT
fcis-5958	56	13	1)feature	1)feature	NUM
fcis-5958	56	14	screening	screening	NOUN
fcis-5958	56	15	was	be	AUX
fcis-5958	56	16	performed	perform	VERB
fcis-5958	56	17	on	on	ADP
fcis-5958	56	18	the	the	DET
fcis-5958	56	19	data	datum	NOUN
fcis-5958	56	20	set	set	VERB
fcis-5958	56	21	by	by	ADP
fcis-5958	56	22	mipca	mipca	ADJ
fcis-5958	56	23	method	method	PROPN
fcis-5958	56	24	,	,	PUNCT
fcis-5958	56	25	and	and	CCONJ
fcis-5958	56	26	the	the	DET
fcis-5958	56	27	first	first	ADJ
fcis-5958	56	28	m	m	PROPN
fcis-5958	56	29	principal	principal	ADJ
fcis-5958	56	30	components	component	NOUN
fcis-5958	56	31	were	be	AUX
fcis-5958	56	32	obtained	obtain	VERB
fcis-5958	56	33	as	as	ADP
fcis-5958	56	34	new	new	ADJ
fcis-5958	56	35	features	feature	NOUN
fcis-5958	56	36	,	,	PUNCT
fcis-5958	56	37	and	and	CCONJ
fcis-5958	56	38	the	the	DET
fcis-5958	56	39	dimensionality	dimensionality	NOUN
fcis-5958	56	40	reduction	reduction	NOUN
fcis-5958	56	41	of	of	ADP
fcis-5958	56	42	the	the	DET
fcis-5958	56	43	initial	initial	ADJ
fcis-5958	56	44	data	datum	NOUN
fcis-5958	56	45	set	set	VERB
fcis-5958	56	46	was	be	AUX
fcis-5958	56	47	realized	realize	VERB
fcis-5958	56	48	;	;	PUNCT
fcis-5958	56	49	2)randomly	2)randomly	NUM
fcis-5958	56	50	divide	divide	VERB
fcis-5958	56	51	the	the	DET
fcis-5958	56	52	new	new	ADJ
fcis-5958	56	53	data	datum	NOUN
fcis-5958	56	54	set	set	VERB
fcis-5958	56	55	after	after	ADP
fcis-5958	56	56	feature	feature	NOUN
fcis-5958	56	57	selection	selection	NOUN
fcis-5958	56	58	processing	processing	NOUN
fcis-5958	56	59	,	,	PUNCT
fcis-5958	56	60	and	and	CCONJ
fcis-5958	56	61	the	the	DET
fcis-5958	56	62	divided	divide	VERB
fcis-5958	56	63	training	training	NOUN
fcis-5958	56	64	set	set	NOUN
fcis-5958	56	65	is	be	AUX
fcis-5958	56	66	used	use	VERB
fcis-5958	56	67	as	as	ADP
fcis-5958	56	68	the	the	DET
fcis-5958	56	69	data	data	NOUN
fcis-5958	56	70	input	input	NOUN
fcis-5958	56	71	of	of	ADP
fcis-5958	56	72	the	the	DET
fcis-5958	56	73	model	model	NOUN
fcis-5958	56	74	,	,	PUNCT
fcis-5958	56	75	and	and	CCONJ
fcis-5958	56	76	the	the	DET
fcis-5958	56	77	test	test	NOUN
fcis-5958	56	78	set	set	NOUN
fcis-5958	56	79	is	be	AUX
fcis-5958	56	80	used	use	VERB
fcis-5958	56	81	to	to	PART
fcis-5958	56	82	verify	verify	VERB
fcis-5958	56	83	the	the	DET
fcis-5958	56	84	trained	train	VERB
fcis-5958	56	85	model	model	NOUN
fcis-5958	56	86	.	.	PUNCT
fcis-5958	57	1	the	the	DET
fcis-5958	57	2	flow	flow	NOUN
fcis-5958	57	3	chart	chart	NOUN
fcis-5958	57	4	of	of	ADP
fcis-5958	57	5	mipca	mipca	PROPN
fcis-5958	57	6	-	-	PUNCT
fcis-5958	57	7	xgboost	xgboost	PROPN
fcis-5958	57	8	is	be	AUX
fcis-5958	57	9	shown	show	VERB
fcis-5958	57	10	in	in	ADP
fcis-5958	57	11	figure	figure	NOUN
fcis-5958	57	12	1	1	NUM
fcis-5958	57	13	.	.	SYM
fcis-5958	57	14	3	3	NUM
fcis-5958	57	15	telecom	telecom	NOUN
fcis-5958	57	16	customer	customer	NOUN
fcis-5958	57	17	data	datum	NOUN
fcis-5958	57	18	set	set	VERB
fcis-5958	57	19	mipca	mipca	ADJ
fcis-5958	57	20	decomposition	decomposition	NOUN
fcis-5958	57	21	deaverage	deaverage	NOUN
fcis-5958	57	22	build	build	NOUN
fcis-5958	57	23	sample	sample	NOUN
fcis-5958	57	24	mutual	mutual	ADJ
fcis-5958	57	25	information	information	NOUN
fcis-5958	57	26	matrix	matrix	NOUN
fcis-5958	57	27	计算特征值与特征向量	计算特征值与特征向量	NOUN
fcis-5958	57	28	select	select	ADJ
fcis-5958	57	29	principal	principal	ADJ
fcis-5958	57	30	components	component	NOUN
fcis-5958	57	31	import	import	PROPN
fcis-5958	57	32	xgboost	xgboost	ADV
fcis-5958	57	33	set	set	VERB
fcis-5958	57	34	initial	initial	ADJ
fcis-5958	57	35	parameters	parameter	NOUN
fcis-5958	57	36	data	data	PROPN
fcis-5958	57	37	training	training	NOUN
fcis-5958	57	38	test	test	NOUN
fcis-5958	57	39	set	set	VERB
fcis-5958	57	40	prediction	prediction	NOUN
fcis-5958	57	41	output	output	NOUN
fcis-5958	57	42	results	result	VERB
fcis-5958	57	43	whether	whether	SCONJ
fcis-5958	57	44	the	the	DET
fcis-5958	57	45	maximum	maximum	ADJ
fcis-5958	57	46	number	number	NOUN
fcis-5958	57	47	of	of	ADP
fcis-5958	57	48	iterations	iteration	NOUN
fcis-5958	57	49	has	have	AUX
fcis-5958	57	50	been	be	AUX
fcis-5958	57	51	reached	reach	VERB
fcis-5958	57	52	no	no	DET
fcis-5958	57	53	yes	yes	INTJ
fcis-5958	57	54	fig	fig	NOUN
fcis-5958	57	55	.	.	PUNCT
fcis-5958	58	1	1	1	NUM
fcis-5958	59	1	the	the	DET
fcis-5958	59	2	flow	flow	NOUN
fcis-5958	59	3	chart	chart	NOUN
fcis-5958	59	4	of	of	ADP
fcis-5958	59	5	mipca	mipca	PROPN
fcis-5958	59	6	-	-	PUNCT
fcis-5958	59	7	xgboost	xgboost	PROPN
fcis-5958	59	8	method	method	PROPN
fcis-5958	59	9	3	3	NUM
fcis-5958	59	10	.	.	PUNCT
fcis-5958	59	11	data	datum	NOUN
fcis-5958	59	12	preparation	preparation	NOUN
fcis-5958	59	13	3.1	3.1	NUM
fcis-5958	59	14	.	.	PUNCT
fcis-5958	60	1	experimental	experimental	ADJ
fcis-5958	60	2	data	datum	NOUN
fcis-5958	60	3	the	the	DET
fcis-5958	60	4	telecom	telecom	NOUN
fcis-5958	60	5	industry	industry	NOUN
fcis-5958	60	6	customer	customer	NOUN
fcis-5958	60	7	data	datum	NOUN
fcis-5958	60	8	set	set	VERB
fcis-5958	60	9	used	use	VERB
fcis-5958	60	10	in	in	ADP
fcis-5958	60	11	this	this	DET
fcis-5958	60	12	paper	paper	NOUN
fcis-5958	60	13	has	have	VERB
fcis-5958	60	14	48,712	48,712	NUM
fcis-5958	60	15	pieces	piece	NOUN
fcis-5958	60	16	of	of	ADP
fcis-5958	60	17	data	datum	NOUN
fcis-5958	60	18	,	,	PUNCT
fcis-5958	60	19	including	include	VERB
fcis-5958	60	20	9876	9876	NUM
fcis-5958	60	21	pieces	piece	NOUN
fcis-5958	60	22	of	of	ADP
fcis-5958	60	23	lost	lose	VERB
fcis-5958	60	24	data	datum	NOUN
fcis-5958	60	25	and	and	CCONJ
fcis-5958	60	26	38,836	38,836	NUM
fcis-5958	60	27	pieces	piece	NOUN
fcis-5958	60	28	of	of	ADP
fcis-5958	60	29	non	non	ADJ
fcis-5958	60	30	-	-	ADJ
fcis-5958	60	31	lost	lost	ADJ
fcis-5958	60	32	data	datum	NOUN
fcis-5958	60	33	.	.	PUNCT
fcis-5958	61	1	the	the	DET
fcis-5958	61	2	amount	amount	NOUN
fcis-5958	61	3	of	of	ADP
fcis-5958	61	4	lost	lose	VERB
fcis-5958	61	5	data	data	NOUN
fcis-5958	61	6	is	be	AUX
fcis-5958	61	7	about	about	ADV
fcis-5958	61	8	20	20	NUM
fcis-5958	61	9	%	%	NOUN
fcis-5958	61	10	of	of	ADP
fcis-5958	61	11	the	the	DET
fcis-5958	61	12	total	total	ADJ
fcis-5958	61	13	data	datum	NOUN
fcis-5958	61	14	set	set	VERB
fcis-5958	61	15	.	.	PUNCT
fcis-5958	62	1	in	in	ADP
fcis-5958	62	2	the	the	DET
fcis-5958	62	3	experiment	experiment	NOUN
fcis-5958	62	4	,	,	PUNCT
fcis-5958	62	5	the	the	DET
fcis-5958	62	6	data	datum	NOUN
fcis-5958	62	7	set	set	VERB
fcis-5958	62	8	is	be	AUX
fcis-5958	62	9	randomly	randomly	ADV
fcis-5958	62	10	divided	divide	VERB
fcis-5958	62	11	into	into	ADP
fcis-5958	62	12	40	40	NUM
fcis-5958	62	13	%	%	NOUN
fcis-5958	62	14	test	test	NOUN
fcis-5958	62	15	set	set	VERB
fcis-5958	62	16	and	and	CCONJ
fcis-5958	62	17	60	60	NUM
fcis-5958	62	18	%	%	NOUN
fcis-5958	62	19	training	training	NOUN
fcis-5958	62	20	set	set	NOUN
fcis-5958	62	21	,	,	PUNCT
fcis-5958	62	22	which	which	PRON
fcis-5958	62	23	comes	come	VERB
fcis-5958	62	24	from	from	ADP
fcis-5958	62	25	kaggle	kaggle	NOUN
fcis-5958	62	26	website	website	NOUN
fcis-5958	62	27	.	.	PUNCT
fcis-5958	63	1	each	each	DET
fcis-5958	63	2	piece	piece	NOUN
fcis-5958	63	3	of	of	ADP
fcis-5958	63	4	data	datum	NOUN
fcis-5958	63	5	in	in	ADP
fcis-5958	63	6	the	the	DET
fcis-5958	63	7	data	data	NOUN
fcis-5958	63	8	set	set	VERB
fcis-5958	63	9	includes	include	VERB
fcis-5958	63	10	57	57	NUM
fcis-5958	63	11	characteristics	characteristic	NOUN
fcis-5958	63	12	and	and	CCONJ
fcis-5958	63	13	attributes	attribute	NOUN
fcis-5958	63	14	,	,	PUNCT
fcis-5958	63	15	such	such	ADJ
fcis-5958	63	16	as	as	ADP
fcis-5958	63	17	account	account	NOUN
fcis-5958	63	18	opening	opening	NOUN
fcis-5958	63	19	time	time	NOUN
fcis-5958	63	20	,	,	PUNCT
fcis-5958	63	21	remaining	remain	VERB
fcis-5958	63	22	accounts	account	NOUN
fcis-5958	63	23	,	,	PUNCT
fcis-5958	63	24	recharge	recharge	VERB
fcis-5958	63	25	period	period	NOUN
fcis-5958	63	26	,	,	PUNCT
fcis-5958	63	27	online	online	ADJ
fcis-5958	63	28	duration	duration	NOUN
fcis-5958	63	29	,	,	PUNCT
fcis-5958	63	30	and	and	CCONJ
fcis-5958	63	31	total	total	ADJ
fcis-5958	63	32	call	call	NOUN
fcis-5958	63	33	duration	duration	NOUN
fcis-5958	63	34	.	.	PUNCT
fcis-5958	64	1	some	some	DET
fcis-5958	64	2	attributes	attribute	NOUN
fcis-5958	64	3	are	be	AUX
fcis-5958	64	4	shown	show	VERB
fcis-5958	64	5	in	in	ADP
fcis-5958	64	6	table	table	NOUN
fcis-5958	64	7	1	1	NUM
fcis-5958	64	8	.	.	PUNCT
fcis-5958	64	9	table	table	NOUN
fcis-5958	64	10	1	1	NUM
fcis-5958	64	11	.	.	PUNCT
fcis-5958	64	12	part	part	NOUN
fcis-5958	64	13	properties	property	NOUN
fcis-5958	64	14	of	of	ADP
fcis-5958	64	15	dataset	dataset	ADJ
fcis-5958	64	16	data	data	NOUN
fcis-5958	64	17	classification	classification	NOUN
fcis-5958	64	18	data	datum	NOUN
fcis-5958	64	19	attributes	attribute	VERB
fcis-5958	64	20	basic	basic	ADJ
fcis-5958	64	21	information	information	NOUN
fcis-5958	64	22	customer	customer	NOUN
fcis-5958	64	23	i	i	PROPN
fcis-5958	64	24	d	d	PROPN
fcis-5958	64	25	,	,	PUNCT
fcis-5958	64	26	age	age	NOUN
fcis-5958	64	27	,	,	PUNCT
fcis-5958	64	28	package	package	NOUN
fcis-5958	64	29	,	,	PUNCT
fcis-5958	64	30	account	account	NOUN
fcis-5958	64	31	opening	opening	NOUN
fcis-5958	64	32	time	time	NOUN
fcis-5958	64	33	billing	billing	NOUN
fcis-5958	64	34	information	information	NOUN
fcis-5958	64	35	account	account	NOUN
fcis-5958	64	36	balance	balance	NOUN
fcis-5958	64	37	,	,	PUNCT
fcis-5958	64	38	recharge	recharge	VERB
fcis-5958	64	39	period	period	NOUN
fcis-5958	64	40	,	,	PUNCT
fcis-5958	64	41	credit	credit	NOUN
fcis-5958	64	42	limit	limit	NOUN
fcis-5958	64	43	,	,	PUNCT
fcis-5958	65	1	etc	etc	X
fcis-5958	65	2	network	network	NOUN
fcis-5958	65	3	duration	duration	NOUN
fcis-5958	65	4	time	time	NOUN
fcis-5958	65	5	spent	spend	VERB
fcis-5958	65	6	online	online	ADV
fcis-5958	65	7	,	,	PUNCT
fcis-5958	65	8	time	time	NOUN
fcis-5958	65	9	spent	spend	VERB
fcis-5958	65	10	online	online	ADV
fcis-5958	65	11	on	on	ADP
fcis-5958	65	12	weekdays	weekday	NOUN
fcis-5958	65	13	,	,	PUNCT
fcis-5958	65	14	time	time	NOUN
fcis-5958	65	15	spent	spend	VERB
fcis-5958	65	16	online	online	ADV
fcis-5958	65	17	on	on	ADP
fcis-5958	65	18	weekends	weekend	NOUN
fcis-5958	65	19	,	,	PUNCT
fcis-5958	65	20	etc	etc	X
fcis-5958	65	21	communication	communication	NOUN
fcis-5958	65	22	information	information	NOUN
fcis-5958	65	23	number	number	NOUN
fcis-5958	65	24	of	of	ADP
fcis-5958	65	25	calls	call	NOUN
fcis-5958	65	26	,	,	PUNCT
fcis-5958	65	27	total	total	ADJ
fcis-5958	65	28	call	call	NOUN
fcis-5958	65	29	duration	duration	NOUN
fcis-5958	65	30	,	,	PUNCT
fcis-5958	65	31	number	number	NOUN
fcis-5958	65	32	of	of	ADP
fcis-5958	65	33	calls	call	NOUN
fcis-5958	65	34	,	,	PUNCT
fcis-5958	65	35	number	number	NOUN
fcis-5958	65	36	of	of	ADP
fcis-5958	65	37	calls	call	NOUN
fcis-5958	65	38	,	,	PUNCT
fcis-5958	65	39	etc	etc	X
fcis-5958	65	40	network	network	NOUN
fcis-5958	65	41	usage	usage	NOUN
fcis-5958	65	42	information	information	NOUN
fcis-5958	65	43	uplink	uplink	VERB
fcis-5958	65	44	traffic	traffic	NOUN
fcis-5958	65	45	,	,	PUNCT
fcis-5958	65	46	downlink	downlink	NOUN
fcis-5958	65	47	traffic	traffic	NOUN
fcis-5958	65	48	,	,	PUNCT
fcis-5958	65	49	traffic	traffic	NOUN
fcis-5958	65	50	overflow	overflow	NOUN
fcis-5958	65	51	,	,	PUNCT
fcis-5958	65	52	etc	etc	X
fcis-5958	65	53	status	status	NOUN
fcis-5958	65	54	of	of	ADP
fcis-5958	65	55	complaints	complaint	NOUN
fcis-5958	65	56	the	the	DET
fcis-5958	65	57	number	number	NOUN
fcis-5958	65	58	of	of	ADP
fcis-5958	65	59	complaints	complaint	NOUN
fcis-5958	65	60	,	,	PUNCT
fcis-5958	65	61	the	the	DET
fcis-5958	65	62	response	response	NOUN
fcis-5958	65	63	time	time	NOUN
fcis-5958	65	64	of	of	ADP
fcis-5958	65	65	complaints	complaint	NOUN
fcis-5958	65	66	,	,	PUNCT
fcis-5958	65	67	etc	etc	X
fcis-5958	65	68	the	the	DET
fcis-5958	65	69	data	datum	NOUN
fcis-5958	65	70	is	be	AUX
fcis-5958	65	71	preprocessed	preprocesse	VERB
fcis-5958	65	72	,	,	PUNCT
fcis-5958	65	73	0	0	NUM
fcis-5958	65	74	is	be	AUX
fcis-5958	65	75	used	use	VERB
fcis-5958	65	76	to	to	PART
fcis-5958	65	77	fill	fill	VERB
fcis-5958	65	78	the	the	DET
fcis-5958	65	79	missing	miss	VERB
fcis-5958	65	80	values	value	NOUN
fcis-5958	65	81	in	in	ADP
fcis-5958	65	82	the	the	DET
fcis-5958	65	83	data	datum	NOUN
fcis-5958	65	84	attributes	attribute	VERB
fcis-5958	65	85	,	,	PUNCT
fcis-5958	65	86	unique	unique	ADJ
fcis-5958	65	87	thermal	thermal	ADJ
fcis-5958	65	88	coding	coding	NOUN
fcis-5958	65	89	is	be	AUX
fcis-5958	65	90	adopted	adopt	VERB
fcis-5958	65	91	for	for	ADP
fcis-5958	65	92	discrete	discrete	ADJ
fcis-5958	65	93	attributes	attribute	NOUN
fcis-5958	65	94	,	,	PUNCT
fcis-5958	65	95	and	and	CCONJ
fcis-5958	65	96	standardization	standardization	NOUN
fcis-5958	65	97	is	be	AUX
fcis-5958	65	98	carried	carry	VERB
fcis-5958	65	99	out	out	ADP
fcis-5958	65	100	for	for	ADP
fcis-5958	65	101	continuous	continuous	ADJ
fcis-5958	65	102	attributes	attribute	NOUN
fcis-5958	65	103	,	,	PUNCT
fcis-5958	65	104	so	so	SCONJ
fcis-5958	65	105	that	that	SCONJ
fcis-5958	65	106	the	the	DET
fcis-5958	65	107	processed	process	VERB
fcis-5958	65	108	data	datum	NOUN
fcis-5958	65	109	can	can	AUX
fcis-5958	65	110	meet	meet	VERB
fcis-5958	65	111	the	the	DET
fcis-5958	65	112	standard	standard	ADJ
fcis-5958	65	113	normal	normal	ADJ
fcis-5958	65	114	distribution	distribution	NOUN
fcis-5958	65	115	.	.	PUNCT
fcis-5958	66	1	3.2	3.2	NUM
fcis-5958	66	2	.	.	PUNCT
fcis-5958	67	1	feature	feature	NOUN
fcis-5958	67	2	selection	selection	NOUN
fcis-5958	67	3	mipca	mipca	PROPN
fcis-5958	67	4	method	method	PROPN
fcis-5958	67	5	was	be	AUX
fcis-5958	67	6	used	use	VERB
fcis-5958	67	7	to	to	PART
fcis-5958	67	8	select	select	VERB
fcis-5958	67	9	the	the	DET
fcis-5958	67	10	feature	feature	NOUN
fcis-5958	67	11	of	of	ADP
fcis-5958	67	12	the	the	DET
fcis-5958	67	13	data	datum	NOUN
fcis-5958	67	14	set	set	VERB
fcis-5958	67	15	,	,	PUNCT
fcis-5958	67	16	and	and	CCONJ
fcis-5958	67	17	the	the	DET
fcis-5958	67	18	feature	feature	NOUN
fcis-5958	67	19	selection	selection	NOUN
fcis-5958	67	20	results	result	NOUN
fcis-5958	67	21	of	of	ADP
fcis-5958	67	22	the	the	DET
fcis-5958	67	23	data	datum	NOUN
fcis-5958	67	24	set	set	VERB
fcis-5958	67	25	were	be	AUX
fcis-5958	67	26	compared	compare	VERB
fcis-5958	67	27	with	with	ADP
fcis-5958	67	28	the	the	DET
fcis-5958	67	29	principal	principal	ADJ
fcis-5958	67	30	component	component	NOUN
fcis-5958	67	31	analysis	analysis	NOUN
fcis-5958	67	32	method	method	NOUN
fcis-5958	67	33	.	.	PUNCT
fcis-5958	68	1	the	the	DET
fcis-5958	68	2	comparison	comparison	NOUN
fcis-5958	68	3	results	result	NOUN
fcis-5958	68	4	were	be	AUX
fcis-5958	68	5	expressed	express	VERB
fcis-5958	68	6	as	as	ADP
fcis-5958	68	7	the	the	DET
fcis-5958	68	8	principal	principal	ADJ
fcis-5958	68	9	component	component	NOUN
fcis-5958	68	10	characteristic	characteristic	ADJ
fcis-5958	68	11	value	value	NOUN
fcis-5958	68	12	μ	μ	NOUN
fcis-5958	68	13	and	and	CCONJ
fcis-5958	68	14	the	the	DET
fcis-5958	68	15	cumulative	cumulative	ADJ
fcis-5958	68	16	contribution	contribution	NOUN
fcis-5958	68	17	rate	rate	NOUN
fcis-5958	68	18	β	β	PROPN
fcis-5958	68	19	,	,	PUNCT
fcis-5958	68	20	as	as	SCONJ
fcis-5958	68	21	shown	show	VERB
fcis-5958	68	22	in	in	ADP
fcis-5958	68	23	table	table	NOUN
fcis-5958	68	24	2	2	NUM
fcis-5958	68	25	.	.	PUNCT
fcis-5958	68	26	table	table	NOUN
fcis-5958	68	27	2	2	NUM
fcis-5958	68	28	.	.	PUNCT
fcis-5958	68	29	principal	principal	ADJ
fcis-5958	68	30	component	component	NOUN
fcis-5958	68	31	eigenvalue	eigenvalue	PROPN
fcis-5958	68	32	and	and	CCONJ
fcis-5958	68	33	cumulative	cumulative	ADJ
fcis-5958	68	34	contribution	contribution	NOUN
fcis-5958	68	35	rate	rate	NOUN
fcis-5958	68	36	principal	principal	ADJ
fcis-5958	68	37	component	component	NOUN
fcis-5958	68	38	pca	pca	PROPN
fcis-5958	68	39	mipca	mipca	ADV
fcis-5958	68	40	𝜇	𝜇	ADP
fcis-5958	68	41	𝛽	𝛽	PROPN
fcis-5958	68	42	𝜇	𝜇	ADP
fcis-5958	68	43	𝛽	𝛽	NOUN
fcis-5958	68	44	pc1	pc1	PROPN
fcis-5958	68	45	16.34	16.34	NUM
fcis-5958	68	46	30.07	30.07	NUM
fcis-5958	68	47	%	%	NOUN
fcis-5958	68	48	22.23	22.23	NUM
fcis-5958	68	49	43.30	43.30	NUM
fcis-5958	68	50	%	%	NOUN
fcis-5958	68	51	pc2	pc2	NOUN
fcis-5958	68	52	8.56	8.56	NUM
fcis-5958	68	53	46.87	46.87	NUM
fcis-5958	68	54	%	%	NOUN
fcis-5958	68	55	8.34	8.34	NUM
fcis-5958	68	56	56.67	56.67	NUM
fcis-5958	68	57	%	%	NOUN
fcis-5958	68	58	pc3	pc3	PROPN
fcis-5958	69	1	5.23	5.23	NUM
fcis-5958	69	2	57.13	57.13	NUM
fcis-5958	69	3	%	%	NOUN
fcis-5958	69	4	4.27	4.27	NUM
fcis-5958	69	5	64.71	64.71	NUM
fcis-5958	69	6	%	%	NOUN
fcis-5958	69	7	pc4	pc4	NOUN
fcis-5958	69	8	4.12	4.12	NUM
fcis-5958	69	9	65.22	65.22	NUM
fcis-5958	69	10	%	%	NOUN
fcis-5958	69	11	3.68	3.68	NUM
fcis-5958	69	12	70.35	70.35	NUM
fcis-5958	69	13	%	%	NOUN
fcis-5958	69	14	pc5	pc5	NOUN
fcis-5958	69	15	2.85	2.85	NUM
fcis-5958	69	16	69.81	69.81	NUM
fcis-5958	69	17	%	%	NOUN
fcis-5958	69	18	2.64	2.64	NUM
fcis-5958	69	19	76.75	76.75	NUM
fcis-5958	69	20	%	%	NOUN
fcis-5958	69	21	pc6	pc6	NOUN
fcis-5958	69	22	2.21	2.21	NUM
fcis-5958	69	23	73.15	73.15	NUM
fcis-5958	69	24	%	%	NOUN
fcis-5958	69	25	1.93	1.93	NUM
fcis-5958	69	26	80.65	80.65	NUM
fcis-5958	69	27	%	%	NOUN
fcis-5958	69	28	pc7	pc7	ADV
fcis-5958	69	29	1.80	1.80	NUM
fcis-5958	69	30	76.68	76.68	NUM
fcis-5958	69	31	%	%	NOUN
fcis-5958	69	32	1.43	1.43	NUM
fcis-5958	69	33	83.77	83.77	NUM
fcis-5958	69	34	%	%	NOUN
fcis-5958	69	35	pc8	pc8	NOUN
fcis-5958	69	36	1.48	1.48	NUM
fcis-5958	69	37	79.59	79.59	NUM
fcis-5958	69	38	%	%	NOUN
fcis-5958	69	39	1.04	1.04	NUM
fcis-5958	69	40	86.34	86.34	NUM
fcis-5958	69	41	%	%	NOUN
fcis-5958	69	42	pc9	pc9	NOUN
fcis-5958	69	43	1.23	1.23	NUM
fcis-5958	69	44	82.64	82.64	NUM
fcis-5958	69	45	%	%	NOUN
fcis-5958	69	46	0.84	0.84	NUM
fcis-5958	69	47	88.76	88.76	NUM
fcis-5958	69	48	%	%	NOUN
fcis-5958	69	49	pc10	pc10	PROPN
fcis-5958	69	50	1.01	1.01	NUM
fcis-5958	69	51	84.76	84.76	NUM
fcis-5958	69	52	%	%	NOUN
fcis-5958	69	53	0.65	0.65	NUM
fcis-5958	69	54	89.93	89.93	NUM
fcis-5958	69	55	%	%	NOUN
fcis-5958	70	1	pc11	pc11	PROPN
fcis-5958	70	2	0.88	0.88	NUM
fcis-5958	70	3	85.60	85.60	NUM
fcis-5958	70	4	%	%	NOUN
fcis-5958	70	5	0.63	0.63	NUM
fcis-5958	70	6	91.14	91.14	NUM
fcis-5958	70	7	%	%	NOUN
fcis-5958	70	8	pc12	pc12	PROPN
fcis-5958	70	9	0.76	0.76	NUM
fcis-5958	70	10	85.72	85.72	NUM
fcis-5958	70	11	%	%	NOUN
fcis-5958	70	12	0.57	0.57	NUM
fcis-5958	70	13	92.22	92.22	NUM
fcis-5958	70	14	%	%	NOUN
fcis-5958	70	15	pc13	pc13	PROPN
fcis-5958	70	16	0.64	0.64	NUM
fcis-5958	70	17	86.43	86.43	NUM
fcis-5958	70	18	%	%	NOUN
fcis-5958	70	19	0.54	0.54	NUM
fcis-5958	70	20	93.27	93.27	NUM
fcis-5958	70	21	%	%	NOUN
fcis-5958	70	22	pc14	pc14	PROPN
fcis-5958	70	23	0.64	0.64	NUM
fcis-5958	70	24	87.14	87.14	NUM
fcis-5958	70	25	%	%	NOUN
fcis-5958	70	26	0.49	0.49	NUM
fcis-5958	70	27	94.12	94.12	NUM
fcis-5958	70	28	%	%	NOUN
fcis-5958	70	29	pc15	pc15	PROPN
fcis-5958	70	30	0.62	0.62	NUM
fcis-5958	70	31	87.80	87.80	NUM
fcis-5958	70	32	%	%	NOUN
fcis-5958	70	33	0.44	0.44	NUM
fcis-5958	70	34	95.03	95.03	NUM
fcis-5958	70	35	%	%	NOUN
fcis-5958	70	36	pc16	pc16	PROPN
fcis-5958	70	37	0.58	0.58	NUM
fcis-5958	70	38	88.47	88.47	NUM
fcis-5958	70	39	%	%	NOUN
fcis-5958	70	40	0.37	0.37	NUM
fcis-5958	70	41	95.87	95.87	NUM
fcis-5958	70	42	%	%	NOUN
fcis-5958	70	43	pc17	pc17	PROPN
fcis-5958	70	44	0.56	0.56	NUM
fcis-5958	70	45	89.22	89.22	NUM
fcis-5958	70	46	%	%	NOUN
fcis-5958	70	47	0.34	0.34	NUM
fcis-5958	70	48	96.21	96.21	NUM
fcis-5958	70	49	%	%	NOUN
fcis-5958	70	50	pc18	pc18	PROPN
fcis-5958	70	51	0.55	0.55	NUM
fcis-5958	70	52	89.75	89.75	NUM
fcis-5958	70	53	%	%	NOUN
fcis-5958	70	54	0.32	0.32	NUM
fcis-5958	70	55	96.54	96.54	NUM
fcis-5958	70	56	%	%	NOUN
fcis-5958	70	57	pc19	pc19	PROPN
fcis-5958	70	58	0.55	0.55	NUM
fcis-5958	70	59	90.28	90.28	NUM
fcis-5958	70	60	%	%	NOUN
fcis-5958	70	61	0.29	0.29	NUM
fcis-5958	70	62	96.84	96.84	NUM
fcis-5958	70	63	%	%	NOUN
fcis-5958	70	64	from	from	ADP
fcis-5958	70	65	the	the	DET
fcis-5958	70	66	extraction	extraction	NOUN
fcis-5958	70	67	results	result	NOUN
fcis-5958	70	68	of	of	ADP
fcis-5958	70	69	principal	principal	ADJ
fcis-5958	70	70	components	component	NOUN
fcis-5958	70	71	in	in	ADP
fcis-5958	70	72	table	table	NOUN
fcis-5958	70	73	2	2	NUM
fcis-5958	70	74	,	,	PUNCT
fcis-5958	70	75	it	it	PRON
fcis-5958	70	76	can	can	AUX
fcis-5958	70	77	be	be	AUX
fcis-5958	70	78	seen	see	VERB
fcis-5958	70	79	that	that	SCONJ
fcis-5958	70	80	mipca	mipca	ADV
fcis-5958	70	81	has	have	VERB
fcis-5958	70	82	a	a	DET
fcis-5958	70	83	higher	high	ADJ
fcis-5958	70	84	cumulative	cumulative	ADJ
fcis-5958	70	85	contribution	contribution	NOUN
fcis-5958	70	86	rate	rate	NOUN
fcis-5958	70	87	when	when	SCONJ
fcis-5958	70	88	principal	principal	ADJ
fcis-5958	70	89	components	component	NOUN
fcis-5958	70	90	of	of	ADP
fcis-5958	70	91	the	the	DET
fcis-5958	70	92	same	same	ADJ
fcis-5958	70	93	dimension	dimension	NOUN
fcis-5958	70	94	are	be	AUX
fcis-5958	70	95	selected	select	VERB
fcis-5958	70	96	.	.	PUNCT
fcis-5958	71	1	for	for	ADP
fcis-5958	71	2	example	example	NOUN
fcis-5958	71	3	,	,	PUNCT
fcis-5958	71	4	to	to	PART
fcis-5958	71	5	achieve	achieve	VERB
fcis-5958	71	6	a	a	DET
fcis-5958	71	7	cumulative	cumulative	ADJ
fcis-5958	71	8	contribution	contribution	NOUN
fcis-5958	71	9	rate	rate	NOUN
fcis-5958	71	10	of	of	ADP
fcis-5958	71	11	90	90	NUM
fcis-5958	71	12	%	%	NOUN
fcis-5958	71	13	,	,	PUNCT
fcis-5958	71	14	pca	pca	NOUN
fcis-5958	71	15	method	method	NOUN
fcis-5958	71	16	needs	need	VERB
fcis-5958	71	17	to	to	PART
fcis-5958	71	18	select	select	VERB
fcis-5958	71	19	19dimensional	19dimensional	ADJ
fcis-5958	71	20	principal	principal	ADJ
fcis-5958	71	21	components	component	NOUN
fcis-5958	71	22	,	,	PUNCT
fcis-5958	71	23	while	while	SCONJ
fcis-5958	71	24	mipca	mipca	ADV
fcis-5958	71	25	method	method	NOUN
fcis-5958	71	26	reduces	reduce	VERB
fcis-5958	71	27	the	the	DET
fcis-5958	71	28	value	value	NOUN
fcis-5958	71	29	to	to	ADP
fcis-5958	71	30	11	11	NUM
fcis-5958	71	31	-	-	PUNCT
fcis-5958	71	32	dimensional	dimensional	ADJ
fcis-5958	71	33	,	,	PUNCT
fcis-5958	71	34	which	which	PRON
fcis-5958	71	35	indicates	indicate	VERB
fcis-5958	71	36	that	that	SCONJ
fcis-5958	71	37	mipca	mipca	ADJ
fcis-5958	71	38	method	method	NOUN
fcis-5958	71	39	can	can	AUX
fcis-5958	71	40	make	make	VERB
fcis-5958	71	41	full	full	ADJ
fcis-5958	71	42	use	use	NOUN
fcis-5958	71	43	of	of	ADP
fcis-5958	71	44	nonlinear	nonlinear	ADJ
fcis-5958	71	45	information	information	NOUN
fcis-5958	71	46	.	.	PUNCT
fcis-5958	72	1	thus	thus	ADV
fcis-5958	72	2	,	,	PUNCT
fcis-5958	72	3	the	the	DET
fcis-5958	72	4	original	original	ADJ
fcis-5958	72	5	information	information	NOUN
fcis-5958	72	6	can	can	AUX
fcis-5958	72	7	be	be	AUX
fcis-5958	72	8	retained	retain	VERB
fcis-5958	72	9	to	to	ADP
fcis-5958	72	10	a	a	DET
fcis-5958	72	11	greater	great	ADJ
fcis-5958	72	12	extent	extent	NOUN
fcis-5958	72	13	,	,	PUNCT
fcis-5958	72	14	which	which	PRON
fcis-5958	72	15	is	be	AUX
fcis-5958	72	16	helpful	helpful	ADJ
fcis-5958	72	17	to	to	PART
fcis-5958	72	18	improve	improve	VERB
fcis-5958	72	19	the	the	DET
fcis-5958	72	20	accuracy	accuracy	NOUN
fcis-5958	72	21	of	of	ADP
fcis-5958	72	22	subsequent	subsequent	ADJ
fcis-5958	72	23	prediction	prediction	NOUN
fcis-5958	72	24	.	.	PUNCT
fcis-5958	73	1	in	in	ADP
fcis-5958	73	2	this	this	DET
fcis-5958	73	3	paper	paper	NOUN
fcis-5958	73	4	,	,	PUNCT
fcis-5958	73	5	when	when	SCONJ
fcis-5958	73	6	the	the	DET
fcis-5958	73	7	cumulative	cumulative	ADJ
fcis-5958	73	8	contribution	contribution	NOUN
fcis-5958	73	9	rate	rate	NOUN
fcis-5958	73	10	reaches	reach	VERB
fcis-5958	73	11	90	90	NUM
fcis-5958	73	12	%	%	NOUN
fcis-5958	73	13	,	,	PUNCT
fcis-5958	73	14	the	the	DET
fcis-5958	73	15	first	first	ADJ
fcis-5958	73	16	11	11	NUM
fcis-5958	73	17	dimensional	dimensional	ADJ
fcis-5958	73	18	principal	principal	ADJ
fcis-5958	73	19	components	component	NOUN
fcis-5958	73	20	screened	screen	VERB
fcis-5958	73	21	by	by	ADP
fcis-5958	73	22	mipca	mipca	ADJ
fcis-5958	73	23	method	method	PROPN
fcis-5958	73	24	are	be	AUX
fcis-5958	73	25	selected	select	VERB
fcis-5958	73	26	for	for	ADP
fcis-5958	73	27	xgboost	xgboost	X
fcis-5958	73	28	customer	customer	NOUN
fcis-5958	73	29	loss	loss	NOUN
fcis-5958	73	30	prediction	prediction	NOUN
fcis-5958	73	31	.	.	PUNCT
fcis-5958	74	1	4	4	X
fcis-5958	74	2	.	.	X
fcis-5958	74	3	customer	customer	NOUN
fcis-5958	74	4	churn	churn	NOUN
fcis-5958	74	5	forecast	forecast	NOUN
fcis-5958	74	6	4.1	4.1	NUM
fcis-5958	74	7	.	.	PUNCT
fcis-5958	74	8	evaluation	evaluation	NOUN
fcis-5958	74	9	index	index	NOUN
fcis-5958	74	10	according	accord	VERB
fcis-5958	74	11	to	to	ADP
fcis-5958	74	12	the	the	DET
fcis-5958	74	13	confusion	confusion	NOUN
fcis-5958	74	14	matrix	matrix	NOUN
fcis-5958	74	15	,	,	PUNCT
fcis-5958	74	16	the	the	DET
fcis-5958	74	17	prediction	prediction	NOUN
fcis-5958	74	18	accuracy	accuracy	NOUN
fcis-5958	74	19	𝐸𝑎𝑐𝑐	𝐸𝑎𝑐𝑐	PROPN
fcis-5958	74	20	,	,	PUNCT
fcis-5958	74	21	the	the	DET
fcis-5958	74	22	recall	recall	NOUN
fcis-5958	74	23	rate	rate	NOUN
fcis-5958	74	24	𝐸𝑟𝑒𝑐𝑎𝑙𝑙	𝐸𝑟𝑒𝑐𝑎𝑙𝑙	PROPN
fcis-5958	74	25	,	,	PUNCT
fcis-5958	74	26	and	and	CCONJ
fcis-5958	74	27	the	the	DET
fcis-5958	74	28	𝐹_𝑆𝑐𝑜𝑟𝑒	𝐹_𝑆𝑐𝑜𝑟𝑒	NOUN
fcis-5958	74	29	were	be	AUX
fcis-5958	74	30	calculated	calculate	VERB
fcis-5958	74	31	as	as	ADP
fcis-5958	74	32	the	the	DET
fcis-5958	74	33	evaluation	evaluation	NOUN
fcis-5958	74	34	index	index	NOUN
fcis-5958	74	35	of	of	ADP
fcis-5958	74	36	the	the	DET
fcis-5958	74	37	algorithm	algorithm	NOUN
fcis-5958	74	38	.	.	PUNCT
fcis-5958	75	1	the	the	DET
fcis-5958	75	2	calculation	calculation	NOUN
fcis-5958	75	3	formula	formula	NOUN
fcis-5958	75	4	of	of	ADP
fcis-5958	75	5	each	each	DET
fcis-5958	75	6	evaluation	evaluation	NOUN
fcis-5958	75	7	index	index	NOUN
fcis-5958	75	8	is	be	AUX
fcis-5958	75	9	shown	show	VERB
fcis-5958	75	10	as	as	SCONJ
fcis-5958	75	11	follows	follow	VERB
fcis-5958	75	12	,	,	PUNCT
fcis-5958	75	13	and	and	CCONJ
fcis-5958	75	14	the	the	DET
fcis-5958	75	15	classification	classification	NOUN
fcis-5958	75	16	of	of	ADP
fcis-5958	75	17	customer	customer	NOUN
fcis-5958	75	18	state	state	NOUN
fcis-5958	75	19	prediction	prediction	NOUN
fcis-5958	75	20	results	result	NOUN
fcis-5958	75	21	[	[	X
fcis-5958	75	22	12	12	NUM
fcis-5958	75	23	]	]	PUNCT
fcis-5958	75	24	is	be	AUX
fcis-5958	75	25	shown	show	VERB
fcis-5958	75	26	in	in	ADP
fcis-5958	75	27	table	table	NOUN
fcis-5958	75	28	3	3	NUM
fcis-5958	75	29	.	.	PUNCT
fcis-5958	75	30	table	table	NOUN
fcis-5958	75	31	3	3	NUM
fcis-5958	75	32	.	.	X
fcis-5958	75	33	customer	customer	NOUN
fcis-5958	75	34	status	status	NOUN
fcis-5958	75	35	classification	classification	NOUN
fcis-5958	75	36	true	true	ADJ
fcis-5958	75	37	customer	customer	NOUN
fcis-5958	75	38	status	status	NOUN
fcis-5958	75	39	loss	loss	NOUN
fcis-5958	75	40	of	of	ADP
fcis-5958	75	41	prediction	prediction	NOUN
fcis-5958	75	42	results	result	VERB
fcis-5958	75	43	the	the	DET
fcis-5958	75	44	prediction	prediction	NOUN
fcis-5958	75	45	result	result	NOUN
fcis-5958	75	46	is	be	AUX
fcis-5958	75	47	not	not	PART
fcis-5958	75	48	churn	churn	VERB
fcis-5958	75	49	actual	actual	ADJ
fcis-5958	75	50	loss	loss	NOUN
fcis-5958	75	51	tp	tp	X
fcis-5958	75	52	fn	fn	ADJ
fcis-5958	75	53	actual	actual	ADJ
fcis-5958	75	54	nonchurn	nonchurn	NOUN
fcis-5958	75	55	fp	fp	PROPN
fcis-5958	75	56	tn	tn	PROPN
fcis-5958	75	57	𝐸𝑎𝑐𝑐	𝐸𝑎𝑐𝑐	PROPN
fcis-5958	75	58	=	=	X
fcis-5958	75	59	𝑇𝑃+𝑇𝑁	𝑇𝑃+𝑇𝑁	X
fcis-5958	75	60	𝑇𝑃+𝑇𝑁+𝐹𝑃+𝐹𝑁	𝑇𝑃+𝑇𝑁+𝐹𝑃+𝐹𝑁	PUNCT
fcis-5958	75	61	(	(	PUNCT
fcis-5958	75	62	20	20	NUM
fcis-5958	75	63	)	)	PUNCT
fcis-5958	75	64	𝐸𝑟𝑒𝑐𝑎𝑙𝑙	𝐸𝑟𝑒𝑐𝑎𝑙𝑙	PROPN
fcis-5958	75	65	=	=	SYM
fcis-5958	75	66	𝑇𝑃	𝑇𝑃	NOUN
fcis-5958	75	67	𝑇𝑃+𝐹𝑁	𝑇𝑃+𝐹𝑁	NOUN
fcis-5958	75	68	(	(	PUNCT
fcis-5958	75	69	21	21	NUM
fcis-5958	75	70	)	)	PUNCT
fcis-5958	75	71	4	4	NUM
fcis-5958	75	72	𝐹_𝑆𝑐𝑜𝑟𝑒	𝐹_𝑆𝑐𝑜𝑟𝑒	NOUN
fcis-5958	75	73	=	=	SYM
fcis-5958	75	74	2×𝑇𝑃	2×𝑇𝑃	PROPN
fcis-5958	75	75	2×𝑇𝑃+𝐹𝑃+𝐹𝑁	2×𝑇𝑃+𝐹𝑃+𝐹𝑁	NUM
fcis-5958	75	76	(	(	PUNCT
fcis-5958	75	77	22	22	NUM
fcis-5958	75	78	)	)	PUNCT
fcis-5958	75	79	4.2	4.2	NUM
fcis-5958	75	80	.	.	PUNCT
fcis-5958	76	1	important	important	ADJ
fcis-5958	76	2	parameter	parameter	NOUN
fcis-5958	76	3	settings	setting	NOUN
fcis-5958	76	4	of	of	ADP
fcis-5958	76	5	xgboost	xgboost	ADJ
fcis-5958	76	6	algorithm	algorithm	PROPN
fcis-5958	76	7	the	the	DET
fcis-5958	76	8	experiment	experiment	NOUN
fcis-5958	76	9	running	run	VERB
fcis-5958	76	10	environment	environment	NOUN
fcis-5958	76	11	in	in	ADP
fcis-5958	76	12	this	this	DET
fcis-5958	76	13	paper	paper	NOUN
fcis-5958	76	14	is	be	AUX
fcis-5958	76	15	a	a	DET
fcis-5958	76	16	64bit	64bit	NOUN
fcis-5958	76	17	windows10	windows10	NOUN
fcis-5958	76	18	operating	operating	NOUN
fcis-5958	76	19	system	system	NOUN
fcis-5958	76	20	,	,	PUNCT
fcis-5958	76	21	and	and	CCONJ
fcis-5958	76	22	the	the	DET
fcis-5958	76	23	specific	specific	ADJ
fcis-5958	76	24	hardware	hardware	NOUN
fcis-5958	76	25	settings	setting	NOUN
fcis-5958	76	26	are	be	AUX
fcis-5958	76	27	as	as	SCONJ
fcis-5958	76	28	follows	follow	VERB
fcis-5958	76	29	:	:	PUNCT
fcis-5958	76	30	16	16	NUM
fcis-5958	76	31	gb	gb	NOUN
fcis-5958	76	32	memory	memory	NOUN
fcis-5958	76	33	and	and	CCONJ
fcis-5958	76	34	12th	12th	ADJ
fcis-5958	76	35	gen	gen	PROPN
fcis-5958	76	36	intel(r	intel(r	PROPN
fcis-5958	76	37	)	)	PUNCT
fcis-5958	76	38	core(tm	core(tm	NOUN
fcis-5958	76	39	)	)	PUNCT
fcis-5958	76	40	i5	i5	NOUN
fcis-5958	76	41	-	-	PUNCT
fcis-5958	76	42	12490f	12490f	NUM
fcis-5958	76	43	@	@	ADP
fcis-5958	76	44	3.00	3.00	NUM
fcis-5958	76	45	ghz	ghz	NOUN
fcis-5958	76	46	cpu	cpu	NOUN
fcis-5958	76	47	.	.	PUNCT
fcis-5958	77	1	python3	python3	NOUN
fcis-5958	77	2	and	and	CCONJ
fcis-5958	77	3	related	related	ADJ
fcis-5958	77	4	toolkits	toolkit	NOUN
fcis-5958	77	5	were	be	AUX
fcis-5958	77	6	used	use	VERB
fcis-5958	77	7	in	in	ADP
fcis-5958	77	8	the	the	DET
fcis-5958	77	9	experiments	experiment	NOUN
fcis-5958	77	10	.	.	PUNCT
fcis-5958	78	1	after	after	ADP
fcis-5958	78	2	many	many	ADJ
fcis-5958	78	3	experiments	experiment	NOUN
fcis-5958	78	4	,	,	PUNCT
fcis-5958	78	5	the	the	DET
fcis-5958	78	6	parameters	parameter	NOUN
fcis-5958	78	7	of	of	ADP
fcis-5958	78	8	xgboost	xgboost	PROPN
fcis-5958	78	9	are	be	AUX
fcis-5958	78	10	finally	finally	ADV
fcis-5958	78	11	set	set	VERB
fcis-5958	78	12	in	in	ADP
fcis-5958	78	13	table	table	NOUN
fcis-5958	78	14	iv	iv	NUM
fcis-5958	78	15	.	.	PUNCT
fcis-5958	78	16	table	table	NOUN
fcis-5958	78	17	4	4	NUM
fcis-5958	78	18	.	.	PUNCT
fcis-5958	79	1	the	the	DET
fcis-5958	79	2	important	important	ADJ
fcis-5958	79	3	parameters	parameter	NOUN
fcis-5958	79	4	of	of	ADP
fcis-5958	79	5	xgboost	xgboost	PROPN
fcis-5958	79	6	parameter	parameter	PROPN
fcis-5958	79	7	parameter	parameter	PROPN
fcis-5958	79	8	configuration	configuration	PROPN
fcis-5958	79	9	max	max	NOUN
fcis-5958	79	10	-	-	PUNCT
fcis-5958	79	11	depth	depth	ADJ
fcis-5958	79	12	5	5	NUM
fcis-5958	79	13	min	min	NOUN
fcis-5958	79	14	-	-	ADJ
fcis-5958	79	15	child	child	NOUN
fcis-5958	79	16	-	-	PUNCT
fcis-5958	79	17	weight	weight	NOUN
fcis-5958	79	18	3	3	NUM
fcis-5958	79	19	learning	learning	NOUN
fcis-5958	79	20	-	-	PUNCT
fcis-5958	79	21	rate	rate	NOUN
fcis-5958	79	22	0.3	0.3	NUM
fcis-5958	79	23	gamma	gamma	NOUN
fcis-5958	79	24	0.1	0.1	NUM
fcis-5958	79	25	subsample	subsample	NOUN
fcis-5958	79	26	0.76	0.76	NUM
fcis-5958	79	27	n	n	CCONJ
fcis-5958	79	28	-	-	PUNCT
fcis-5958	79	29	estimators	estimator	NOUN
fcis-5958	79	30	200	200	NUM
fcis-5958	79	31	4.3	4.3	NUM
fcis-5958	79	32	.	.	PUNCT
fcis-5958	80	1	analysis	analysis	NOUN
fcis-5958	80	2	of	of	ADP
fcis-5958	80	3	prediction	prediction	NOUN
fcis-5958	80	4	results	result	NOUN
fcis-5958	80	5	in	in	ADP
fcis-5958	80	6	this	this	DET
fcis-5958	80	7	paper	paper	NOUN
fcis-5958	80	8	,	,	PUNCT
fcis-5958	80	9	mipca	mipca	PROPN
fcis-5958	80	10	-	-	PUNCT
fcis-5958	80	11	xgboost	xgboost	PROPN
fcis-5958	80	12	algorithm	algorithm	PROPN
fcis-5958	80	13	is	be	AUX
fcis-5958	80	14	applied	apply	VERB
fcis-5958	80	15	to	to	ADP
fcis-5958	80	16	customer	customer	NOUN
fcis-5958	80	17	churn	churn	NOUN
fcis-5958	80	18	prediction	prediction	NOUN
fcis-5958	80	19	in	in	ADP
fcis-5958	80	20	the	the	DET
fcis-5958	80	21	telecom	telecom	NOUN
fcis-5958	80	22	industry	industry	NOUN
fcis-5958	80	23	,	,	PUNCT
fcis-5958	80	24	and	and	CCONJ
fcis-5958	80	25	random	random	ADJ
fcis-5958	80	26	forest	forest	NOUN
fcis-5958	80	27	and	and	CCONJ
fcis-5958	80	28	logistic	logistic	ADJ
fcis-5958	80	29	regression	regression	NOUN
fcis-5958	80	30	algorithms	algorithm	NOUN
fcis-5958	80	31	commonly	commonly	ADV
fcis-5958	80	32	used	use	VERB
fcis-5958	80	33	in	in	ADP
fcis-5958	80	34	this	this	DET
fcis-5958	80	35	field	field	NOUN
fcis-5958	80	36	are	be	AUX
fcis-5958	80	37	used	use	VERB
fcis-5958	80	38	for	for	ADP
fcis-5958	80	39	comparison	comparison	NOUN
fcis-5958	80	40	experiments	experiment	NOUN
fcis-5958	80	41	.	.	PUNCT
fcis-5958	81	1	the	the	DET
fcis-5958	81	2	comparison	comparison	NOUN
fcis-5958	81	3	results	result	NOUN
fcis-5958	81	4	of	of	ADP
fcis-5958	81	5	precision	precision	NOUN
fcis-5958	81	6	,	,	PUNCT
fcis-5958	81	7	recall	recall	NOUN
fcis-5958	81	8	and	and	CCONJ
fcis-5958	81	9	𝐹_𝑆𝑐𝑜𝑟𝑒	𝐹_𝑆𝑐𝑜𝑟𝑒	NOUN
fcis-5958	81	10	of	of	ADP
fcis-5958	81	11	related	related	ADJ
fcis-5958	81	12	algorithms	algorithm	NOUN
fcis-5958	81	13	are	be	AUX
fcis-5958	81	14	shown	show	VERB
fcis-5958	81	15	in	in	ADP
fcis-5958	81	16	table	table	NOUN
fcis-5958	81	17	5	5	NUM
fcis-5958	81	18	.	.	PUNCT
fcis-5958	81	19	table	table	NOUN
fcis-5958	81	20	5	5	NUM
fcis-5958	81	21	.	.	PUNCT
fcis-5958	81	22	comparison	comparison	NOUN
fcis-5958	81	23	of	of	ADP
fcis-5958	81	24	prediction	prediction	NOUN
fcis-5958	81	25	results	result	NOUN
fcis-5958	81	26	of	of	ADP
fcis-5958	81	27	various	various	ADJ
fcis-5958	81	28	algorithms(%	algorithms(%	NOUN
fcis-5958	81	29	)	)	PUNCT
fcis-5958	81	30	type	type	NOUN
fcis-5958	81	31	of	of	ADP
fcis-5958	81	32	algorithm	algorithm	NOUN
fcis-5958	81	33	𝐸𝑎𝑐𝑐	𝐸𝑎𝑐𝑐	PROPN
fcis-5958	81	34	𝐸𝑟𝑒𝑐𝑎𝑙𝑙	𝐸𝑟𝑒𝑐𝑎𝑙𝑙	PROPN
fcis-5958	81	35	𝐹_𝑆𝑐𝑜𝑟𝑒	𝐹_𝑆𝑐𝑜𝑟𝑒	PUNCT
fcis-5958	81	36	rf	rf	VERB
fcis-5958	81	37	84.12	84.12	NUM
fcis-5958	81	38	84.36	84.36	NUM
fcis-5958	81	39	83.25	83.25	NUM
fcis-5958	81	40	lr	lr	X
fcis-5958	81	41	81.43	81.43	NUM
fcis-5958	81	42	83.03	83.03	NUM
fcis-5958	81	43	80.13	80.13	NUM
fcis-5958	81	44	xgboost	xgboost	ADP
fcis-5958	81	45	86.37	86.37	NUM
fcis-5958	81	46	84.58	84.58	NUM
fcis-5958	81	47	84.73	84.73	NUM
fcis-5958	81	48	mipca	mipca	ADV
fcis-5958	81	49	-	-	PUNCT
fcis-5958	81	50	xgboost	xgboost	PROPN
fcis-5958	81	51	90.56	90.56	NUM
fcis-5958	81	52	90.23	90.23	NUM
fcis-5958	81	53	89.64	89.64	NUM
fcis-5958	81	54	observing	observe	VERB
fcis-5958	81	55	the	the	DET
fcis-5958	81	56	prediction	prediction	NOUN
fcis-5958	81	57	results	result	NOUN
fcis-5958	81	58	of	of	ADP
fcis-5958	81	59	various	various	ADJ
fcis-5958	81	60	algorithms	algorithm	NOUN
fcis-5958	81	61	in	in	ADP
fcis-5958	81	62	table	table	NOUN
fcis-5958	81	63	5	5	NUM
fcis-5958	81	64	,	,	PUNCT
fcis-5958	81	65	on	on	ADP
fcis-5958	81	66	the	the	DET
fcis-5958	81	67	whole	whole	NOUN
fcis-5958	81	68	,	,	PUNCT
fcis-5958	81	69	the	the	DET
fcis-5958	81	70	accuracy	accuracy	NOUN
fcis-5958	81	71	of	of	ADP
fcis-5958	81	72	three	three	NUM
fcis-5958	81	73	methods	method	NOUN
fcis-5958	81	74	of	of	ADP
fcis-5958	81	75	random	random	ADJ
fcis-5958	81	76	forest	forest	NOUN
fcis-5958	81	77	,	,	PUNCT
fcis-5958	81	78	logistic	logistic	ADJ
fcis-5958	81	79	regression	regression	NOUN
fcis-5958	81	80	and	and	CCONJ
fcis-5958	81	81	xgboost	xgboost	NOUN
fcis-5958	81	82	is	be	AUX
fcis-5958	81	83	more	more	ADJ
fcis-5958	81	84	than	than	ADP
fcis-5958	81	85	80	80	NUM
fcis-5958	81	86	%	%	NOUN
fcis-5958	81	87	,	,	PUNCT
fcis-5958	81	88	and	and	CCONJ
fcis-5958	81	89	the	the	DET
fcis-5958	81	90	mipca	mipca	ADV
fcis-5958	81	91	-	-	PUNCT
fcis-5958	81	92	xgboost	xgboost	PROPN
fcis-5958	81	93	algorithm	algorithm	PROPN
fcis-5958	81	94	has	have	VERB
fcis-5958	81	95	the	the	DET
fcis-5958	81	96	highest	high	ADJ
fcis-5958	81	97	score	score	NOUN
fcis-5958	81	98	,	,	PUNCT
fcis-5958	81	99	with	with	ADP
fcis-5958	81	100	an	an	DET
fcis-5958	81	101	accuracy	accuracy	NOUN
fcis-5958	81	102	of	of	ADP
fcis-5958	81	103	90.56	90.56	NUM
fcis-5958	81	104	%	%	NOUN
fcis-5958	81	105	,	,	PUNCT
fcis-5958	81	106	a	a	DET
fcis-5958	81	107	recall	recall	NOUN
fcis-5958	81	108	rate	rate	NOUN
fcis-5958	81	109	of	of	ADP
fcis-5958	81	110	90.23	90.23	NUM
fcis-5958	81	111	%	%	NOUN
fcis-5958	81	112	,	,	PUNCT
fcis-5958	81	113	and	and	CCONJ
fcis-5958	81	114	a	a	DET
fcis-5958	81	115	𝐹_𝑆𝑐𝑜𝑟𝑒	𝐹_𝑆𝑐𝑜𝑟𝑒	NOUN
fcis-5958	81	116	value	value	NOUN
fcis-5958	81	117	of	of	ADP
fcis-5958	81	118	89.64	89.64	NUM
fcis-5958	81	119	%	%	NOUN
fcis-5958	81	120	.	.	PUNCT
fcis-5958	82	1	the	the	DET
fcis-5958	82	2	accuracy	accuracy	NOUN
fcis-5958	82	3	of	of	ADP
fcis-5958	82	4	the	the	DET
fcis-5958	82	5	random	random	ADJ
fcis-5958	82	6	forest	forest	NOUN
fcis-5958	82	7	algorithm	algorithm	NOUN
fcis-5958	82	8	is	be	AUX
fcis-5958	82	9	84.12	84.12	NUM
fcis-5958	82	10	%	%	NOUN
fcis-5958	82	11	,	,	PUNCT
fcis-5958	82	12	and	and	CCONJ
fcis-5958	82	13	the	the	DET
fcis-5958	82	14	accuracy	accuracy	NOUN
fcis-5958	82	15	of	of	ADP
fcis-5958	82	16	the	the	DET
fcis-5958	82	17	logistic	logistic	ADJ
fcis-5958	82	18	regression	regression	NOUN
fcis-5958	82	19	algorithm	algorithm	NOUN
fcis-5958	82	20	is	be	AUX
fcis-5958	82	21	81.43	81.43	NUM
fcis-5958	82	22	%	%	NOUN
fcis-5958	82	23	,	,	PUNCT
fcis-5958	82	24	which	which	PRON
fcis-5958	82	25	indicates	indicate	VERB
fcis-5958	82	26	that	that	SCONJ
fcis-5958	82	27	the	the	DET
fcis-5958	82	28	mipca	mipca	ADV
fcis-5958	82	29	-	-	PUNCT
fcis-5958	82	30	xgboost	xgboost	PROPN
fcis-5958	82	31	method	method	NOUN
fcis-5958	82	32	is	be	AUX
fcis-5958	82	33	more	more	ADV
fcis-5958	82	34	suitable	suitable	ADJ
fcis-5958	82	35	for	for	ADP
fcis-5958	82	36	application	application	NOUN
fcis-5958	82	37	in	in	ADP
fcis-5958	82	38	this	this	DET
fcis-5958	82	39	field	field	NOUN
fcis-5958	82	40	.	.	PUNCT
fcis-5958	83	1	secondly	secondly	ADV
fcis-5958	83	2	,	,	PUNCT
fcis-5958	83	3	through	through	ADP
fcis-5958	83	4	the	the	DET
fcis-5958	83	5	comparison	comparison	NOUN
fcis-5958	83	6	of	of	ADP
fcis-5958	83	7	the	the	DET
fcis-5958	83	8	xgboost	xgboost	PROPN
fcis-5958	83	9	algorithm	algorithm	PROPN
fcis-5958	83	10	mipca	mipca	PROPN
fcis-5958	83	11	-	-	PUNCT
fcis-5958	83	12	xgboost	xgboost	PROPN
fcis-5958	83	13	algorithm	algorithm	PROPN
fcis-5958	83	14	,	,	PUNCT
fcis-5958	83	15	it	it	PRON
fcis-5958	83	16	can	can	AUX
fcis-5958	83	17	be	be	AUX
fcis-5958	83	18	seen	see	VERB
fcis-5958	83	19	that	that	SCONJ
fcis-5958	83	20	the	the	DET
fcis-5958	83	21	mipca	mipca	ADJ
fcis-5958	83	22	feature	feature	NOUN
fcis-5958	83	23	selection	selection	NOUN
fcis-5958	83	24	method	method	NOUN
fcis-5958	83	25	improves	improve	VERB
fcis-5958	83	26	the	the	DET
fcis-5958	83	27	prediction	prediction	NOUN
fcis-5958	83	28	accuracy	accuracy	NOUN
fcis-5958	83	29	of	of	ADP
fcis-5958	83	30	the	the	DET
fcis-5958	83	31	xgboost	xgboost	PROPN
fcis-5958	83	32	algorithm	algorithm	NOUN
fcis-5958	83	33	in	in	ADP
fcis-5958	83	34	this	this	DET
fcis-5958	83	35	experiment	experiment	NOUN
fcis-5958	83	36	,	,	PUNCT
fcis-5958	83	37	its	its	PRON
fcis-5958	83	38	accuracy	accuracy	NOUN
fcis-5958	83	39	is	be	AUX
fcis-5958	83	40	increased	increase	VERB
fcis-5958	83	41	by	by	ADP
fcis-5958	83	42	4.19	4.19	NUM
fcis-5958	83	43	%	%	NOUN
fcis-5958	83	44	,	,	PUNCT
fcis-5958	83	45	the	the	DET
fcis-5958	83	46	recall	recall	NOUN
fcis-5958	83	47	rate	rate	NOUN
fcis-5958	83	48	is	be	AUX
fcis-5958	83	49	increased	increase	VERB
fcis-5958	83	50	by	by	ADP
fcis-5958	83	51	5.65	5.65	NUM
fcis-5958	83	52	%	%	NOUN
fcis-5958	83	53	,	,	PUNCT
fcis-5958	83	54	and	and	CCONJ
fcis-5958	83	55	the	the	DET
fcis-5958	83	56	𝐹_𝑆𝑐𝑜𝑟𝑒	𝐹_𝑆𝑐𝑜𝑟𝑒	NOUN
fcis-5958	83	57	value	value	NOUN
fcis-5958	83	58	is	be	AUX
fcis-5958	83	59	increased	increase	VERB
fcis-5958	83	60	by	by	ADP
fcis-5958	83	61	4.91	4.91	NUM
fcis-5958	83	62	%	%	NOUN
fcis-5958	83	63	.	.	PUNCT
fcis-5958	84	1	5	5	X
fcis-5958	84	2	.	.	X
fcis-5958	84	3	conclusion	conclusion	NOUN
fcis-5958	84	4	from	from	ADP
fcis-5958	84	5	the	the	DET
fcis-5958	84	6	perspective	perspective	NOUN
fcis-5958	84	7	of	of	ADP
fcis-5958	84	8	making	make	VERB
fcis-5958	84	9	full	full	ADJ
fcis-5958	84	10	use	use	NOUN
fcis-5958	84	11	of	of	ADP
fcis-5958	84	12	the	the	DET
fcis-5958	84	13	original	original	ADJ
fcis-5958	84	14	information	information	NOUN
fcis-5958	84	15	of	of	ADP
fcis-5958	84	16	the	the	DET
fcis-5958	84	17	data	datum	NOUN
fcis-5958	84	18	set	set	VERB
fcis-5958	84	19	,	,	PUNCT
fcis-5958	84	20	this	this	DET
fcis-5958	84	21	paper	paper	NOUN
fcis-5958	84	22	introduces	introduce	VERB
fcis-5958	84	23	the	the	DET
fcis-5958	84	24	mutual	mutual	ADJ
fcis-5958	84	25	information	information	NOUN
fcis-5958	84	26	feature	feature	NOUN
fcis-5958	84	27	selection	selection	NOUN
fcis-5958	84	28	method	method	NOUN
fcis-5958	84	29	in	in	ADP
fcis-5958	84	30	the	the	DET
fcis-5958	84	31	study	study	NOUN
fcis-5958	84	32	of	of	ADP
fcis-5958	84	33	customer	customer	NOUN
fcis-5958	84	34	churn	churn	NOUN
fcis-5958	84	35	prediction	prediction	NOUN
fcis-5958	84	36	in	in	ADP
fcis-5958	84	37	the	the	DET
fcis-5958	84	38	telecom	telecom	NOUN
fcis-5958	84	39	industry	industry	NOUN
fcis-5958	84	40	,	,	PUNCT
fcis-5958	84	41	and	and	CCONJ
fcis-5958	84	42	proposes	propose	VERB
fcis-5958	84	43	the	the	DET
fcis-5958	84	44	mipca	mipca	ADV
fcis-5958	84	45	-	-	PUNCT
fcis-5958	84	46	xgboost	xgboost	PROPN
fcis-5958	84	47	method	method	NOUN
fcis-5958	84	48	,	,	PUNCT
fcis-5958	84	49	which	which	PRON
fcis-5958	84	50	successfully	successfully	ADV
fcis-5958	84	51	improves	improve	VERB
fcis-5958	84	52	the	the	DET
fcis-5958	84	53	prediction	prediction	NOUN
fcis-5958	84	54	accuracy	accuracy	NOUN
fcis-5958	84	55	.	.	PUNCT
fcis-5958	85	1	by	by	ADP
fcis-5958	85	2	selecting	select	VERB
fcis-5958	85	3	the	the	DET
fcis-5958	85	4	telecom	telecom	NOUN
fcis-5958	85	5	customer	customer	NOUN
fcis-5958	85	6	data	datum	NOUN
fcis-5958	85	7	set	set	VERB
fcis-5958	85	8	from	from	ADP
fcis-5958	85	9	kaggle	kaggle	NOUN
fcis-5958	85	10	website	website	NOUN
fcis-5958	85	11	for	for	ADP
fcis-5958	85	12	experiments	experiment	NOUN
fcis-5958	85	13	,	,	PUNCT
fcis-5958	85	14	the	the	DET
fcis-5958	85	15	results	result	NOUN
fcis-5958	85	16	show	show	VERB
fcis-5958	85	17	that	that	SCONJ
fcis-5958	85	18	the	the	DET
fcis-5958	85	19	prediction	prediction	NOUN
fcis-5958	85	20	accuracy	accuracy	NOUN
fcis-5958	85	21	of	of	ADP
fcis-5958	85	22	mipca	mipca	PROPN
fcis-5958	85	23	-	-	PUNCT
fcis-5958	85	24	xgboost	xgboost	PROPN
fcis-5958	85	25	method	method	NOUN
fcis-5958	85	26	is	be	AUX
fcis-5958	85	27	as	as	ADV
fcis-5958	85	28	high	high	ADJ
fcis-5958	85	29	as	as	ADP
fcis-5958	85	30	90.56	90.56	NUM
fcis-5958	85	31	%	%	NOUN
fcis-5958	85	32	.	.	PUNCT
fcis-5958	86	1	it	it	PRON
fcis-5958	86	2	can	can	AUX
fcis-5958	86	3	be	be	AUX
fcis-5958	86	4	seen	see	VERB
fcis-5958	86	5	that	that	SCONJ
fcis-5958	86	6	the	the	DET
fcis-5958	86	7	algorithm	algorithm	NOUN
fcis-5958	86	8	is	be	AUX
fcis-5958	86	9	helpful	helpful	ADJ
fcis-5958	86	10	for	for	ADP
fcis-5958	86	11	the	the	DET
fcis-5958	86	12	further	further	ADJ
fcis-5958	86	13	research	research	NOUN
fcis-5958	86	14	of	of	ADP
fcis-5958	86	15	customer	customer	NOUN
fcis-5958	86	16	churn	churn	NOUN
fcis-5958	86	17	prediction	prediction	NOUN
fcis-5958	86	18	in	in	ADP
fcis-5958	86	19	the	the	DET
fcis-5958	86	20	future	future	NOUN
fcis-5958	86	21	,	,	PUNCT
fcis-5958	86	22	and	and	CCONJ
fcis-5958	86	23	provides	provide	VERB
fcis-5958	86	24	a	a	DET
fcis-5958	86	25	new	new	ADJ
fcis-5958	86	26	idea	idea	NOUN
fcis-5958	86	27	for	for	SCONJ
fcis-5958	86	28	enterprises	enterprise	NOUN
fcis-5958	86	29	to	to	PART
fcis-5958	86	30	solve	solve	VERB
fcis-5958	86	31	the	the	DET
fcis-5958	86	32	problem	problem	NOUN
fcis-5958	86	33	of	of	ADP
fcis-5958	86	34	customer	customer	NOUN
fcis-5958	86	35	churn	churn	NOUN
fcis-5958	86	36	,	,	PUNCT
fcis-5958	86	37	helping	help	VERB
fcis-5958	86	38	enterprises	enterprise	NOUN
fcis-5958	86	39	to	to	PART
fcis-5958	86	40	be	be	AUX
fcis-5958	86	41	more	more	ADV
fcis-5958	86	42	targeted	target	VERB
fcis-5958	86	43	and	and	CCONJ
fcis-5958	86	44	accurate	accurate	ADJ
fcis-5958	86	45	in	in	ADP
fcis-5958	86	46	formulating	formulate	VERB
fcis-5958	86	47	customer	customer	NOUN
fcis-5958	86	48	maintenance	maintenance	NOUN
fcis-5958	86	49	strategies	strategy	NOUN
fcis-5958	86	50	.	.	PUNCT
fcis-5958	87	1	the	the	DET
fcis-5958	87	2	research	research	NOUN
fcis-5958	87	3	of	of	ADP
fcis-5958	87	4	this	this	DET
fcis-5958	87	5	paper	paper	NOUN
fcis-5958	87	6	also	also	ADV
fcis-5958	87	7	has	have	VERB
fcis-5958	87	8	some	some	DET
fcis-5958	87	9	shortcomings	shortcoming	NOUN
fcis-5958	87	10	:1	:1	PUNCT
fcis-5958	87	11	)	)	PUNCT
fcis-5958	87	12	at	at	ADP
fcis-5958	87	13	present	present	ADJ
fcis-5958	87	14	,	,	PUNCT
fcis-5958	87	15	the	the	DET
fcis-5958	87	16	rapid	rapid	ADJ
fcis-5958	87	17	development	development	NOUN
fcis-5958	87	18	of	of	ADP
fcis-5958	87	19	internet	internet	NOUN
fcis-5958	87	20	communication	communication	NOUN
fcis-5958	87	21	has	have	AUX
fcis-5958	87	22	become	become	VERB
fcis-5958	87	23	an	an	DET
fcis-5958	87	24	inseparable	inseparable	ADJ
fcis-5958	87	25	part	part	NOUN
fcis-5958	87	26	of	of	ADP
fcis-5958	87	27	people	people	NOUN
fcis-5958	87	28	's	's	PART
fcis-5958	87	29	communication	communication	NOUN
fcis-5958	87	30	and	and	CCONJ
fcis-5958	87	31	contact	contact	NOUN
fcis-5958	87	32	.	.	PUNCT
fcis-5958	88	1	under	under	ADP
fcis-5958	88	2	this	this	DET
fcis-5958	88	3	impact	impact	NOUN
fcis-5958	88	4	,	,	PUNCT
fcis-5958	88	5	many	many	ADJ
fcis-5958	88	6	users	user	NOUN
fcis-5958	88	7	'	'	PART
fcis-5958	88	8	communication	communication	NOUN
fcis-5958	88	9	behaviors	behavior	NOUN
fcis-5958	88	10	will	will	AUX
fcis-5958	88	11	be	be	AUX
fcis-5958	88	12	affected	affect	VERB
fcis-5958	88	13	by	by	ADP
fcis-5958	88	14	instant	instant	ADJ
fcis-5958	88	15	messaging	messaging	NOUN
fcis-5958	88	16	tools	tool	NOUN
fcis-5958	88	17	.	.	PUNCT
fcis-5958	89	1	in	in	ADP
fcis-5958	89	2	the	the	DET
fcis-5958	89	3	future	future	NOUN
fcis-5958	89	4	,	,	PUNCT
fcis-5958	89	5	we	we	PRON
fcis-5958	89	6	will	will	AUX
fcis-5958	89	7	consider	consider	VERB
fcis-5958	89	8	the	the	DET
fcis-5958	89	9	third	third	ADJ
fcis-5958	89	10	-	-	PUNCT
fcis-5958	89	11	party	party	NOUN
fcis-5958	89	12	communication	communication	NOUN
fcis-5958	89	13	software	software	NOUN
fcis-5958	89	14	and	and	CCONJ
fcis-5958	89	15	internet	internet	NOUN
fcis-5958	89	16	platform	platform	NOUN
fcis-5958	89	17	data	datum	NOUN
fcis-5958	89	18	for	for	ADP
fcis-5958	89	19	research	research	NOUN
fcis-5958	89	20	.	.	PUNCT
fcis-5958	90	1	2	2	X
fcis-5958	90	2	)	)	PUNCT
fcis-5958	90	3	the	the	DET
fcis-5958	90	4	data	datum	NOUN
fcis-5958	90	5	in	in	ADP
fcis-5958	90	6	this	this	DET
fcis-5958	90	7	paper	paper	NOUN
fcis-5958	90	8	are	be	AUX
fcis-5958	90	9	from	from	ADP
fcis-5958	90	10	a	a	DET
fcis-5958	90	11	single	single	ADJ
fcis-5958	90	12	telecom	telecom	NOUN
fcis-5958	90	13	industry	industry	NOUN
fcis-5958	90	14	customer	customer	NOUN
fcis-5958	90	15	data	datum	NOUN
fcis-5958	90	16	,	,	PUNCT
fcis-5958	90	17	and	and	CCONJ
fcis-5958	90	18	in	in	ADP
fcis-5958	90	19	the	the	DET
fcis-5958	90	20	future	future	NOUN
fcis-5958	90	21	,	,	PUNCT
fcis-5958	90	22	multiple	multiple	ADJ
fcis-5958	90	23	operators	operator	NOUN
fcis-5958	90	24	in	in	ADP
fcis-5958	90	25	the	the	DET
fcis-5958	90	26	same	same	ADJ
fcis-5958	90	27	time	time	NOUN
fcis-5958	90	28	period	period	NOUN
fcis-5958	90	29	will	will	AUX
fcis-5958	90	30	be	be	AUX
fcis-5958	90	31	considered	consider	VERB
fcis-5958	90	32	for	for	ADP
fcis-5958	90	33	joint	joint	ADJ
fcis-5958	90	34	research	research	NOUN
fcis-5958	90	35	to	to	PART
fcis-5958	90	36	make	make	VERB
fcis-5958	90	37	the	the	DET
fcis-5958	90	38	research	research	NOUN
fcis-5958	90	39	more	more	ADV
fcis-5958	90	40	comprehensive	comprehensive	ADJ
fcis-5958	90	41	.	.	PUNCT
fcis-5958	91	1	in	in	ADP
fcis-5958	91	2	future	future	ADJ
fcis-5958	91	3	research	research	NOUN
fcis-5958	91	4	,	,	PUNCT
fcis-5958	91	5	it	it	PRON
fcis-5958	91	6	is	be	AUX
fcis-5958	91	7	necessary	necessary	ADJ
fcis-5958	91	8	to	to	PART
fcis-5958	91	9	classify	classify	VERB
fcis-5958	91	10	the	the	DET
fcis-5958	91	11	customers	customer	NOUN
fcis-5958	91	12	who	who	PRON
fcis-5958	91	13	have	have	AUX
fcis-5958	91	14	been	be	AUX
fcis-5958	91	15	predicted	predict	VERB
fcis-5958	91	16	to	to	PART
fcis-5958	91	17	have	have	VERB
fcis-5958	91	18	churn	churn	NOUN
fcis-5958	91	19	trend	trend	NOUN
fcis-5958	91	20	,	,	PUNCT
fcis-5958	91	21	create	create	VERB
fcis-5958	91	22	a	a	DET
fcis-5958	91	23	classification	classification	NOUN
fcis-5958	91	24	model	model	NOUN
fcis-5958	91	25	,	,	PUNCT
fcis-5958	91	26	and	and	CCONJ
fcis-5958	91	27	then	then	ADV
fcis-5958	91	28	propose	propose	VERB
fcis-5958	91	29	an	an	DET
fcis-5958	91	30	effective	effective	ADJ
fcis-5958	91	31	customer	customer	NOUN
fcis-5958	91	32	retention	retention	NOUN
fcis-5958	91	33	strategy	strategy	NOUN
fcis-5958	91	34	.	.	PUNCT
fcis-5958	92	1	in	in	ADP
fcis-5958	92	2	addition	addition	NOUN
fcis-5958	92	3	,	,	PUNCT
fcis-5958	92	4	in	in	ADP
fcis-5958	92	5	the	the	DET
fcis-5958	92	6	study	study	NOUN
fcis-5958	92	7	of	of	ADP
fcis-5958	92	8	customer	customer	NOUN
fcis-5958	92	9	churn	churn	NOUN
fcis-5958	92	10	prediction	prediction	NOUN
fcis-5958	92	11	,	,	PUNCT
fcis-5958	92	12	it	it	PRON
fcis-5958	92	13	is	be	AUX
fcis-5958	92	14	necessary	necessary	ADJ
fcis-5958	92	15	to	to	PART
fcis-5958	92	16	pay	pay	VERB
fcis-5958	92	17	attention	attention	NOUN
fcis-5958	92	18	to	to	ADP
fcis-5958	92	19	the	the	DET
fcis-5958	92	20	interaction	interaction	NOUN
fcis-5958	92	21	between	between	ADP
fcis-5958	92	22	customers	customer	NOUN
fcis-5958	92	23	,	,	PUNCT
fcis-5958	92	24	which	which	PRON
fcis-5958	92	25	may	may	AUX
fcis-5958	92	26	be	be	AUX
fcis-5958	92	27	an	an	DET
fcis-5958	92	28	important	important	ADJ
fcis-5958	92	29	factor	factor	NOUN
fcis-5958	92	30	affecting	affect	VERB
fcis-5958	92	31	customer	customer	NOUN
fcis-5958	92	32	churn	churn	NOUN
fcis-5958	92	33	,	,	PUNCT
fcis-5958	92	34	which	which	PRON
fcis-5958	92	35	is	be	AUX
fcis-5958	92	36	of	of	ADP
fcis-5958	92	37	great	great	ADJ
fcis-5958	92	38	significance	significance	NOUN
fcis-5958	92	39	for	for	ADP
fcis-5958	92	40	the	the	DET
fcis-5958	92	41	research	research	NOUN
fcis-5958	92	42	in	in	ADP
fcis-5958	92	43	this	this	DET
fcis-5958	92	44	field	field	NOUN
fcis-5958	92	45	.	.	PUNCT
fcis-5958	93	1	references	reference	NOUN
fcis-5958	93	2	[	[	X
fcis-5958	93	3	1	1	NUM
fcis-5958	93	4	]	]	PUNCT
fcis-5958	93	5	hadden	hadden	PROPN
fcis-5958	93	6	j	j	PROPN
fcis-5958	93	7	,	,	PUNCT
fcis-5958	93	8	tiwari	tiwari	X
fcis-5958	93	9	a	a	PRON
fcis-5958	93	10	,	,	PUNCT
fcis-5958	93	11	rajkuar	rajkuar	NOUN
fcis-5958	93	12	r	r	NOUN
fcis-5958	93	13	,	,	PUNCT
fcis-5958	93	14	dymitr	dymitr	PROPN
fcis-5958	93	15	r.	r.	PROPN
fcis-5958	93	16	(	(	PUNCT
fcis-5958	93	17	2007	2007	NUM
fcis-5958	93	18	)	)	PUNCT
fcis-5958	93	19	computer	computer	NOUN
fcis-5958	93	20	assisted	assist	VERB
fcis-5958	93	21	customer	customer	NOUN
fcis-5958	93	22	churn	churn	NOUN
fcis-5958	93	23	management	management	NOUN
fcis-5958	93	24	:	:	PUNCT
fcis-5958	93	25	state	state	NOUN
fcis-5958	93	26	-	-	PUNCT
fcis-5958	93	27	of	of	ADP
fcis-5958	93	28	-	-	PUNCT
fcis-5958	93	29	theart	theart	NOUN
fcis-5958	93	30	and	and	CCONJ
fcis-5958	93	31	future	future	ADJ
fcis-5958	93	32	trends[j].computers	trends[j].computer	NOUN
fcis-5958	93	33	and	and	CCONJ
fcis-5958	93	34	operations	operation	NOUN
fcis-5958	93	35	research	research	NOUN
fcis-5958	93	36	,	,	PUNCT
fcis-5958	93	37	2007	2007	NUM
fcis-5958	93	38	,	,	PUNCT
fcis-5958	93	39	34(10):2902	34(10):2902	NUM
fcis-5958	93	40	-	-	SYM
fcis-5958	93	41	2917	2917	NUM
fcis-5958	93	42	.	.	PUNCT
fcis-5958	94	1	[	[	X
fcis-5958	94	2	2	2	NUM
fcis-5958	94	3	]	]	PUNCT
fcis-5958	94	4	zhang	zhang	PROPN
fcis-5958	94	5	l	l	PROPN
fcis-5958	94	6	l	l	PROPN
fcis-5958	94	7	,	,	PUNCT
fcis-5958	94	8	ma	ma	PROPN
fcis-5958	94	9	y	y	PROPN
fcis-5958	94	10	q.	q.	PROPN
fcis-5958	94	11	(	(	PUNCT
fcis-5958	94	12	2019	2019	NUM
fcis-5958	94	13	)	)	PUNCT
fcis-5958	94	14	analysis	analysis	NOUN
fcis-5958	94	15	of	of	ADP
fcis-5958	94	16	airline	airline	NOUN
fcis-5958	94	17	customer	customer	NOUN
fcis-5958	94	18	churn	churn	NOUN
fcis-5958	94	19	and	and	CCONJ
fcis-5958	94	20	consumer	consumer	NOUN
fcis-5958	94	21	segmentation	segmentation	NOUN
fcis-5958	94	22	based	base	VERB
fcis-5958	94	23	on	on	ADP
fcis-5958	94	24	data	data	NOUN
fcis-5958	94	25	mining	mining	NOUN
fcis-5958	94	26	algorithm	algorithm	NOUN
fcis-5958	94	27	using	use	VERB
fcis-5958	94	28	r[j].mathematics	r[j].mathematic	NOUN
fcis-5958	94	29	in	in	ADP
fcis-5958	94	30	practice	practice	NOUN
fcis-5958	94	31	and	and	CCONJ
fcis-5958	94	32	theory,2019,49(06):134	theory,2019,49(06):134	PROPN
fcis-5958	94	33	-	-	SYM
fcis-5958	94	34	142	142	NUM
fcis-5958	94	35	.	.	PUNCT
fcis-5958	95	1	[	[	X
fcis-5958	95	2	3	3	X
fcis-5958	95	3	]	]	X
fcis-5958	95	4	yan	yan	PROPN
fcis-5958	96	1	c	c	X
fcis-5958	96	2	,	,	PUNCT
fcis-5958	96	3	zhang	zhang	PROPN
fcis-5958	96	4	x	x	PUNCT
fcis-5958	96	5	y.	y.	PROPN
fcis-5958	96	6	(	(	PUNCT
fcis-5958	96	7	2022	2022	NUM
fcis-5958	96	8	)	)	PUNCT
fcis-5958	96	9	life	life	NOUN
fcis-5958	96	10	insurance	insurance	NOUN
fcis-5958	96	11	customer	customer	NOUN
fcis-5958	96	12	churn	churn	NOUN
fcis-5958	96	13	prediction	prediction	NOUN
fcis-5958	96	14	algorithm	algorithm	NOUN
fcis-5958	96	15	based	base	VERB
fcis-5958	96	16	on	on	ADP
fcis-5958	96	17	improved	improved	ADJ
fcis-5958	96	18	k	k	PROPN
fcis-5958	96	19	-	-	PUNCT
fcis-5958	96	20	means	mean	VERB
fcis-5958	96	21	ang	ang	PROPN
fcis-5958	96	22	bpadaboost[j	bpadaboost[j	NOUN
fcis-5958	96	23	]	]	PUNCT
fcis-5958	96	24	.	.	PUNCT
fcis-5958	97	1	journal	journal	PROPN
fcis-5958	97	2	of	of	ADP
fcis-5958	97	3	shandong	shandong	PROPN
fcis-5958	97	4	university	university	PROPN
fcis-5958	97	5	of	of	ADP
fcis-5958	97	6	science	science	NOUN
fcis-5958	97	7	and	and	CCONJ
fcis-5958	97	8	technology	technology	NOUN
fcis-5958	97	9	(	(	PUNCT
fcis-5958	97	10	natural	natural	ADJ
fcis-5958	97	11	science),2022,41(01):54	science),2022,41(01):54	NOUN
fcis-5958	97	12	-	-	PUNCT
fcis-5958	97	13	65	65	NUM
fcis-5958	97	14	.	.	PUNCT
fcis-5958	98	1	[	[	X
fcis-5958	98	2	4	4	NUM
fcis-5958	98	3	]	]	X
fcis-5958	98	4	wu	wu	PROPN
fcis-5958	98	5	y	y	PROPN
fcis-5958	98	6	c.	c.	PROPN
fcis-5958	98	7	(	(	PUNCT
fcis-5958	98	8	2020	2020	NUM
fcis-5958	98	9	)	)	PUNCT
fcis-5958	98	10	prediction	prediction	NOUN
fcis-5958	98	11	of	of	ADP
fcis-5958	98	12	churn	churn	NOUN
fcis-5958	98	13	rate	rate	NOUN
fcis-5958	98	14	of	of	ADP
fcis-5958	98	15	e	e	NOUN
fcis-5958	98	16	-	-	NOUN
fcis-5958	98	17	commerce	commerce	NOUN
fcis-5958	98	18	customers	customer	NOUN
fcis-5958	98	19	in	in	ADP
fcis-5958	98	20	context	context	NOUN
fcis-5958	98	21	of	of	ADP
fcis-5958	98	22	big	big	ADJ
fcis-5958	98	23	data[j	data[j	NOUN
fcis-5958	98	24	]	]	X
fcis-5958	98	25	.	.	PUNCT
fcis-5958	99	1	modern	modern	ADJ
fcis-5958	99	2	electronics	electronic	NOUN
fcis-5958	99	3	technique,2020,43(11):144	technique,2020,43(11):144	NUM
fcis-5958	99	4	-	-	SYM
fcis-5958	99	5	147	147	NUM
fcis-5958	99	6	.	.	PUNCT
fcis-5958	100	1	[	[	X
fcis-5958	100	2	5	5	X
fcis-5958	100	3	]	]	PUNCT
fcis-5958	100	4	xing	xing	PROPN
fcis-5958	100	5	w	w	PROPN
fcis-5958	100	6	,	,	PUNCT
fcis-5958	100	7	wang	wang	PROPN
fcis-5958	100	8	s	s	PROPN
fcis-5958	100	9	y	y	PROPN
fcis-5958	100	10	,	,	PUNCT
fcis-5958	100	11	zhang	zhang	PROPN
fcis-5958	100	12	q	q	PROPN
fcis-5958	100	13	h	h	PROPN
fcis-5958	100	14	,	,	PUNCT
fcis-5958	100	15	et	et	PROPN
fcis-5958	100	16	al	al	PROPN
fcis-5958	100	17	.	.	PUNCT
fcis-5958	100	18	(	(	PUNCT
fcis-5958	100	19	2011	2011	NUM
fcis-5958	100	20	)	)	PUNCT
fcis-5958	100	21	dual	dual	ADJ
fcis-5958	100	22	channel	channel	NOUN
fcis-5958	100	23	supply	supply	NOUN
fcis-5958	100	24	chain	chain	NOUN
fcis-5958	100	25	equilibrium	equilibrium	NOUN
fcis-5958	100	26	strategy	strategy	NOUN
fcis-5958	100	27	considering	consider	VERB
fcis-5958	100	28	channer	channer	PROPN
fcis-5958	100	29	fairness[j].systems	fairness[j].system	NOUN
fcis-5958	100	30	engineering	engineering	NOUN
fcis-5958	100	31	-	-	PUNCT
fcis-5958	100	32	theory	theory	NOUN
fcis-5958	100	33	&	&	CCONJ
fcis-5958	100	34	practice,2011,31(07):1249	practice,2011,31(07):1249	PROPN
fcis-5958	100	35	-	-	PUNCT
fcis-5958	100	36	1256	1256	NUM
fcis-5958	100	37	.	.	PUNCT
fcis-5958	101	1	[	[	X
fcis-5958	101	2	6	6	NUM
fcis-5958	101	3	]	]	PUNCT
fcis-5958	101	4	lemmens	lemmen	VERB
fcis-5958	101	5	a	a	DET
fcis-5958	101	6	,	,	PUNCT
fcis-5958	101	7	croux	croux	ADJ
fcis-5958	101	8	c.	c.	NOUN
fcis-5958	101	9	(	(	PUNCT
fcis-5958	101	10	2005	2005	NUM
fcis-5958	101	11	)	)	PUNCT
fcis-5958	101	12	bagging	bagging	NOUN
fcis-5958	101	13	and	and	CCONJ
fcis-5958	101	14	boosting	boost	VERB
fcis-5958	101	15	classification	classification	NOUN
fcis-5958	101	16	trees	tree	NOUN
fcis-5958	101	17	to	to	PART
fcis-5958	101	18	predict	predict	VERB
fcis-5958	101	19	churn[j	churn[j	PROPN
fcis-5958	101	20	]	]	PUNCT
fcis-5958	101	21	.	.	PUNCT
fcis-5958	102	1	journal	journal	PROPN
fcis-5958	102	2	of	of	ADP
fcis-5958	102	3	marketing	marketing	NOUN
fcis-5958	102	4	research	research	NOUN
fcis-5958	102	5	,	,	PUNCT
fcis-5958	102	6	2005	2005	NUM
fcis-5958	102	7	,	,	PUNCT
fcis-5958	102	8	43(2):276	43(2):276	NOUN
fcis-5958	102	9	-	-	PUNCT
fcis-5958	102	10	286	286	NUM
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fcis-5958	103	6	l	l	NOUN
fcis-5958	103	7	,	,	PUNCT
fcis-5958	103	8	feng	feng	PROPN
fcis-5958	103	9	h	h	PROPN
fcis-5958	103	10	h	h	PROPN
fcis-5958	103	11	,	,	PUNCT
fcis-5958	103	12	yuan	yuan	NOUN
fcis-5958	103	13	m.	m.	NOUN
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fcis-5958	103	16	)	)	PUNCT
fcis-5958	103	17	pca	pca	NOUN
fcis-5958	103	18	based	base	VERB
fcis-5958	103	19	on	on	ADP
fcis-5958	103	20	mutual	mutual	ADJ
fcis-5958	103	21	information	information	NOUN
fcis-5958	103	22	for	for	ADP
fcis-5958	103	23	feature	feature	NOUN
fcis-5958	103	24	selection[j].control	selection[j].control	NOUN
fcis-5958	103	25	and	and	CCONJ
fcis-5958	103	26	decision	decision	NOUN
fcis-5958	103	27	,	,	PUNCT
fcis-5958	103	28	2013	2013	NUM
fcis-5958	103	29	,	,	PUNCT
fcis-5958	103	30	28(06):915	28(06):915	NUM
fcis-5958	103	31	-	-	SYM
fcis-5958	103	32	919	919	NUM
fcis-5958	103	33	.	.	PUNCT
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fcis-5958	104	3	]	]	X
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fcis-5958	104	5	h	h	PROPN
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fcis-5958	104	7	,	,	PUNCT
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fcis-5958	104	9	q	q	PROPN
fcis-5958	104	10	l	l	PROPN
fcis-5958	104	11	,	,	PUNCT
fcis-5958	104	12	xing	xing	PROPN
fcis-5958	104	13	j	j	PROPN
fcis-5958	104	14	c	c	X
fcis-5958	104	15	,	,	PUNCT
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fcis-5958	104	17	al	al	PROPN
fcis-5958	104	18	.	.	PROPN
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fcis-5958	104	24	prediction	prediction	NOUN
fcis-5958	104	25	based	base	VERB
fcis-5958	104	26	on	on	ADP
fcis-5958	104	27	combined	combined	ADJ
fcis-5958	104	28	xgboost	xgboost	PROPN
fcis-5958	104	29	-	-	PUNCT
fcis-5958	104	30	lstm	lstm	ADJ
fcis-5958	104	31	model[j].acta	model[j].acta	ADJ
fcis-5958	104	32	energiae	energiae	NOUN
fcis-5958	104	33	solaris	solaris	PROPN
fcis-5958	104	34	sinica,2022,43(08):75	sinica,2022,43(08):75	PROPN
fcis-5958	104	35	-	-	PUNCT
fcis-5958	104	36	81	81	NUM
fcis-5958	104	37	.	.	PUNCT
fcis-5958	105	1	[	[	X
fcis-5958	105	2	9	9	NUM
fcis-5958	105	3	]	]	X
fcis-5958	105	4	peng	peng	PROPN
fcis-5958	105	5	s	s	PROPN
fcis-5958	105	6	y	y	PROPN
fcis-5958	105	7	,	,	PUNCT
fcis-5958	105	8	zheng	zheng	PROPN
fcis-5958	105	9	g	g	PROPN
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fcis-5958	105	11	,	,	PUNCT
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fcis-5958	105	13	s	s	PROPN
fcis-5958	105	14	j	j	PROPN
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fcis-5958	105	17	al	al	PROPN
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fcis-5958	105	20	2020	2020	NUM
fcis-5958	105	21	)	)	PUNCT
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fcis-5958	105	23	short	short	ADJ
fcis-5958	105	24	-	-	PUNCT
fcis-5958	105	25	term	term	NOUN
fcis-5958	105	26	photovoltaic	photovoltaic	NOUN
fcis-5958	105	27	generation	generation	NOUN
fcis-5958	105	28	forecasting	forecasting	NOUN
fcis-5958	105	29	based	base	VERB
fcis-5958	105	30	on	on	ADP
fcis-5958	105	31	xgboost	xgboost	PROPN
fcis-5958	105	32	algorithm[j].electrical	algorithm[j].electrical	PROPN
fcis-5958	105	33	measurement	measurement	PROPN
fcis-5958	105	34	&	&	CCONJ
fcis-5958	105	35	instrumentation	instrumentation	NOUN
fcis-5958	105	36	,	,	PUNCT
fcis-5958	105	37	2020,57(24):76	2020,57(24):76	NUM
fcis-5958	105	38	-	-	SYM
fcis-5958	105	39	83	83	NUM
fcis-5958	105	40	.	.	PUNCT
fcis-5958	106	1	[	[	X
fcis-5958	106	2	10	10	NUM
fcis-5958	106	3	]	]	X
fcis-5958	106	4	lu	lu	PROPN
fcis-5958	106	5	s	s	PROPN
fcis-5958	106	6	,	,	PUNCT
fcis-5958	106	7	xu	xu	PROPN
fcis-5958	106	8	w	w	PROPN
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fcis-5958	106	10	,	,	PUNCT
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fcis-5958	106	13	l	l	PROPN
fcis-5958	106	14	,	,	PUNCT
fcis-5958	106	15	et	et	PROPN
fcis-5958	106	16	al	al	PROPN
fcis-5958	106	17	.	.	PROPN
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fcis-5958	106	19	2020	2020	NUM
fcis-5958	106	20	)	)	PUNCT
fcis-5958	106	21	short	short	ADJ
fcis-5958	106	22	-	-	PUNCT
fcis-5958	106	23	term	term	NOUN
fcis-5958	106	24	forecasting	forecasting	NOUN
fcis-5958	106	25	of	of	ADP
fcis-5958	106	26	pv	pv	NOUN
fcis-5958	106	27	power	power	NOUN
fcis-5958	106	28	generation	generation	NOUN
fcis-5958	106	29	based	base	VERB
fcis-5958	106	30	on	on	ADP
fcis-5958	106	31	clustering	clustering	NOUN
fcis-5958	106	32	and	and	CCONJ
fcis-5958	106	33	later	later	ADV
fcis-5958	106	34	regression[j].zhejiang	regression[j].zhejiang	VERB
fcis-5958	106	35	electic	electic	ADJ
fcis-5958	106	36	power,2020,39(07):48	power,2020,39(07):48	PROPN
fcis-5958	106	37	-	-	PUNCT
fcis-5958	106	38	54	54	NUM
fcis-5958	106	39	.	.	PUNCT
fcis-5958	107	1	[	[	X
fcis-5958	107	2	11	11	NUM
fcis-5958	107	3	]	]	X
fcis-5958	107	4	huang	huang	PROPN
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fcis-5958	107	6	of	of	ADP
fcis-5958	107	7	power	power	NOUN
fcis-5958	107	8	genereation	genereation	NOUN
fcis-5958	107	9	capacity	capacity	NOUN
fcis-5958	107	10	of	of	ADP
fcis-5958	107	11	photovoltatic	photovoltatic	ADJ
fcis-5958	107	12	system	system	NOUN
fcis-5958	107	13	base	base	NOUN
fcis-5958	107	14	on	on	ADP
fcis-5958	107	15	artificial	artificial	ADJ
fcis-5958	107	16	neural	neural	ADJ
fcis-5958	107	17	network.[d].wuhu	network.[d].wuhu	PROPN
fcis-5958	107	18	:	:	PUNCT
fcis-5958	107	19	anhui	anhui	PROPN
fcis-5958	107	20	polytechnic	polytechnic	PROPN
fcis-5958	107	21	university,2016	university,2016	PROPN
fcis-5958	107	22	.	.	PROPN
fcis-5958	107	23	5	5	NUM
fcis-5958	108	1	[	[	SYM
fcis-5958	108	2	12	12	NUM
fcis-5958	108	3	]	]	PUNCT
fcis-5958	108	4	tang	tang	PROPN
fcis-5958	108	5	h	h	PROPN
fcis-5958	108	6	,	,	PUNCT
fcis-5958	108	7	wang	wang	PROPN
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fcis-5958	108	9	,	,	PUNCT
fcis-5958	108	10	song	song	NOUN
fcis-5958	108	11	b	b	PROPN
fcis-5958	108	12	,	,	PUNCT
fcis-5958	108	13	et	et	PROPN
fcis-5958	108	14	al	al	PROPN
fcis-5958	108	15	.	.	PROPN
fcis-5958	109	1	(	(	PUNCT
fcis-5958	109	2	2021	2021	NUM
fcis-5958	109	3	)	)	PUNCT
fcis-5958	109	4	classification	classification	NOUN
fcis-5958	109	5	of	of	ADP
fcis-5958	109	6	flight	flight	NOUN
fcis-5958	109	7	delay	delay	NOUN
fcis-5958	109	8	based	base	VERB
fcis-5958	109	9	on	on	ADP
fcis-5958	109	10	nonlinear	nonlinear	ADJ
fcis-5958	109	11	weighted	weight	VERB
fcis-5958	109	12	xgboost[j].journal	xgboost[j].journal	PROPN
fcis-5958	109	13	of	of	ADP
fcis-5958	109	14	system	system	NOUN
fcis-5958	109	15	simulation,2021,33(09):2261	simulation,2021,33(09):2261	PROPN
fcis-5958	109	16	-	-	PUNCT
fcis-5958	109	17	2269	2269	NUM
fcis-5958	109	18	.	.	PUNCT
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fcis-5958	110	4	wang	wang	PROPN
fcis-5958	110	5	h	h	PROPN
fcis-5958	110	6	,	,	PUNCT
fcis-5958	110	7	zhang	zhang	PROPN
fcis-5958	110	8	w	w	PROPN
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fcis-5958	110	10	,	,	PUNCT
fcis-5958	110	11	liu	liu	PROPN
fcis-5958	110	12	j	j	PROPN
fcis-5958	110	13	,	,	PUNCT
fcis-5958	110	14	et	et	PROPN
fcis-5958	110	15	al	al	PROPN
fcis-5958	110	16	.	.	PROPN
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fcis-5958	110	18	2022	2022	NUM
fcis-5958	110	19	)	)	PUNCT
fcis-5958	110	20	flight	flight	NOUN
fcis-5958	110	21	delay	delay	NOUN
fcis-5958	110	22	prediction	prediction	NOUN
fcis-5958	110	23	model	model	NOUN
fcis-5958	110	24	based	base	VERB
fcis-5958	110	25	on	on	ADP
fcis-5958	110	26	cart	cart	NOUN
fcis-5958	110	27	algorithm[j	algorithm[j	PROPN
fcis-5958	110	28	]	]	PUNCT
fcis-5958	110	29	.	.	PUNCT
fcis-5958	111	1	journal	journal	PROPN
fcis-5958	111	2	of	of	ADP
fcis-5958	111	3	civil	civil	ADJ
fcis-5958	111	4	aviation	aviation	NOUN
fcis-5958	111	5	university	university	PROPN
fcis-5958	111	6	of	of	ADP
fcis-5958	111	7	china,2022,40(03):35	china,2022,40(03):35	NOUN
fcis-5958	111	8	-	-	SYM
fcis-5958	111	9	40	40	NUM
fcis-5958	111	10	.	.	PUNCT
fcis-5958	112	1	[	[	X
fcis-5958	112	2	14	14	NUM
fcis-5958	112	3	]	]	X
fcis-5958	112	4	lu	lu	PROPN
fcis-5958	112	5	m	m	PROPN
fcis-5958	112	6	d	d	PROPN
fcis-5958	112	7	,	,	PUNCT
fcis-5958	112	8	wei	wei	PROPN
fcis-5958	112	9	p	p	PROPN
fcis-5958	112	10	,	,	PUNCT
fcis-5958	112	11	he	he	PRON
fcis-5958	112	12	m	m	VERB
fcis-5958	112	13	s	s	PROPN
fcis-5958	112	14	,	,	PUNCT
fcis-5958	112	15	teng	teng	PROPN
fcis-5958	112	16	y	y	PROPN
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fcis-5958	112	19	2021	2021	NUM
fcis-5958	112	20	)	)	PUNCT
fcis-5958	112	21	flight	flight	NOUN
fcis-5958	112	22	delay	delay	NOUN
fcis-5958	112	23	prediction	prediction	NOUN
fcis-5958	112	24	using	use	VERB
fcis-5958	112	25	gradient	gradient	NOUN
fcis-5958	112	26	boosting	boost	VERB
fcis-5958	112	27	machine	machine	NOUN
fcis-5958	112	28	learning	learn	VERB
fcis-5958	112	29	classifiers[j	classifiers[j	PROPN
fcis-5958	112	30	]	]	PUNCT
fcis-5958	112	31	.	.	PUNCT
fcis-5958	113	1	journal	journal	PROPN
fcis-5958	113	2	of	of	ADP
fcis-5958	113	3	quantum	quantum	ADJ
fcis-5958	113	4	computing,2021,3(1	computing,2021,3(1	NOUN
fcis-5958	113	5	)	)	PUNCT
fcis-5958	113	6	.	.	PUNCT
fcis-5958	114	1	[	[	X
fcis-5958	114	2	15	15	NUM
fcis-5958	114	3	]	]	X
fcis-5958	114	4	wang	wang	PROPN
fcis-5958	114	5	x	x	PROPN
fcis-5958	114	6	y	y	PROPN
fcis-5958	114	7	,	,	PUNCT
fcis-5958	114	8	wang	wang	PROPN
fcis-5958	114	9	z	z	PROPN
fcis-5958	114	10	y	y	PROPN
fcis-5958	114	11	,	,	PUNCT
fcis-5958	114	12	zhao	zhao	PROPN
fcis-5958	114	13	z	z	PROPN
fcis-5958	114	14	,	,	PUNCT
fcis-5958	114	15	et	et	PROPN
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fcis-5958	114	25	forecast	forecast	NOUN
fcis-5958	114	26	model	model	NOUN
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fcis-5958	114	28	with	with	ADP
fcis-5958	114	29	improved	improved	ADJ
fcis-5958	114	30	ahp	ahp	NOUN
fcis-5958	114	31	and	and	CCONJ
fcis-5958	114	32	xgboost	xgboost	NOUN
fcis-5958	114	33	algorithm	algorithm	PROPN
fcis-5958	114	34	:	:	PUNCT
fcis-5958	114	35	a	a	DET
fcis-5958	114	36	case	case	NOUN
fcis-5958	114	37	study	study	NOUN
fcis-5958	114	38	of	of	ADP
fcis-5958	114	39	rice[j	rice[j	NOUN
fcis-5958	114	40	]	]	PUNCT
fcis-5958	114	41	.	.	PUNCT
fcis-5958	115	1	journal	journal	PROPN
fcis-5958	115	2	of	of	ADP
fcis-5958	115	3	food	food	NOUN
fcis-5958	115	4	science	science	NOUN
fcis-5958	115	5	and	and	CCONJ
fcis-5958	115	6	technology,2022,40(01):150	technology,2022,40(01):150	PROPN
fcis-5958	115	7	-	-	PUNCT
fcis-5958	115	8	158	158	NUM
fcis-5958	115	9	.	.	PUNCT
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fcis-5958	116	3	]	]	X
fcis-5958	116	4	ma	ma	PROPN
fcis-5958	116	5	h	h	PROPN
fcis-5958	116	6	d.	d.	PROPN
fcis-5958	116	7	food	food	PROPN
fcis-5958	116	8	safety	safety	PROPN
fcis-5958	116	9	risk	risk	NOUN
fcis-5958	116	10	warning	warning	NOUN
fcis-5958	116	11	based	base	VERB
fcis-5958	116	12	on	on	ADP
fcis-5958	116	13	decision	decision	NOUN
fcis-5958	116	14	tree	tree	NOUN
fcis-5958	116	15	and	and	CCONJ
fcis-5958	116	16	random	random	ADJ
fcis-5958	116	17	forest	forest	NOUN
fcis-5958	116	18	model[d	model[d	NOUN
fcis-5958	116	19	]	]	PUNCT
fcis-5958	116	20	.	.	PUNCT
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fcis-5958	117	2	:	:	PUNCT
fcis-5958	117	3	dongbei	dongbei	PROPN
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fcis-5958	117	7	and	and	CCONJ
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fcis-5958	117	9	.	.	PUNCT
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fcis-5958	118	6	y	y	PROPN
fcis-5958	118	7	,	,	PUNCT
fcis-5958	118	8	dian	dian	PROPN
fcis-5958	118	9	y	y	PROPN
fcis-5958	118	10	f	f	PROPN
fcis-5958	118	11	,	,	PUNCT
fcis-5958	118	12	zhang	zhang	PROPN
fcis-5958	118	13	r	r	PROPN
fcis-5958	118	14	f	f	PROPN
fcis-5958	118	15	,	,	PUNCT
fcis-5958	118	16	et	et	PROPN
fcis-5958	118	17	al	al	PROPN
fcis-5958	118	18	.	.	PROPN
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fcis-5958	118	21	)	)	PUNCT
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fcis-5958	118	24	meat	meat	NOUN
fcis-5958	118	25	product	product	NOUN
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fcis-5958	118	27	risk	risk	NOUN
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fcis-5958	118	29	on	on	ADP
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fcis-5958	118	32	machine[j	machine[j	PROPN
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fcis-5958	118	34	.	.	PUNCT
fcis-5958	119	1	computer	computer	NOUN
fcis-5958	119	2	simulation,2019,36(10):413	simulation,2019,36(10):413	PROPN
fcis-5958	119	3	-	-	PUNCT
fcis-5958	119	4	418	418	NUM
fcis-5958	119	5	.	.	PUNCT
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fcis-5958	120	7	,	,	PUNCT
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fcis-5958	120	10	y	y	PROPN
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fcis-5958	120	12	li	li	PROPN
fcis-5958	120	13	j	j	PROPN
fcis-5958	120	14	t	t	PROPN
fcis-5958	120	15	,	,	PUNCT
fcis-5958	120	16	chu	chu	PROPN
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fcis-5958	120	26	prediction	prediction	NOUN
fcis-5958	120	27	model	model	NOUN
fcis-5958	120	28	for	for	ADP
fcis-5958	120	29	food	food	NOUN
fcis-5958	120	30	safety	safety	NOUN
fcis-5958	120	31	based	base	VERB
fcis-5958	120	32	on	on	ADP
fcis-5958	120	33	improved	improve	VERB
fcis-5958	120	34	random	random	ADJ
fcis-5958	120	35	forest	forest	NOUN
fcis-5958	120	36	integrating	integrate	VERB
fcis-5958	120	37	virtual	virtual	ADJ
fcis-5958	120	38	sample[j	sample[j	NOUN
fcis-5958	120	39	]	]	PUNCT
fcis-5958	120	40	.	.	PUNCT
fcis-5958	121	1	engineering	engineering	NOUN
fcis-5958	121	2	applications	application	NOUN
fcis-5958	121	3	of	of	ADP
fcis-5958	121	4	artificial	artificial	ADJ
fcis-5958	121	5	intelligence,2022,116	intelligence,2022,116	NOUN
fcis-5958	121	6	.	.	PUNCT
fcis-5958	122	1	[	[	X
fcis-5958	122	2	19	19	NUM
fcis-5958	122	3	]	]	X
fcis-5958	122	4	ding	ding	PROPN
fcis-5958	122	5	b	b	PROPN
fcis-5958	122	6	x	x	PROPN
fcis-5958	122	7	,	,	PUNCT
fcis-5958	122	8	zhang	zhang	PROPN
fcis-5958	122	9	h	h	PROPN
fcis-5958	122	10	,	,	PUNCT
fcis-5958	122	11	wang	wang	PROPN
fcis-5958	122	12	g.	g.	PROPN
fcis-5958	122	13	(	(	PUNCT
fcis-5958	122	14	2019	2019	NUM
fcis-5958	122	15	)	)	PUNCT
fcis-5958	122	16	research	research	NOUN
fcis-5958	122	17	of	of	ADP
fcis-5958	122	18	network	network	NOUN
fcis-5958	122	19	intrusion	intrusion	NOUN
fcis-5958	122	20	detection	detection	NOUN
fcis-5958	122	21	method	method	NOUN
fcis-5958	122	22	based	base	VERB
fcis-5958	122	23	on	on	ADP
fcis-5958	122	24	mi	mi	PROPN
fcis-5958	122	25	and	and	CCONJ
fcis-5958	122	26	svm[j	svm[j	PROPN
fcis-5958	122	27	]	]	PUNCT
fcis-5958	122	28	.	.	PUNCT
fcis-5958	123	1	journal	journal	PROPN
fcis-5958	123	2	of	of	ADP
fcis-5958	123	3	west	west	PROPN
fcis-5958	123	4	anhui	anhui	PROPN
fcis-5958	123	5	university,2019,35(05):45	university,2019,35(05):45	PROPN
fcis-5958	123	6	-	-	PUNCT
fcis-5958	123	7	49	49	NUM
fcis-5958	123	8	+	+	NOUN
fcis-5958	123	9	63	63	NUM
fcis-5958	123	10	.	.	PUNCT
fcis-5958	124	1	[	[	X
fcis-5958	124	2	20	20	NUM
fcis-5958	124	3	]	]	PUNCT
fcis-5958	124	4	wang	wang	PROPN
fcis-5958	124	5	c	c	PROPN
fcis-5958	124	6	r	r	PROPN
fcis-5958	124	7	,	,	PUNCT
fcis-5958	124	8	hang	hang	NOUN
fcis-5958	124	9	d	d	NOUN
fcis-5958	124	10	m.	m.	NOUN
fcis-5958	124	11	(	(	PUNCT
fcis-5958	124	12	2017	2017	NUM
fcis-5958	124	13	)	)	PUNCT
fcis-5958	124	14	a	a	DET
fcis-5958	124	15	study	study	NOUN
fcis-5958	124	16	on	on	ADP
fcis-5958	124	17	internet	internet	NOUN
fcis-5958	124	18	customer	customer	NOUN
fcis-5958	124	19	churn	churn	NOUN
fcis-5958	124	20	prediction	prediction	NOUN
fcis-5958	124	21	based	base	VERB
fcis-5958	124	22	on	on	ADP
fcis-5958	124	23	social	social	ADJ
fcis-5958	124	24	network	network	NOUN
fcis-5958	124	25	analysis	analysis	NOUN
fcis-5958	124	26	and	and	CCONJ
fcis-5958	124	27	xgboost	xgboost	NOUN
fcis-5958	125	1	[	[	X
fcis-5958	125	2	j	j	X
fcis-5958	125	3	]	]	X
fcis-5958	125	4	.	.	PUNCT
fcis-5958	126	1	cyber	cyber	PROPN
fcis-5958	126	2	security	security	NOUN
fcis-5958	126	3	and	and	CCONJ
fcis-5958	126	4	data	datum	NOUN
fcis-5958	126	5	governance	governance	NOUN
fcis-5958	126	6	,	,	PUNCT
fcis-5958	126	7	2017	2017	NUM
fcis-5958	126	8	,	,	PUNCT
fcis-5958	126	9	36(23):58	36(23):58	NUM
fcis-5958	126	10	-	-	SYM
fcis-5958	126	11	61	61	NUM
fcis-5958	126	12	.	.	PUNCT