id	sid	tid	token	lemma	pos
ijassa-351	1	1	adv	adv	PROPN
ijassa-351	1	2	syst	syst	PROPN
ijassa-351	1	3	sci	sci	PROPN
ijassa-351	1	4	appl	appl	PROPN
ijassa-351	1	5	2016	2016	NUM
ijassa-351	1	6	;	;	PUNCT
ijassa-351	1	7	16(2	16(2	NUM
ijassa-351	1	8	)	)	PUNCT
ijassa-351	1	9	;	;	PUNCT
ijassa-351	1	10	70	70	NUM
ijassa-351	1	11	-	-	SYM
ijassa-351	1	12	80	80	NUM
ijassa-351	1	13	published	publish	VERB
ijassa-351	1	14	online	online	ADV
ijassa-351	1	15	at	at	ADP
ijassa-351	1	16	http://ijassa.ipu.ru/ojs/ijassa/article/view/351	http://ijassa.ipu.ru/ojs/ijassa/article/view/351	PROPN
ijassa-351	1	17	copyright	copyright	NOUN
ijassa-351	1	18	©	©	PROPN
ijassa-351	1	19	2016	2016	NUM
ijassa-351	1	20	assa	assa	NOUN
ijassa-351	1	21	.	.	PUNCT
ijassa-351	2	1	adv	adv	PROPN
ijassa-351	2	2	.	.	PUNCT
ijassa-351	3	1	in	in	ADP
ijassa-351	3	2	systems	system	NOUN
ijassa-351	3	3	science	science	NOUN
ijassa-351	3	4	and	and	CCONJ
ijassa-351	3	5	appl	appl	NOUN
ijassa-351	3	6	.	.	PUNCT
ijassa-351	4	1	(	(	PUNCT
ijassa-351	4	2	2016	2016	NUM
ijassa-351	4	3	)	)	PUNCT
ijassa-351	4	4	improved	improve	VERB
ijassa-351	4	5	prediction	prediction	NOUN
ijassa-351	4	6	of	of	ADP
ijassa-351	4	7	post	post	ADJ
ijassa-351	4	8	-	-	ADJ
ijassa-351	4	9	operative	operative	ADJ
ijassa-351	4	10	life	life	NOUN
ijassa-351	4	11	expectancy	expectancy	NOUN
ijassa-351	4	12	after	after	ADP
ijassa-351	4	13	thoracic	thoracic	NOUN
ijassa-351	4	14	surgery	surgery	NOUN
ijassa-351	4	15	abeer	abeer	PROPN
ijassa-351	4	16	s.	s.	PROPN
ijassa-351	4	17	desuky	desuky	PROPN
ijassa-351	4	18	,	,	PUNCT
ijassa-351	4	19	lamiaa	lamiaa	NOUN
ijassa-351	4	20	m.	m.	PROPN
ijassa-351	4	21	el	el	PROPN
ijassa-351	4	22	bakrawy	bakrawy	PROPN
ijassa-351	4	23	al	al	PROPN
ijassa-351	4	24	-	-	PUNCT
ijassa-351	4	25	azhar	azhar	PROPN
ijassa-351	4	26	university	university	PROPN
ijassa-351	4	27	,	,	PUNCT
ijassa-351	4	28	cairo	cairo	PROPN
ijassa-351	4	29	,	,	PUNCT
ijassa-351	4	30	egypt	egypt	PROPN
ijassa-351	4	31	e	e	PROPN
ijassa-351	4	32	-	-	NOUN
ijassa-351	4	33	mail	mail	NOUN
ijassa-351	4	34	:	:	PUNCT
ijassa-351	4	35	abeerdesuky@yahoo.com	abeerdesuky@yahoo.com	PROPN
ijassa-351	4	36	,	,	PUNCT
ijassa-351	4	37	lamiaabak@yahoo.com	lamiaabak@yahoo.com	X
ijassa-351	5	1	abstract	abstract	ADJ
ijassa-351	5	2	:	:	PUNCT
ijassa-351	5	3	monitoring	monitor	VERB
ijassa-351	5	4	health	health	NOUN
ijassa-351	5	5	outcomes	outcome	NOUN
ijassa-351	5	6	is	be	AUX
ijassa-351	5	7	essential	essential	ADJ
ijassa-351	5	8	to	to	PART
ijassa-351	5	9	enhance	enhance	VERB
ijassa-351	5	10	quality	quality	NOUN
ijassa-351	5	11	initiatives	initiative	NOUN
ijassa-351	5	12	,	,	PUNCT
ijassa-351	5	13	healthcare	healthcare	NOUN
ijassa-351	5	14	management	management	NOUN
ijassa-351	5	15	and	and	CCONJ
ijassa-351	5	16	consumer	consumer	NOUN
ijassa-351	5	17	education	education	NOUN
ijassa-351	5	18	.	.	PUNCT
ijassa-351	6	1	thoracic	thoracic	NOUN
ijassa-351	6	2	surgery	surgery	NOUN
ijassa-351	6	3	is	be	AUX
ijassa-351	6	4	the	the	DET
ijassa-351	6	5	data	datum	NOUN
ijassa-351	6	6	collected	collect	VERB
ijassa-351	6	7	for	for	ADP
ijassa-351	6	8	patients	patient	NOUN
ijassa-351	6	9	who	who	PRON
ijassa-351	6	10	underwent	undergo	VERB
ijassa-351	6	11	major	major	ADJ
ijassa-351	6	12	lung	lung	NOUN
ijassa-351	6	13	resections	resection	NOUN
ijassa-351	6	14	for	for	ADP
ijassa-351	6	15	primary	primary	ADJ
ijassa-351	6	16	lung	lung	NOUN
ijassa-351	6	17	cancer	cancer	NOUN
ijassa-351	6	18	.	.	PUNCT
ijassa-351	7	1	the	the	DET
ijassa-351	7	2	application	application	NOUN
ijassa-351	7	3	of	of	ADP
ijassa-351	7	4	machine	machine	NOUN
ijassa-351	7	5	learning	learn	VERB
ijassa-351	7	6	techniques	technique	NOUN
ijassa-351	7	7	for	for	ADP
ijassa-351	7	8	predicting	predict	VERB
ijassa-351	7	9	post	post	ADJ
ijassa-351	7	10	-	-	ADJ
ijassa-351	7	11	operative	operative	ADJ
ijassa-351	7	12	life	life	NOUN
ijassa-351	7	13	expectancy	expectancy	NOUN
ijassa-351	7	14	in	in	ADP
ijassa-351	7	15	the	the	DET
ijassa-351	7	16	lung	lung	NOUN
ijassa-351	7	17	cancer	cancer	NOUN
ijassa-351	7	18	patients	patient	NOUN
ijassa-351	7	19	is	be	AUX
ijassa-351	7	20	an	an	DET
ijassa-351	7	21	area	area	NOUN
ijassa-351	7	22	with	with	ADP
ijassa-351	7	23	little	little	ADJ
ijassa-351	7	24	research	research	NOUN
ijassa-351	7	25	and	and	CCONJ
ijassa-351	7	26	few	few	ADJ
ijassa-351	7	27	concrete	concrete	ADJ
ijassa-351	7	28	recommendations	recommendation	NOUN
ijassa-351	7	29	.	.	PUNCT
ijassa-351	8	1	in	in	ADP
ijassa-351	8	2	order	order	NOUN
ijassa-351	8	3	to	to	PART
ijassa-351	8	4	use	use	VERB
ijassa-351	8	5	machine	machine	NOUN
ijassa-351	8	6	learning	learn	VERB
ijassa-351	8	7	techniques	technique	NOUN
ijassa-351	8	8	effectively	effectively	ADV
ijassa-351	8	9	,	,	PUNCT
ijassa-351	8	10	attribute	attribute	NOUN
ijassa-351	8	11	ranking	ranking	NOUN
ijassa-351	8	12	and	and	CCONJ
ijassa-351	8	13	selection	selection	NOUN
ijassa-351	8	14	is	be	AUX
ijassa-351	8	15	an	an	DET
ijassa-351	8	16	integral	integral	ADJ
ijassa-351	8	17	component	component	NOUN
ijassa-351	8	18	to	to	ADP
ijassa-351	8	19	successful	successful	ADJ
ijassa-351	8	20	health	health	NOUN
ijassa-351	8	21	outcome	outcome	NOUN
ijassa-351	8	22	prediction	prediction	NOUN
ijassa-351	8	23	.	.	PUNCT
ijassa-351	9	1	in	in	ADP
ijassa-351	9	2	this	this	DET
ijassa-351	9	3	paper	paper	NOUN
ijassa-351	9	4	,	,	PUNCT
ijassa-351	9	5	we	we	PRON
ijassa-351	9	6	present	present	VERB
ijassa-351	9	7	three	three	NUM
ijassa-351	9	8	attribute	attribute	NOUN
ijassa-351	9	9	ranking	ranking	NOUN
ijassa-351	9	10	and	and	CCONJ
ijassa-351	9	11	selection	selection	NOUN
ijassa-351	9	12	methods	method	NOUN
ijassa-351	9	13	to	to	PART
ijassa-351	9	14	improve	improve	VERB
ijassa-351	9	15	algorithms	algorithms	NOUN
ijassa-351	9	16	performance	performance	NOUN
ijassa-351	9	17	for	for	ADP
ijassa-351	9	18	health	health	NOUN
ijassa-351	9	19	outcomes	outcome	NOUN
ijassa-351	9	20	research	research	NOUN
ijassa-351	9	21	.	.	PUNCT
ijassa-351	10	1	two	two	NUM
ijassa-351	10	2	papers	paper	NOUN
ijassa-351	10	3	results	result	VERB
ijassa-351	10	4	for	for	ADP
ijassa-351	10	5	other	other	ADJ
ijassa-351	10	6	researchers	researcher	NOUN
ijassa-351	10	7	are	be	AUX
ijassa-351	10	8	used	use	VERB
ijassa-351	10	9	in	in	ADP
ijassa-351	10	10	comparison	comparison	NOUN
ijassa-351	10	11	to	to	PART
ijassa-351	10	12	show	show	VERB
ijassa-351	10	13	the	the	DET
ijassa-351	10	14	efficiency	efficiency	NOUN
ijassa-351	10	15	of	of	ADP
ijassa-351	10	16	our	our	PRON
ijassa-351	10	17	proposed	propose	VERB
ijassa-351	10	18	attribute	attribute	NOUN
ijassa-351	10	19	ranking	ranking	NOUN
ijassa-351	10	20	and	and	CCONJ
ijassa-351	10	21	selection	selection	NOUN
ijassa-351	10	22	methods	method	NOUN
ijassa-351	10	23	.	.	PUNCT
ijassa-351	11	1	keywords	keyword	NOUN
ijassa-351	11	2	:	:	PUNCT
ijassa-351	11	3	attribute	attribute	NOUN
ijassa-351	11	4	ranking	ranking	NOUN
ijassa-351	11	5	,	,	PUNCT
ijassa-351	11	6	machine	machine	NOUN
ijassa-351	11	7	learning	learning	NOUN
ijassa-351	11	8	,	,	PUNCT
ijassa-351	11	9	prediction	prediction	NOUN
ijassa-351	11	10	,	,	PUNCT
ijassa-351	11	11	thoracic	thoracic	NOUN
ijassa-351	11	12	surgery	surgery	NOUN
ijassa-351	11	13	.	.	PUNCT
ijassa-351	12	1	1	1	X
ijassa-351	12	2	.	.	X
ijassa-351	12	3	introduction	introduction	NOUN
ijassa-351	12	4	integrating	integrate	VERB
ijassa-351	12	5	computer	computer	NOUN
ijassa-351	12	6	applications	application	NOUN
ijassa-351	12	7	into	into	ADP
ijassa-351	12	8	the	the	DET
ijassa-351	12	9	medical	medical	ADJ
ijassa-351	12	10	field	field	NOUN
ijassa-351	12	11	have	have	AUX
ijassa-351	12	12	directly	directly	ADV
ijassa-351	12	13	affected	affect	VERB
ijassa-351	12	14	the	the	DET
ijassa-351	12	15	productivity	productivity	NOUN
ijassa-351	12	16	and	and	CCONJ
ijassa-351	12	17	accuracy	accuracy	NOUN
ijassa-351	12	18	of	of	ADP
ijassa-351	12	19	doctors	doctor	NOUN
ijassa-351	12	20	nowadays	nowadays	ADV
ijassa-351	12	21	.	.	PUNCT
ijassa-351	13	1	measuring	measure	VERB
ijassa-351	13	2	health	health	NOUN
ijassa-351	13	3	outcomes	outcome	NOUN
ijassa-351	13	4	is	be	AUX
ijassa-351	13	5	one	one	NUM
ijassa-351	13	6	of	of	ADP
ijassa-351	13	7	these	these	DET
ijassa-351	13	8	applications	application	NOUN
ijassa-351	13	9	.	.	PUNCT
ijassa-351	14	1	clearly	clearly	ADV
ijassa-351	14	2	,	,	PUNCT
ijassa-351	14	3	there	there	PRON
ijassa-351	14	4	is	be	VERB
ijassa-351	14	5	a	a	DET
ijassa-351	14	6	growing	grow	VERB
ijassa-351	14	7	role	role	NOUN
ijassa-351	14	8	for	for	ADP
ijassa-351	14	9	health	health	NOUN
ijassa-351	14	10	outcomes	outcome	NOUN
ijassa-351	14	11	in	in	ADP
ijassa-351	14	12	the	the	DET
ijassa-351	14	13	purchasing	purchasing	NOUN
ijassa-351	14	14	and	and	CCONJ
ijassa-351	14	15	management	management	NOUN
ijassa-351	14	16	of	of	ADP
ijassa-351	14	17	healthcare	healthcare	PROPN
ijassa-351	14	18	.	.	PUNCT
ijassa-351	15	1	these	these	DET
ijassa-351	15	2	days	day	NOUN
ijassa-351	15	3	cancer	cancer	NOUN
ijassa-351	15	4	is	be	AUX
ijassa-351	15	5	one	one	NUM
ijassa-351	15	6	of	of	ADP
ijassa-351	15	7	the	the	DET
ijassa-351	15	8	major	major	ADJ
ijassa-351	15	9	causes	cause	NOUN
ijassa-351	15	10	of	of	ADP
ijassa-351	15	11	death	death	NOUN
ijassa-351	15	12	in	in	ADP
ijassa-351	15	13	the	the	DET
ijassa-351	15	14	most	most	ADJ
ijassa-351	15	15	countries	country	NOUN
ijassa-351	15	16	.	.	PUNCT
ijassa-351	16	1	currently	currently	ADV
ijassa-351	16	2	,	,	PUNCT
ijassa-351	16	3	lung	lung	NOUN
ijassa-351	16	4	cancer	cancer	NOUN
ijassa-351	16	5	is	be	AUX
ijassa-351	16	6	the	the	DET
ijassa-351	16	7	most	most	ADV
ijassa-351	16	8	frequent	frequent	ADJ
ijassa-351	16	9	augury	augury	NOUN
ijassa-351	16	10	for	for	ADP
ijassa-351	16	11	thoracic	thoracic	NOUN
ijassa-351	16	12	surgery	surgery	NOUN
ijassa-351	16	13	[	[	X
ijassa-351	16	14	1	1	NUM
ijassa-351	16	15	]	]	PUNCT
ijassa-351	16	16	.	.	PUNCT
ijassa-351	17	1	researchers	researcher	NOUN
ijassa-351	17	2	applied	apply	VERB
ijassa-351	17	3	different	different	ADJ
ijassa-351	17	4	strategies	strategy	NOUN
ijassa-351	17	5	,	,	PUNCT
ijassa-351	17	6	such	such	ADJ
ijassa-351	17	7	as	as	ADP
ijassa-351	17	8	examination	examination	NOUN
ijassa-351	17	9	in	in	ADP
ijassa-351	17	10	early	early	ADJ
ijassa-351	17	11	stage	stage	NOUN
ijassa-351	17	12	,	,	PUNCT
ijassa-351	17	13	to	to	PART
ijassa-351	17	14	identify	identify	VERB
ijassa-351	17	15	the	the	DET
ijassa-351	17	16	type	type	NOUN
ijassa-351	17	17	of	of	ADP
ijassa-351	17	18	cancer	cancer	NOUN
ijassa-351	17	19	before	before	ADP
ijassa-351	17	20	the	the	DET
ijassa-351	17	21	emergence	emergence	NOUN
ijassa-351	17	22	of	of	ADP
ijassa-351	17	23	symptoms	symptom	NOUN
ijassa-351	17	24	.	.	PUNCT
ijassa-351	18	1	furthermore	furthermore	ADV
ijassa-351	18	2	,	,	PUNCT
ijassa-351	18	3	new	new	ADJ
ijassa-351	18	4	methods	method	NOUN
ijassa-351	18	5	for	for	ADP
ijassa-351	18	6	the	the	DET
ijassa-351	18	7	early	early	ADJ
ijassa-351	18	8	prediction	prediction	NOUN
ijassa-351	18	9	of	of	ADP
ijassa-351	18	10	cancer	cancer	NOUN
ijassa-351	18	11	therapy	therapy	NOUN
ijassa-351	18	12	outcome	outcome	NOUN
ijassa-351	18	13	have	have	AUX
ijassa-351	18	14	been	be	AUX
ijassa-351	18	15	developed	develop	VERB
ijassa-351	18	16	[	[	PUNCT
ijassa-351	18	17	2].with	2].with	NUM
ijassa-351	18	18	the	the	DET
ijassa-351	18	19	raise	raise	NOUN
ijassa-351	18	20	of	of	ADP
ijassa-351	18	21	new	new	ADJ
ijassa-351	18	22	techniques	technique	NOUN
ijassa-351	18	23	in	in	ADP
ijassa-351	18	24	the	the	DET
ijassa-351	18	25	field	field	NOUN
ijassa-351	18	26	of	of	ADP
ijassa-351	18	27	medicine	medicine	NOUN
ijassa-351	18	28	,	,	PUNCT
ijassa-351	18	29	massive	massive	ADJ
ijassa-351	18	30	datasets	dataset	NOUN
ijassa-351	18	31	of	of	ADP
ijassa-351	18	32	cancer	cancer	NOUN
ijassa-351	18	33	have	have	AUX
ijassa-351	18	34	been	be	AUX
ijassa-351	18	35	collected	collect	VERB
ijassa-351	18	36	and	and	CCONJ
ijassa-351	18	37	now	now	ADV
ijassa-351	18	38	available	available	ADJ
ijassa-351	18	39	to	to	ADP
ijassa-351	18	40	researchers	researcher	NOUN
ijassa-351	18	41	in	in	ADP
ijassa-351	18	42	the	the	DET
ijassa-351	18	43	medical	medical	ADJ
ijassa-351	18	44	field	field	NOUN
ijassa-351	18	45	.	.	PUNCT
ijassa-351	19	1	however	however	ADV
ijassa-351	19	2	,	,	PUNCT
ijassa-351	19	3	the	the	DET
ijassa-351	19	4	most	most	ADV
ijassa-351	19	5	challenging	challenging	ADJ
ijassa-351	19	6	task	task	NOUN
ijassa-351	19	7	is	be	AUX
ijassa-351	19	8	predicting	predict	VERB
ijassa-351	19	9	a	a	DET
ijassa-351	19	10	disease	disease	NOUN
ijassa-351	19	11	outcome	outcome	VERB
ijassa-351	19	12	accurately	accurately	ADV
ijassa-351	19	13	.	.	PUNCT
ijassa-351	20	1	so	so	ADV
ijassa-351	20	2	,	,	PUNCT
ijassa-351	20	3	the	the	DET
ijassa-351	20	4	current	current	ADJ
ijassa-351	20	5	research	research	NOUN
ijassa-351	20	6	efforts	effort	NOUN
ijassa-351	20	7	examine	examine	VERB
ijassa-351	20	8	the	the	DET
ijassa-351	20	9	use	use	NOUN
ijassa-351	20	10	of	of	ADP
ijassa-351	20	11	machine	machine	NOUN
ijassa-351	20	12	learning	learn	VERB
ijassa-351	20	13	techniques	technique	NOUN
ijassa-351	20	14	for	for	ADP
ijassa-351	20	15	discover	discover	VERB
ijassa-351	20	16	and	and	CCONJ
ijassa-351	20	17	identify	identify	VERB
ijassa-351	20	18	models	model	NOUN
ijassa-351	20	19	and	and	CCONJ
ijassa-351	20	20	relationships	relationship	NOUN
ijassa-351	20	21	between	between	ADP
ijassa-351	20	22	them	they	PRON
ijassa-351	20	23	,	,	PUNCT
ijassa-351	20	24	from	from	ADP
ijassa-351	20	25	large	large	ADJ
ijassa-351	20	26	datasets	dataset	NOUN
ijassa-351	20	27	,	,	PUNCT
ijassa-351	20	28	the	the	DET
ijassa-351	20	29	data	data	NOUN
ijassa-351	20	30	is	be	AUX
ijassa-351	20	31	analyzed	analyze	VERB
ijassa-351	20	32	to	to	PART
ijassa-351	20	33	extract	extract	VERB
ijassa-351	20	34	useful	useful	ADJ
ijassa-351	20	35	information	information	NOUN
ijassa-351	20	36	that	that	PRON
ijassa-351	20	37	supports	support	VERB
ijassa-351	20	38	disease	disease	NOUN
ijassa-351	20	39	augury	augury	VERB
ijassa-351	20	40	,	,	PUNCT
ijassa-351	20	41	and	and	CCONJ
ijassa-351	20	42	to	to	PART
ijassa-351	20	43	improve	improve	VERB
ijassa-351	20	44	models	model	NOUN
ijassa-351	20	45	that	that	PRON
ijassa-351	20	46	predict	predict	VERB
ijassa-351	20	47	patient	patient	NOUN
ijassa-351	20	48	’s	’s	PART
ijassa-351	20	49	health	health	NOUN
ijassa-351	20	50	more	more	ADV
ijassa-351	20	51	accurately	accurately	ADV
ijassa-351	20	52	[	[	X
ijassa-351	20	53	3,4	3,4	NUM
ijassa-351	20	54	]	]	PUNCT
ijassa-351	20	55	.	.	PUNCT
ijassa-351	21	1	huge	huge	ADJ
ijassa-351	21	2	datasets	dataset	NOUN
ijassa-351	21	3	usually	usually	ADV
ijassa-351	21	4	lead	lead	VERB
ijassa-351	21	5	to	to	PART
ijassa-351	21	6	crumble	crumble	VERB
ijassa-351	21	7	the	the	DET
ijassa-351	21	8	performance	performance	NOUN
ijassa-351	21	9	and	and	CCONJ
ijassa-351	21	10	accuracy	accuracy	NOUN
ijassa-351	21	11	of	of	ADP
ijassa-351	21	12	the	the	DET
ijassa-351	21	13	machine	machine	NOUN
ijassa-351	21	14	learning	learn	VERB
ijassa-351	21	15	systems	system	NOUN
ijassa-351	21	16	.	.	PUNCT
ijassa-351	22	1	datasets	dataset	NOUN
ijassa-351	22	2	with	with	ADP
ijassa-351	22	3	high	high	ADJ
ijassa-351	22	4	dimensional	dimensional	ADJ
ijassa-351	22	5	attributes	attribute	NOUN
ijassa-351	22	6	have	have	VERB
ijassa-351	22	7	more	more	ADJ
ijassa-351	22	8	processing	processing	NOUN
ijassa-351	22	9	complexity	complexity	NOUN
ijassa-351	22	10	with	with	ADP
ijassa-351	22	11	longer	long	ADJ
ijassa-351	22	12	computational	computational	ADJ
ijassa-351	22	13	time	time	NOUN
ijassa-351	22	14	for	for	ADP
ijassa-351	22	15	prediction	prediction	NOUN
ijassa-351	22	16	.	.	PUNCT
ijassa-351	23	1	attribute	attribute	NOUN
ijassa-351	23	2	ranking	ranking	NOUN
ijassa-351	23	3	and	and	CCONJ
ijassa-351	23	4	selection	selection	NOUN
ijassa-351	23	5	is	be	AUX
ijassa-351	23	6	a	a	DET
ijassa-351	23	7	solution	solution	NOUN
ijassa-351	23	8	to	to	ADP
ijassa-351	23	9	complex	complex	ADJ
ijassa-351	23	10	datasets	dataset	NOUN
ijassa-351	23	11	[	[	X
ijassa-351	23	12	5	5	NUM
ijassa-351	23	13	]	]	PUNCT
ijassa-351	23	14	.	.	PUNCT
ijassa-351	24	1	several	several	ADJ
ijassa-351	24	2	attribute	attribute	NOUN
ijassa-351	24	3	and	and	CCONJ
ijassa-351	24	4	selection	selection	NOUN
ijassa-351	24	5	methods	method	NOUN
ijassa-351	24	6	have	have	AUX
ijassa-351	24	7	been	be	AUX
ijassa-351	24	8	presented	present	VERB
ijassa-351	24	9	in	in	ADP
ijassa-351	24	10	the	the	DET
ijassa-351	24	11	machine	machine	NOUN
ijassa-351	24	12	learning	learning	NOUN
ijassa-351	24	13	domain	domain	NOUN
ijassa-351	24	14	.	.	PUNCT
ijassa-351	25	1	the	the	DET
ijassa-351	25	2	main	main	ADJ
ijassa-351	25	3	aim	aim	NOUN
ijassa-351	25	4	of	of	ADP
ijassa-351	25	5	these	these	DET
ijassa-351	25	6	methods	method	NOUN
ijassa-351	25	7	is	be	AUX
ijassa-351	25	8	to	to	PART
ijassa-351	25	9	remove	remove	VERB
ijassa-351	25	10	attributes	attribute	NOUN
ijassa-351	25	11	that	that	PRON
ijassa-351	25	12	can	can	AUX
ijassa-351	25	13	be	be	AUX
ijassa-351	25	14	irrelevant	irrelevant	ADJ
ijassa-351	25	15	,	,	PUNCT
ijassa-351	25	16	misleading	misleading	ADJ
ijassa-351	25	17	,	,	PUNCT
ijassa-351	25	18	or	or	CCONJ
ijassa-351	25	19	redundant	redundant	ADJ
ijassa-351	25	20	which	which	PRON
ijassa-351	25	21	increase	increase	VERB
ijassa-351	25	22	search	search	NOUN
ijassa-351	25	23	space	space	NOUN
ijassa-351	25	24	size	size	NOUN
ijassa-351	25	25	resulting	result	VERB
ijassa-351	25	26	in	in	ADP
ijassa-351	25	27	difficulty	difficulty	NOUN
ijassa-351	25	28	to	to	PART
ijassa-351	25	29	process	process	VERB
ijassa-351	25	30	data	datum	NOUN
ijassa-351	25	31	further	far	ADV
ijassa-351	25	32	thus	thus	ADV
ijassa-351	25	33	not	not	PART
ijassa-351	25	34	contributing	contribute	VERB
ijassa-351	25	35	to	to	ADP
ijassa-351	25	36	the	the	DET
ijassa-351	25	37	learning	learning	NOUN
ijassa-351	25	38	process	process	NOUN
ijassa-351	25	39	.	.	PUNCT
ijassa-351	26	1	attribute	attribute	NOUN
ijassa-351	26	2	and	and	CCONJ
ijassa-351	26	3	ranking	ranking	NOUN
ijassa-351	26	4	selection	selection	NOUN
ijassa-351	26	5	is	be	AUX
ijassa-351	26	6	the	the	DET
ijassa-351	26	7	process	process	NOUN
ijassa-351	26	8	of	of	ADP
ijassa-351	26	9	choosing	choose	VERB
ijassa-351	26	10	best	good	ADJ
ijassa-351	26	11	attributes	attribute	NOUN
ijassa-351	26	12	from	from	ADP
ijassa-351	26	13	all	all	DET
ijassa-351	26	14	the	the	DET
ijassa-351	26	15	attributes	attribute	NOUN
ijassa-351	26	16	that	that	PRON
ijassa-351	26	17	are	be	AUX
ijassa-351	26	18	useful	useful	ADJ
ijassa-351	26	19	to	to	PART
ijassa-351	26	20	discriminate	discriminate	VERB
ijassa-351	26	21	classes	class	NOUN
ijassa-351	26	22	[	[	X
ijassa-351	26	23	6	6	NUM
ijassa-351	26	24	,	,	PUNCT
ijassa-351	26	25	7	7	NUM
ijassa-351	26	26	]	]	PUNCT
ijassa-351	26	27	.	.	PUNCT
ijassa-351	27	1	mailto:abeerdesuky@yahoo.com	mailto:abeerdesuky@yahoo.com	PROPN
ijassa-351	27	2	mailto:lamiaabak@yahoo.com	mailto:lamiaabak@yahoo.com	X
ijassa-351	28	1	71	71	NUM
ijassa-351	28	2	a.	a.	NOUN
ijassa-351	28	3	s.	s.	PROPN
ijassa-351	28	4	desuky	desuky	PROPN
ijassa-351	28	5	,	,	PUNCT
ijassa-351	28	6	l.m	l.m	PROPN
ijassa-351	28	7	.	.	PROPN
ijassa-351	28	8	el	el	PROPN
ijassa-351	28	9	bakrawy	bakrawy	PROPN
ijassa-351	28	10	copyright	copyright	NOUN
ijassa-351	28	11	©	©	PROPN
ijassa-351	28	12	2016	2016	NUM
ijassa-351	28	13	assa	assa	NOUN
ijassa-351	28	14	.	.	PUNCT
ijassa-351	29	1	adv	adv	PROPN
ijassa-351	29	2	.	.	PUNCT
ijassa-351	30	1	in	in	ADP
ijassa-351	30	2	systems	system	NOUN
ijassa-351	30	3	science	science	NOUN
ijassa-351	30	4	and	and	CCONJ
ijassa-351	30	5	appl	appl	NOUN
ijassa-351	30	6	.	.	PUNCT
ijassa-351	31	1	(	(	PUNCT
ijassa-351	31	2	2016	2016	NUM
ijassa-351	31	3	)	)	PUNCT
ijassa-351	31	4	the	the	DET
ijassa-351	31	5	rest	rest	NOUN
ijassa-351	31	6	of	of	ADP
ijassa-351	31	7	this	this	DET
ijassa-351	31	8	paper	paper	NOUN
ijassa-351	31	9	is	be	AUX
ijassa-351	31	10	organized	organize	VERB
ijassa-351	31	11	as	as	SCONJ
ijassa-351	31	12	follows	follow	VERB
ijassa-351	31	13	:	:	PUNCT
ijassa-351	31	14	brief	brief	ADJ
ijassa-351	31	15	introduction	introduction	NOUN
ijassa-351	31	16	on	on	ADP
ijassa-351	31	17	machine	machine	NOUN
ijassa-351	31	18	learning	learn	VERB
ijassa-351	31	19	algorithms	algorithm	NOUN
ijassa-351	31	20	and	and	CCONJ
ijassa-351	31	21	attribute	attribute	NOUN
ijassa-351	31	22	ranking	ranking	NOUN
ijassa-351	31	23	and	and	CCONJ
ijassa-351	31	24	selection	selection	NOUN
ijassa-351	31	25	methods	method	NOUN
ijassa-351	31	26	have	have	AUX
ijassa-351	31	27	been	be	AUX
ijassa-351	31	28	applied	apply	VERB
ijassa-351	31	29	to	to	ADP
ijassa-351	31	30	disease	disease	NOUN
ijassa-351	31	31	prognosis	prognosis	NOUN
ijassa-351	31	32	and	and	CCONJ
ijassa-351	31	33	prediction	prediction	NOUN
ijassa-351	31	34	are	be	AUX
ijassa-351	31	35	introduced	introduce	VERB
ijassa-351	31	36	in	in	ADP
ijassa-351	31	37	sections	section	NOUN
ijassa-351	31	38	(	(	PUNCT
ijassa-351	31	39	2	2	NUM
ijassa-351	31	40	and	and	CCONJ
ijassa-351	31	41	3	3	NUM
ijassa-351	31	42	)	)	PUNCT
ijassa-351	31	43	.	.	PUNCT
ijassa-351	32	1	the	the	DET
ijassa-351	32	2	details	detail	NOUN
ijassa-351	32	3	of	of	ADP
ijassa-351	32	4	the	the	DET
ijassa-351	32	5	proposed	propose	VERB
ijassa-351	32	6	methods	method	NOUN
ijassa-351	32	7	and	and	CCONJ
ijassa-351	32	8	the	the	DET
ijassa-351	32	9	data	datum	NOUN
ijassa-351	32	10	set	set	VERB
ijassa-351	32	11	are	be	AUX
ijassa-351	32	12	presented	present	VERB
ijassa-351	32	13	in	in	ADP
ijassa-351	32	14	section	section	NOUN
ijassa-351	32	15	(	(	PUNCT
ijassa-351	32	16	4	4	NUM
ijassa-351	32	17	)	)	PUNCT
ijassa-351	32	18	.	.	PUNCT
ijassa-351	33	1	section	section	NOUN
ijassa-351	33	2	(	(	PUNCT
ijassa-351	33	3	5	5	X
ijassa-351	33	4	)	)	PUNCT
ijassa-351	33	5	shows	show	VERB
ijassa-351	33	6	experimental	experimental	ADJ
ijassa-351	33	7	results	result	NOUN
ijassa-351	33	8	.	.	PUNCT
ijassa-351	34	1	conclusions	conclusion	NOUN
ijassa-351	34	2	are	be	AUX
ijassa-351	34	3	discussed	discuss	VERB
ijassa-351	34	4	in	in	ADP
ijassa-351	34	5	section	section	NOUN
ijassa-351	34	6	(	(	PUNCT
ijassa-351	34	7	6	6	NUM
ijassa-351	34	8	)	)	PUNCT
ijassa-351	34	9	.	.	PUNCT
ijassa-351	35	1	2	2	X
ijassa-351	35	2	.	.	X
ijassa-351	35	3	related	relate	VERB
ijassa-351	35	4	work	work	NOUN
ijassa-351	35	5	the	the	DET
ijassa-351	35	6	most	most	ADV
ijassa-351	35	7	major	major	ADJ
ijassa-351	35	8	operation	operation	NOUN
ijassa-351	35	9	that	that	PRON
ijassa-351	35	10	performed	perform	VERB
ijassa-351	35	11	on	on	ADP
ijassa-351	35	12	lung	lung	NOUN
ijassa-351	35	13	cancer	cancer	NOUN
ijassa-351	35	14	patients	patient	NOUN
ijassa-351	35	15	is	be	AUX
ijassa-351	35	16	the	the	DET
ijassa-351	35	17	thoracic	thoracic	NOUN
ijassa-351	35	18	surgery	surgery	NOUN
ijassa-351	35	19	.	.	PUNCT
ijassa-351	36	1	survival	survival	NOUN
ijassa-351	36	2	rate	rate	NOUN
ijassa-351	36	3	is	be	AUX
ijassa-351	36	4	very	very	ADV
ijassa-351	36	5	critical	critical	ADJ
ijassa-351	36	6	factor	factor	NOUN
ijassa-351	36	7	for	for	ADP
ijassa-351	36	8	sawbones	sawbone	NOUN
ijassa-351	36	9	to	to	PART
ijassa-351	36	10	decide	decide	VERB
ijassa-351	36	11	on	on	ADP
ijassa-351	36	12	which	which	PRON
ijassa-351	36	13	patient	patient	ADJ
ijassa-351	36	14	surgery	surgery	NOUN
ijassa-351	36	15	would	would	AUX
ijassa-351	36	16	be	be	AUX
ijassa-351	36	17	performed	perform	VERB
ijassa-351	36	18	.	.	PUNCT
ijassa-351	37	1	selection	selection	NOUN
ijassa-351	37	2	of	of	ADP
ijassa-351	37	3	the	the	DET
ijassa-351	37	4	appropriate	appropriate	ADJ
ijassa-351	37	5	patient	patient	NOUN
ijassa-351	37	6	for	for	ADP
ijassa-351	37	7	surgery	surgery	NOUN
ijassa-351	37	8	is	be	AUX
ijassa-351	37	9	one	one	NUM
ijassa-351	37	10	of	of	ADP
ijassa-351	37	11	the	the	DET
ijassa-351	37	12	common	common	ADJ
ijassa-351	37	13	clinical	clinical	ADJ
ijassa-351	37	14	decision	decision	NOUN
ijassa-351	37	15	challenges	challenge	NOUN
ijassa-351	37	16	in	in	ADP
ijassa-351	37	17	thoracic	thoracic	NOUN
ijassa-351	37	18	surgery	surgery	NOUN
ijassa-351	37	19	,	,	PUNCT
ijassa-351	37	20	bearing	bear	VERB
ijassa-351	37	21	in	in	ADP
ijassa-351	37	22	mind	mind	NOUN
ijassa-351	37	23	risk	risk	NOUN
ijassa-351	37	24	and	and	CCONJ
ijassa-351	37	25	benefits	benefit	NOUN
ijassa-351	37	26	for	for	ADP
ijassa-351	37	27	a	a	DET
ijassa-351	37	28	patient	patient	NOUN
ijassa-351	37	29	,	,	PUNCT
ijassa-351	37	30	both	both	CCONJ
ijassa-351	37	31	in	in	ADP
ijassa-351	37	32	short	short	ADJ
ijassa-351	37	33	-	-	PUNCT
ijassa-351	37	34	term	term	NOUN
ijassa-351	37	35	(	(	PUNCT
ijassa-351	37	36	e.g.	e.g.	ADV
ijassa-351	37	37	post	post	ADJ
ijassa-351	37	38	-	-	ADJ
ijassa-351	37	39	operative	operative	ADJ
ijassa-351	37	40	complications	complication	NOUN
ijassa-351	37	41	,	,	PUNCT
ijassa-351	37	42	including	include	VERB
ijassa-351	37	43	death	death	NOUN
ijassa-351	37	44	-	-	PUNCT
ijassa-351	37	45	rate	rate	NOUN
ijassa-351	37	46	in	in	ADP
ijassa-351	37	47	the	the	DET
ijassa-351	37	48	first	first	ADJ
ijassa-351	37	49	month	month	NOUN
ijassa-351	37	50	)	)	PUNCT
ijassa-351	37	51	and	and	CCONJ
ijassa-351	37	52	long	long	ADJ
ijassa-351	37	53	-	-	PUNCT
ijassa-351	37	54	term	term	NOUN
ijassa-351	37	55	perspective	perspective	NOUN
ijassa-351	37	56	(	(	PUNCT
ijassa-351	37	57	e.g.	e.g.	ADV
ijassa-351	37	58	survival	survival	NOUN
ijassa-351	37	59	for	for	ADP
ijassa-351	37	60	1	1	NUM
ijassa-351	37	61	-	-	SYM
ijassa-351	37	62	5	5	NUM
ijassa-351	37	63	years	year	NOUN
ijassa-351	37	64	)	)	PUNCT
ijassa-351	38	1	[	[	X
ijassa-351	38	2	8	8	NUM
ijassa-351	38	3	]	]	PUNCT
ijassa-351	38	4	.	.	PUNCT
ijassa-351	39	1	a	a	DET
ijassa-351	39	2	variety	variety	NOUN
ijassa-351	39	3	of	of	ADP
ijassa-351	39	4	different	different	ADJ
ijassa-351	39	5	machine	machine	NOUN
ijassa-351	39	6	learning	learn	VERB
ijassa-351	39	7	algorithms	algorithm	NOUN
ijassa-351	39	8	and	and	CCONJ
ijassa-351	39	9	attribute	attribute	NOUN
ijassa-351	39	10	ranking	ranking	NOUN
ijassa-351	39	11	and	and	CCONJ
ijassa-351	39	12	selection	selection	NOUN
ijassa-351	39	13	methods	method	NOUN
ijassa-351	39	14	have	have	AUX
ijassa-351	39	15	been	be	AUX
ijassa-351	39	16	applied	apply	VERB
ijassa-351	39	17	to	to	ADP
ijassa-351	39	18	disease	disease	NOUN
ijassa-351	39	19	prognosis	prognosis	NOUN
ijassa-351	39	20	and	and	CCONJ
ijassa-351	39	21	prediction	prediction	NOUN
ijassa-351	39	22	in	in	ADP
ijassa-351	39	23	the	the	DET
ijassa-351	39	24	last	last	ADJ
ijassa-351	39	25	decades	decade	NOUN
ijassa-351	39	26	.	.	PUNCT
ijassa-351	40	1	a	a	DET
ijassa-351	40	2	comprehensive	comprehensive	ADJ
ijassa-351	40	3	search	search	NOUN
ijassa-351	40	4	was	be	AUX
ijassa-351	40	5	performed	perform	VERB
ijassa-351	40	6	relevant	relevant	ADJ
ijassa-351	40	7	to	to	ADP
ijassa-351	40	8	the	the	DET
ijassa-351	40	9	use	use	NOUN
ijassa-351	40	10	of	of	ADP
ijassa-351	40	11	machine	machine	NOUN
ijassa-351	40	12	learning	learn	VERB
ijassa-351	40	13	algorithms	algorithm	NOUN
ijassa-351	40	14	in	in	ADP
ijassa-351	40	15	cancer	cancer	NOUN
ijassa-351	40	16	receptivity	receptivity	NOUN
ijassa-351	40	17	,	,	PUNCT
ijassa-351	40	18	recurrence	recurrence	NOUN
ijassa-351	40	19	and	and	CCONJ
ijassa-351	40	20	survival	survival	NOUN
ijassa-351	40	21	prediction	prediction	NOUN
ijassa-351	40	22	[	[	X
ijassa-351	40	23	2	2	NUM
ijassa-351	40	24	]	]	PUNCT
ijassa-351	40	25	.	.	PUNCT
ijassa-351	41	1	k.	k.	PROPN
ijassa-351	41	2	kourou	kourou	PROPN
ijassa-351	41	3	et	et	PROPN
ijassa-351	41	4	al	al	PROPN
ijassa-351	41	5	.	.	PUNCT
ijassa-351	42	1	[	[	X
ijassa-351	42	2	2	2	X
ijassa-351	42	3	]	]	PUNCT
ijassa-351	42	4	presented	present	VERB
ijassa-351	42	5	predictive	predictive	ADJ
ijassa-351	42	6	models	model	NOUN
ijassa-351	42	7	based	base	VERB
ijassa-351	42	8	on	on	ADP
ijassa-351	42	9	various	various	ADJ
ijassa-351	42	10	supervised	supervised	ADJ
ijassa-351	42	11	machine	machine	NOUN
ijassa-351	42	12	learning	learn	VERB
ijassa-351	42	13	techniques	technique	NOUN
ijassa-351	42	14	including	include	VERB
ijassa-351	42	15	support	support	NOUN
ijassa-351	42	16	vector	vector	NOUN
ijassa-351	42	17	machines	machine	NOUN
ijassa-351	42	18	,	,	PUNCT
ijassa-351	42	19	bayesian	bayesian	NOUN
ijassa-351	42	20	networks	network	NOUN
ijassa-351	42	21	,	,	PUNCT
ijassa-351	42	22	artificial	artificial	ADJ
ijassa-351	42	23	neural	neural	ADJ
ijassa-351	42	24	networks	network	NOUN
ijassa-351	42	25	,	,	PUNCT
ijassa-351	42	26	and	and	CCONJ
ijassa-351	42	27	decision	decision	NOUN
ijassa-351	42	28	trees	tree	NOUN
ijassa-351	42	29	as	as	ADP
ijassa-351	42	30	an	an	DET
ijassa-351	42	31	aim	aim	NOUN
ijassa-351	42	32	to	to	PART
ijassa-351	42	33	model	model	VERB
ijassa-351	42	34	cancer	cancer	NOUN
ijassa-351	42	35	risk	risk	NOUN
ijassa-351	42	36	or	or	CCONJ
ijassa-351	42	37	patient	patient	ADJ
ijassa-351	42	38	outcomes	outcome	NOUN
ijassa-351	42	39	.	.	PUNCT
ijassa-351	43	1	in	in	ADP
ijassa-351	43	2	their	their	PRON
ijassa-351	43	3	work	work	NOUN
ijassa-351	43	4	,	,	PUNCT
ijassa-351	43	5	maciej	maciej	PROPN
ijassa-351	43	6	zieba	zieba	PROPN
ijassa-351	43	7	et	et	PROPN
ijassa-351	43	8	al.[9	al.[9	PROPN
ijassa-351	43	9	]	]	PUNCT
ijassa-351	43	10	,	,	PUNCT
ijassa-351	43	11	used	use	VERB
ijassa-351	43	12	boosted	boost	VERB
ijassa-351	43	13	svm	svm	NOUN
ijassa-351	43	14	for	for	ADP
ijassa-351	43	15	predicting	predict	VERB
ijassa-351	43	16	post	post	ADJ
ijassa-351	43	17	-	-	ADJ
ijassa-351	43	18	operative	operative	ADJ
ijassa-351	43	19	life	life	NOUN
ijassa-351	43	20	expectancy	expectancy	NOUN
ijassa-351	43	21	.	.	PUNCT
ijassa-351	44	1	in	in	ADP
ijassa-351	44	2	their	their	PRON
ijassa-351	44	3	research	research	NOUN
ijassa-351	44	4	,	,	PUNCT
ijassa-351	44	5	they	they	PRON
ijassa-351	44	6	applied	apply	VERB
ijassa-351	44	7	oracle	oracle	NOUN
ijassa-351	44	8	-	-	PUNCT
ijassa-351	44	9	based	base	VERB
ijassa-351	44	10	approach	approach	NOUN
ijassa-351	44	11	for	for	ADP
ijassa-351	44	12	extracting	extract	VERB
ijassa-351	44	13	decision	decision	NOUN
ijassa-351	44	14	rules	rule	NOUN
ijassa-351	44	15	from	from	ADP
ijassa-351	44	16	the	the	DET
ijassa-351	44	17	boosted	boost	VERB
ijassa-351	44	18	svm	svm	NOUN
ijassa-351	44	19	in	in	ADP
ijassa-351	44	20	order	order	NOUN
ijassa-351	44	21	to	to	PART
ijassa-351	44	22	solve	solve	VERB
ijassa-351	44	23	imbalanced	imbalanced	ADJ
ijassa-351	44	24	data	datum	NOUN
ijassa-351	44	25	problems	problem	NOUN
ijassa-351	44	26	.	.	PUNCT
ijassa-351	45	1	sindhu	sindhu	PROPN
ijassa-351	45	2	et	et	PROPN
ijassa-351	45	3	al	al	PROPN
ijassa-351	45	4	.	.	PUNCT
ijassa-351	46	1	[	[	X
ijassa-351	46	2	1	1	X
ijassa-351	46	3	]	]	PUNCT
ijassa-351	46	4	used	use	VERB
ijassa-351	46	5	six	six	NUM
ijassa-351	46	6	classification	classification	NOUN
ijassa-351	46	7	approaches	approach	NOUN
ijassa-351	46	8	-	-	PUNCT
ijassa-351	46	9	naive	naive	ADJ
ijassa-351	46	10	bayes	bayes	NOUN
ijassa-351	46	11	,	,	PUNCT
ijassa-351	46	12	j48	j48	PROPN
ijassa-351	46	13	,	,	PUNCT
ijassa-351	46	14	part	part	NOUN
ijassa-351	46	15	,	,	PUNCT
ijassa-351	46	16	oner	oner	NOUN
ijassa-351	46	17	,	,	PUNCT
ijassa-351	46	18	decision	decision	NOUN
ijassa-351	46	19	stump	stump	NOUN
ijassa-351	46	20	and	and	CCONJ
ijassa-351	46	21	random	random	ADJ
ijassa-351	46	22	forest	forest	NOUN
ijassa-351	46	23	-	-	PUNCT
ijassa-351	46	24	toanalyse	toanalyse	NOUN
ijassa-351	46	25	thoracic	thoracic	NOUN
ijassa-351	46	26	surgery	surgery	NOUN
ijassa-351	46	27	data	datum	NOUN
ijassa-351	46	28	and	and	CCONJ
ijassa-351	46	29	they	they	PRON
ijassa-351	46	30	found	find	VERB
ijassa-351	46	31	that	that	SCONJ
ijassa-351	46	32	random	random	ADJ
ijassa-351	46	33	forest	forest	NOUN
ijassa-351	46	34	gives	give	VERB
ijassa-351	46	35	the	the	DET
ijassa-351	46	36	best	good	ADJ
ijassa-351	46	37	classification	classification	NOUN
ijassa-351	46	38	accuracy	accuracy	NOUN
ijassa-351	46	39	with	with	ADP
ijassa-351	46	40	all	all	DET
ijassa-351	46	41	split	split	ADJ
ijassa-351	46	42	percentages	percentage	NOUN
ijassa-351	46	43	.	.	PUNCT
ijassa-351	47	1	another	another	DET
ijassa-351	47	2	paper	paper	NOUN
ijassa-351	47	3	[	[	X
ijassa-351	47	4	10	10	NUM
ijassa-351	47	5	]	]	PUNCT
ijassa-351	47	6	have	have	AUX
ijassa-351	47	7	analyzed	analyze	VERB
ijassa-351	47	8	and	and	CCONJ
ijassa-351	47	9	compared	compare	VERB
ijassa-351	47	10	the	the	DET
ijassa-351	47	11	performance	performance	NOUN
ijassa-351	47	12	of	of	ADP
ijassa-351	47	13	four	four	NUM
ijassa-351	47	14	machine	machine	NOUN
ijassa-351	47	15	learning	learn	VERB
ijassa-351	47	16	techniques	technique	NOUN
ijassa-351	47	17	(	(	PUNCT
ijassa-351	47	18	naïve	naïve	ADJ
ijassa-351	47	19	bayes	bayes	NOUN
ijassa-351	47	20	,	,	PUNCT
ijassa-351	47	21	simple	simple	ADJ
ijassa-351	47	22	logistic	logistic	ADJ
ijassa-351	47	23	regression	regression	NOUN
ijassa-351	47	24	,	,	PUNCT
ijassa-351	47	25	multilayer	multilayer	PROPN
ijassa-351	47	26	perceptron	perceptron	PROPN
ijassa-351	47	27	and	and	CCONJ
ijassa-351	47	28	j48	j48	PROPN
ijassa-351	47	29	)	)	PUNCT
ijassa-351	47	30	with	with	ADP
ijassa-351	47	31	their	their	PRON
ijassa-351	47	32	boosted	boost	VERB
ijassa-351	47	33	versions	version	NOUN
ijassa-351	47	34	by	by	ADP
ijassa-351	47	35	different	different	ADJ
ijassa-351	47	36	metrics	metric	NOUN
ijassa-351	47	37	.	.	PUNCT
ijassa-351	48	1	their	their	PRON
ijassa-351	48	2	results	result	NOUN
ijassa-351	48	3	indicate	indicate	VERB
ijassa-351	48	4	that	that	SCONJ
ijassa-351	48	5	boosted	boost	VERB
ijassa-351	48	6	simple	simple	ADJ
ijassa-351	48	7	logistic	logistic	ADJ
ijassa-351	48	8	regression	regression	NOUN
ijassa-351	48	9	technique	technique	NOUN
ijassa-351	48	10	is	be	AUX
ijassa-351	48	11	generally	generally	ADV
ijassa-351	48	12	better	well	ADJ
ijassa-351	48	13	or	or	CCONJ
ijassa-351	48	14	at	at	ADP
ijassa-351	48	15	least	least	ADJ
ijassa-351	48	16	competitive	competitive	ADJ
ijassa-351	48	17	against	against	ADP
ijassa-351	48	18	the	the	DET
ijassa-351	48	19	rest	rest	NOUN
ijassa-351	48	20	of	of	ADP
ijassa-351	48	21	four	four	NUM
ijassa-351	48	22	machine	machine	NOUN
ijassa-351	48	23	learning	learn	VERB
ijassa-351	48	24	techniques	technique	NOUN
ijassa-351	48	25	with	with	ADP
ijassa-351	48	26	84.53	84.53	NUM
ijassa-351	48	27	%	%	NOUN
ijassa-351	48	28	prediction	prediction	NOUN
ijassa-351	48	29	accuracy	accuracy	NOUN
ijassa-351	48	30	.	.	PUNCT
ijassa-351	49	1	3	3	X
ijassa-351	49	2	.	.	X
ijassa-351	49	3	machine	machine	NOUN
ijassa-351	49	4	learning	learn	VERB
ijassa-351	49	5	machine	machine	NOUN
ijassa-351	49	6	learning	learning	NOUN
ijassa-351	49	7	is	be	AUX
ijassa-351	49	8	a	a	DET
ijassa-351	49	9	branch	branch	NOUN
ijassa-351	49	10	of	of	ADP
ijassa-351	49	11	artificial	artificial	ADJ
ijassa-351	49	12	intelligence	intelligence	NOUN
ijassa-351	49	13	which	which	PRON
ijassa-351	49	14	utilizes	utilize	VERB
ijassa-351	49	15	statistical	statistical	ADJ
ijassa-351	49	16	,	,	PUNCT
ijassa-351	49	17	optimization	optimization	NOUN
ijassa-351	49	18	and	and	CCONJ
ijassa-351	49	19	probabilistic	probabilistic	ADJ
ijassa-351	49	20	techniques	technique	NOUN
ijassa-351	49	21	that	that	PRON
ijassa-351	49	22	allows	allow	VERB
ijassa-351	49	23	computers	computer	NOUN
ijassa-351	49	24	to	to	PART
ijassa-351	49	25	“	"	PUNCT
ijassa-351	49	26	learn	learn	VERB
ijassa-351	49	27	”	"	PUNCT
ijassa-351	49	28	from	from	ADP
ijassa-351	49	29	past	past	ADJ
ijassa-351	49	30	examples	example	NOUN
ijassa-351	49	31	and	and	CCONJ
ijassa-351	49	32	to	to	PART
ijassa-351	49	33	detect	detect	VERB
ijassa-351	49	34	hard	hard	ADJ
ijassa-351	49	35	-	-	PUNCT
ijassa-351	49	36	to	to	ADP
ijassa-351	49	37	-	-	PUNCT
ijassa-351	49	38	discern	discern	ADJ
ijassa-351	49	39	patterns	pattern	NOUN
ijassa-351	49	40	from	from	ADP
ijassa-351	49	41	large	large	ADJ
ijassa-351	49	42	,	,	PUNCT
ijassa-351	49	43	noisy	noisy	ADJ
ijassa-351	49	44	or	or	CCONJ
ijassa-351	49	45	complex	complex	ADJ
ijassa-351	49	46	data	datum	NOUN
ijassa-351	49	47	sets	set	NOUN
ijassa-351	49	48	.	.	PUNCT
ijassa-351	50	1	these	these	DET
ijassa-351	50	2	techniques	technique	NOUN
ijassa-351	50	3	have	have	AUX
ijassa-351	50	4	become	become	VERB
ijassa-351	50	5	a	a	DET
ijassa-351	50	6	popular	popular	ADJ
ijassa-351	50	7	tool	tool	NOUN
ijassa-351	50	8	in	in	ADP
ijassa-351	50	9	medical	medical	ADJ
ijassa-351	50	10	diagnosis	diagnosis	NOUN
ijassa-351	50	11	,	,	PUNCT
ijassa-351	50	12	which	which	PRON
ijassa-351	50	13	can	can	AUX
ijassa-351	50	14	find	find	VERB
ijassa-351	50	15	and	and	CCONJ
ijassa-351	50	16	identify	identify	VERB
ijassa-351	50	17	models	model	NOUN
ijassa-351	50	18	and	and	CCONJ
ijassa-351	50	19	relationships	relationship	NOUN
ijassa-351	50	20	between	between	ADP
ijassa-351	50	21	them	they	PRON
ijassa-351	50	22	from	from	ADP
ijassa-351	50	23	large	large	ADJ
ijassa-351	50	24	,	,	PUNCT
ijassa-351	50	25	noisy	noisy	ADJ
ijassa-351	50	26	or	or	CCONJ
ijassa-351	50	27	complex	complex	ADJ
ijassa-351	50	28	datasets[3	datasets[3	NOUN
ijassa-351	50	29	]	]	PUNCT
ijassa-351	50	30	.	.	PUNCT
ijassa-351	51	1	the	the	DET
ijassa-351	51	2	inputs	input	NOUN
ijassa-351	51	3	are	be	AUX
ijassa-351	51	4	the	the	DET
ijassa-351	51	5	information	information	NOUN
ijassa-351	51	6	about	about	ADP
ijassa-351	51	7	the	the	DET
ijassa-351	51	8	patient	patient	NOUN
ijassa-351	51	9	's	's	PART
ijassa-351	51	10	age	age	NOUN
ijassa-351	51	11	,	,	PUNCT
ijassa-351	51	12	gender	gender	NOUN
ijassa-351	51	13	,	,	PUNCT
ijassa-351	51	14	past	past	ADJ
ijassa-351	51	15	medical	medical	ADJ
ijassa-351	51	16	history	history	NOUN
ijassa-351	51	17	,	,	PUNCT
ijassa-351	51	18	past	past	ADP
ijassa-351	51	19	medical	medical	ADJ
ijassa-351	51	20	procedures	procedure	NOUN
ijassa-351	51	21	,	,	PUNCT
ijassa-351	51	22	family	family	NOUN
ijassa-351	51	23	medical	medical	ADJ
ijassa-351	51	24	history	history	NOUN
ijassa-351	51	25	and	and	CCONJ
ijassa-351	51	26	current	current	ADJ
ijassa-351	51	27	symptoms	symptom	NOUN
ijassa-351	51	28	,	,	PUNCT
ijassa-351	51	29	while	while	SCONJ
ijassa-351	51	30	labels	label	NOUN
ijassa-351	51	31	are	be	AUX
ijassa-351	51	32	the	the	DET
ijassa-351	51	33	illnesses	illness	NOUN
ijassa-351	51	34	.	.	PUNCT
ijassa-351	52	1	in	in	ADP
ijassa-351	52	2	some	some	DET
ijassa-351	52	3	cases	case	NOUN
ijassa-351	52	4	,	,	PUNCT
ijassa-351	52	5	these	these	DET
ijassa-351	52	6	inputs	input	NOUN
ijassa-351	52	7	are	be	AUX
ijassa-351	52	8	missed	miss	VERB
ijassa-351	52	9	because	because	SCONJ
ijassa-351	52	10	some	some	DET
ijassa-351	52	11	tests	test	NOUN
ijassa-351	52	12	have	have	AUX
ijassa-351	52	13	n't	not	PART
ijassa-351	52	14	been	be	AUX
ijassa-351	52	15	applied	apply	VERB
ijassa-351	52	16	to	to	ADP
ijassa-351	52	17	the	the	DET
ijassa-351	52	18	patient	patient	NOUN
ijassa-351	52	19	,	,	PUNCT
ijassa-351	52	20	so	so	ADV
ijassa-351	52	21	we	we	PRON
ijassa-351	52	22	do	do	AUX
ijassa-351	52	23	not	not	PART
ijassa-351	52	24	apply	apply	VERB
ijassa-351	52	25	machine	machine	NOUN
ijassa-351	52	26	learning	learn	VERB
ijassa-351	52	27	techniques	technique	NOUN
ijassa-351	52	28	unless	unless	SCONJ
ijassa-351	52	29	we	we	PRON
ijassa-351	52	30	confirm	confirm	VERB
ijassa-351	52	31	that	that	SCONJ
ijassa-351	52	32	the	the	DET
ijassa-351	52	33	patient	patient	NOUN
ijassa-351	52	34	will	will	AUX
ijassa-351	52	35	give	give	VERB
ijassa-351	52	36	us	we	PRON
ijassa-351	52	37	valuable	valuable	ADJ
ijassa-351	52	38	information	information	NOUN
ijassa-351	52	39	.	.	PUNCT
ijassa-351	53	1	if	if	SCONJ
ijassa-351	53	2	the	the	DET
ijassa-351	53	3	medical	medical	ADJ
ijassa-351	53	4	diagnosis	diagnosis	NOUN
ijassa-351	53	5	is	be	AUX
ijassa-351	53	6	wrong	wrong	ADJ
ijassa-351	53	7	,	,	PUNCT
ijassa-351	53	8	decision	decision	NOUN
ijassa-351	53	9	may	may	AUX
ijassa-351	53	10	lead	lead	VERB
ijassa-351	53	11	to	to	ADP
ijassa-351	53	12	a	a	DET
ijassa-351	53	13	wrong	wrong	NOUN
ijassa-351	53	14	or	or	CCONJ
ijassa-351	53	15	no	no	DET
ijassa-351	53	16	treatment	treatment	NOUN
ijassa-351	53	17	,	,	PUNCT
ijassa-351	53	18	so	so	ADV
ijassa-351	53	19	machine	machine	NOUN
ijassa-351	53	20	learning	learning	NOUN
ijassa-351	53	21	is	be	AUX
ijassa-351	53	22	extremely	extremely	ADV
ijassa-351	53	23	used	used	ADJ
ijassa-351	53	24	to	to	PART
ijassa-351	53	25	diagnose	diagnose	VERB
ijassa-351	53	26	and	and	CCONJ
ijassa-351	53	27	detect	detect	VERB
ijassa-351	53	28	cancer	cancer	NOUN
ijassa-351	53	29	[	[	X
ijassa-351	53	30	4	4	NUM
ijassa-351	53	31	]	]	PUNCT
ijassa-351	53	32	.	.	PUNCT
ijassa-351	54	1	more	more	ADV
ijassa-351	54	2	recently	recently	ADV
ijassa-351	54	3	,	,	PUNCT
ijassa-351	54	4	it	it	PRON
ijassa-351	54	5	has	have	AUX
ijassa-351	54	6	been	be	AUX
ijassa-351	54	7	widely	widely	ADV
ijassa-351	54	8	applied	apply	VERB
ijassa-351	54	9	in	in	ADP
ijassa-351	54	10	the	the	DET
ijassa-351	54	11	field	field	NOUN
ijassa-351	54	12	of	of	ADP
ijassa-351	54	13	cancer	cancer	NOUN
ijassa-351	54	14	prediction	prediction	NOUN
ijassa-351	54	15	and	and	CCONJ
ijassa-351	54	16	prognosis	prognosis	NOUN
ijassa-351	54	17	which	which	PRON
ijassa-351	54	18	are	be	AUX
ijassa-351	54	19	differ	differ	VERB
ijassa-351	54	20	from	from	ADP
ijassa-351	54	21	cancer	cancer	NOUN
ijassa-351	54	22	detection	detection	NOUN
ijassa-351	54	23	and	and	CCONJ
ijassa-351	54	24	diagnosis	diagnosis	NOUN
ijassa-351	54	25	.	.	PUNCT
ijassa-351	55	1	there	there	PRON
ijassa-351	55	2	are	be	VERB
ijassa-351	55	3	three	three	NUM
ijassa-351	55	4	types	type	NOUN
ijassa-351	55	5	of	of	ADP
ijassa-351	55	6	cancer	cancer	NOUN
ijassa-351	55	7	prediction	prediction	NOUN
ijassa-351	55	8	and	and	CCONJ
ijassa-351	55	9	prognosis	prognosis	NOUN
ijassa-351	55	10	:	:	PUNCT
ijassa-351	55	11	one	one	NUM
ijassa-351	55	12	of	of	ADP
ijassa-351	55	13	them	they	PRON
ijassa-351	55	14	is	be	AUX
ijassa-351	55	15	prediction	prediction	NOUN
ijassa-351	55	16	of	of	ADP
ijassa-351	55	17	cancer	cancer	NOUN
ijassa-351	55	18	receptivity	receptivity	NOUN
ijassa-351	55	19	.	.	PUNCT
ijassa-351	56	1	in	in	ADP
ijassa-351	56	2	this	this	DET
ijassa-351	56	3	type	type	NOUN
ijassa-351	56	4	,	,	PUNCT
ijassa-351	56	5	one	one	PRON
ijassa-351	56	6	is	be	AUX
ijassa-351	56	7	trying	try	VERB
ijassa-351	56	8	to	to	PART
ijassa-351	56	9	predict	predict	VERB
ijassa-351	56	10	the	the	DET
ijassa-351	56	11	probability	probability	NOUN
ijassa-351	56	12	of	of	ADP
ijassa-351	56	13	cancer	cancer	NOUN
ijassa-351	56	14	progression	progression	NOUN
ijassa-351	56	15	before	before	ADP
ijassa-351	56	16	occurrence	occurrence	NOUN
ijassa-351	56	17	of	of	ADP
ijassa-351	56	18	the	the	DET
ijassa-351	56	19	disease	disease	NOUN
ijassa-351	56	20	.	.	PUNCT
ijassa-351	57	1	second	second	ADJ
ijassa-351	57	2	type	type	NOUN
ijassa-351	57	3	is	be	AUX
ijassa-351	57	4	the	the	DET
ijassa-351	57	5	prediction	prediction	NOUN
ijassa-351	57	6	of	of	ADP
ijassa-351	57	7	cancer	cancer	NOUN
ijassa-351	57	8	recurrence	recurrence	NOUN
ijassa-351	57	9	by	by	ADP
ijassa-351	57	10	trying	try	VERB
ijassa-351	57	11	to	to	PART
ijassa-351	57	12	predict	predict	VERB
ijassa-351	57	13	the	the	DET
ijassa-351	57	14	probability	probability	NOUN
ijassa-351	57	15	of	of	ADP
ijassa-351	57	16	redeveloping	redeveloping	NOUN
ijassa-351	57	17	cancer	cancer	NOUN
ijassa-351	57	18	after	after	ADP
ijassa-351	57	19	treatment	treatment	NOUN
ijassa-351	57	20	and	and	CCONJ
ijassa-351	57	21	after	after	ADP
ijassa-351	57	22	a	a	DET
ijassa-351	57	23	period	period	NOUN
ijassa-351	57	24	of	of	ADP
ijassa-351	57	25	time	time	NOUN
ijassa-351	57	26	during	during	ADP
ijassa-351	57	27	which	which	PRON
ijassa-351	57	28	the	the	DET
ijassa-351	57	29	cancer	cancer	NOUN
ijassa-351	57	30	can	can	AUX
ijassa-351	57	31	not	not	PART
ijassa-351	57	32	improved	improve	VERB
ijassa-351	57	33	prediction	prediction	NOUN
ijassa-351	57	34	of	of	ADP
ijassa-351	57	35	post	post	ADJ
ijassa-351	57	36	-	-	ADJ
ijassa-351	57	37	operative	operative	ADJ
ijassa-351	57	38	life	life	NOUN
ijassa-351	57	39	expectancy	expectancy	NOUN
ijassa-351	57	40	after	after	ADP
ijassa-351	57	41	thoracic	thoracic	NOUN
ijassa-351	57	42	surgery	surgery	NOUN
ijassa-351	57	43	72	72	NUM
ijassa-351	57	44	copyright	copyright	NOUN
ijassa-351	57	45	©	©	PROPN
ijassa-351	57	46	2016	2016	NUM
ijassa-351	57	47	assa	assa	NOUN
ijassa-351	57	48	.	.	PUNCT
ijassa-351	58	1	adv	adv	PROPN
ijassa-351	58	2	.	.	PUNCT
ijassa-351	59	1	in	in	ADP
ijassa-351	59	2	systems	system	NOUN
ijassa-351	59	3	science	science	NOUN
ijassa-351	59	4	and	and	CCONJ
ijassa-351	59	5	appl	appl	NOUN
ijassa-351	59	6	.	.	PUNCT
ijassa-351	60	1	(	(	PUNCT
ijassa-351	60	2	2016	2016	NUM
ijassa-351	60	3	)	)	PUNCT
ijassa-351	60	4	be	be	AUX
ijassa-351	60	5	detected	detect	VERB
ijassa-351	60	6	.	.	PUNCT
ijassa-351	61	1	third	third	ADJ
ijassa-351	61	2	type	type	NOUN
ijassa-351	61	3	is	be	AUX
ijassa-351	61	4	the	the	DET
ijassa-351	61	5	prediction	prediction	NOUN
ijassa-351	61	6	of	of	ADP
ijassa-351	61	7	cancer	cancer	NOUN
ijassa-351	61	8	survivability	survivability	NOUN
ijassa-351	61	9	by	by	ADP
ijassa-351	61	10	trying	try	VERB
ijassa-351	61	11	to	to	PART
ijassa-351	61	12	predict	predict	VERB
ijassa-351	61	13	an	an	DET
ijassa-351	61	14	outcome	outcome	NOUN
ijassa-351	61	15	which	which	PRON
ijassa-351	61	16	usually	usually	ADV
ijassa-351	61	17	refers	refer	VERB
ijassa-351	61	18	to	to	ADP
ijassa-351	61	19	life	life	NOUN
ijassa-351	61	20	expectancy	expectancy	NOUN
ijassa-351	61	21	,	,	PUNCT
ijassa-351	61	22	survivability	survivability	NOUN
ijassa-351	61	23	,	,	PUNCT
ijassa-351	61	24	progression	progression	NOUN
ijassa-351	61	25	and	and	CCONJ
ijassa-351	61	26	tumor	tumor	NOUN
ijassa-351	61	27	-	-	PUNCT
ijassa-351	61	28	drug	drug	NOUN
ijassa-351	61	29	sensitivity	sensitivity	NOUN
ijassa-351	61	30	.	.	PUNCT
ijassa-351	62	1	these	these	DET
ijassa-351	62	2	days	day	NOUN
ijassa-351	62	3	,	,	PUNCT
ijassa-351	62	4	different	different	ADJ
ijassa-351	62	5	types	type	NOUN
ijassa-351	62	6	of	of	ADP
ijassa-351	62	7	cancer	cancer	NOUN
ijassa-351	62	8	such	such	ADJ
ijassa-351	62	9	as	as	ADP
ijassa-351	62	10	prostate	prostate	NOUN
ijassa-351	62	11	,	,	PUNCT
ijassa-351	62	12	brain	brain	NOUN
ijassa-351	62	13	,	,	PUNCT
ijassa-351	62	14	cervical	cervical	ADJ
ijassa-351	62	15	,	,	PUNCT
ijassa-351	62	16	esophageal	esophageal	ADJ
ijassa-351	62	17	,	,	PUNCT
ijassa-351	62	18	leukemia	leukemia	NOUN
ijassa-351	62	19	,	,	PUNCT
ijassa-351	62	20	head	head	NOUN
ijassa-351	62	21	,	,	PUNCT
ijassa-351	62	22	neck	neck	NOUN
ijassa-351	62	23	,	,	PUNCT
ijassa-351	62	24	breast	breast	NOUN
ijassa-351	62	25	,	,	PUNCT
ijassa-351	62	26	and	and	CCONJ
ijassa-351	62	27	thoracic	thoracic	NOUN
ijassa-351	62	28	are	be	AUX
ijassa-351	62	29	appear	appear	VERB
ijassa-351	62	30	to	to	PART
ijassa-351	62	31	be	be	AUX
ijassa-351	62	32	compatible	compatible	ADJ
ijassa-351	62	33	with	with	ADP
ijassa-351	62	34	machine	machine	NOUN
ijassa-351	62	35	learning	learn	VERB
ijassa-351	62	36	prediction	prediction	NOUN
ijassa-351	62	37	.	.	PUNCT
ijassa-351	63	1	the	the	DET
ijassa-351	63	2	thoracic	thoracic	NOUN
ijassa-351	63	3	datasets	dataset	NOUN
ijassa-351	63	4	is	be	AUX
ijassa-351	63	5	concerned	concern	VERB
ijassa-351	63	6	with	with	ADP
ijassa-351	63	7	classification	classification	NOUN
ijassa-351	63	8	problem	problem	NOUN
ijassa-351	63	9	related	relate	VERB
ijassa-351	63	10	to	to	ADP
ijassa-351	63	11	the	the	DET
ijassa-351	63	12	post	post	ADJ
ijassa-351	63	13	-	-	ADJ
ijassa-351	63	14	operative	operative	ADJ
ijassa-351	63	15	life	life	NOUN
ijassa-351	63	16	expectancy	expectancy	NOUN
ijassa-351	63	17	in	in	ADP
ijassa-351	63	18	the	the	DET
ijassa-351	63	19	lung	lung	NOUN
ijassa-351	63	20	cancer	cancer	NOUN
ijassa-351	63	21	patients[2,3,4	patients[2,3,4	NOUN
ijassa-351	63	22	]	]	PUNCT
ijassa-351	63	23	in	in	ADP
ijassa-351	63	24	order	order	NOUN
ijassa-351	63	25	to	to	PART
ijassa-351	63	26	improve	improve	VERB
ijassa-351	63	27	machine	machine	NOUN
ijassa-351	63	28	learning	learn	VERB
ijassa-351	63	29	techniques	technique	NOUN
ijassa-351	63	30	when	when	SCONJ
ijassa-351	63	31	the	the	DET
ijassa-351	63	32	datasets	dataset	NOUN
ijassa-351	63	33	have	have	VERB
ijassa-351	63	34	a	a	DET
ijassa-351	63	35	large	large	ADJ
ijassa-351	63	36	number	number	NOUN
ijassa-351	63	37	of	of	ADP
ijassa-351	63	38	features	feature	NOUN
ijassa-351	63	39	or	or	CCONJ
ijassa-351	63	40	attributes	attribute	NOUN
ijassa-351	63	41	,	,	PUNCT
ijassa-351	63	42	attribute	attribute	NOUN
ijassa-351	63	43	ranking	ranking	NOUN
ijassa-351	63	44	and	and	CCONJ
ijassa-351	63	45	selection	selection	NOUN
ijassa-351	63	46	is	be	AUX
ijassa-351	63	47	used	use	VERB
ijassa-351	63	48	to	to	PART
ijassa-351	63	49	identify	identify	VERB
ijassa-351	63	50	the	the	DET
ijassa-351	63	51	most	most	ADV
ijassa-351	63	52	relevant	relevant	ADJ
ijassa-351	63	53	attributes	attribute	NOUN
ijassa-351	63	54	and	and	CCONJ
ijassa-351	63	55	remove	remove	VERB
ijassa-351	63	56	the	the	DET
ijassa-351	63	57	redundant	redundant	ADJ
ijassa-351	63	58	and	and	CCONJ
ijassa-351	63	59	irrelevant	irrelevant	ADJ
ijassa-351	63	60	attributes	attribute	NOUN
ijassa-351	63	61	from	from	ADP
ijassa-351	63	62	the	the	DET
ijassa-351	63	63	dataset	dataset	NOUN
ijassa-351	63	64	.	.	PUNCT
ijassa-351	64	1	attribute	attribute	NOUN
ijassa-351	64	2	ranking	ranking	NOUN
ijassa-351	64	3	and	and	CCONJ
ijassa-351	64	4	selection	selection	NOUN
ijassa-351	64	5	algorithms	algorithm	NOUN
ijassa-351	64	6	can	can	AUX
ijassa-351	64	7	be	be	AUX
ijassa-351	64	8	divided	divide	VERB
ijassa-351	64	9	into	into	ADP
ijassa-351	64	10	wrapper	wrapper	NOUN
ijassa-351	64	11	and	and	CCONJ
ijassa-351	64	12	filter	filter	NOUN
ijassa-351	64	13	methods	method	NOUN
ijassa-351	64	14	.	.	PUNCT
ijassa-351	65	1	the	the	DET
ijassa-351	65	2	wrapper	wrapper	NOUN
ijassa-351	65	3	methods	method	NOUN
ijassa-351	65	4	select	select	VERB
ijassa-351	65	5	attributes	attribute	NOUN
ijassa-351	65	6	based	base	VERB
ijassa-351	65	7	on	on	ADP
ijassa-351	65	8	an	an	DET
ijassa-351	65	9	estimation	estimation	NOUN
ijassa-351	65	10	of	of	ADP
ijassa-351	65	11	the	the	DET
ijassa-351	65	12	accuracy	accuracy	NOUN
ijassa-351	65	13	according	accord	VERB
ijassa-351	65	14	to	to	ADP
ijassa-351	65	15	target	target	NOUN
ijassa-351	65	16	learning	learning	NOUN
ijassa-351	65	17	algorithm	algorithm	NOUN
ijassa-351	65	18	.	.	PUNCT
ijassa-351	66	1	after	after	ADP
ijassa-351	66	2	applying	apply	VERB
ijassa-351	66	3	the	the	DET
ijassa-351	66	4	learning	learning	NOUN
ijassa-351	66	5	algorithm	algorithm	NOUN
ijassa-351	66	6	,	,	PUNCT
ijassa-351	66	7	wrapper	wrapper	NOUN
ijassa-351	66	8	searches	search	VERB
ijassa-351	66	9	the	the	DET
ijassa-351	66	10	feature	feature	NOUN
ijassa-351	66	11	space	space	NOUN
ijassa-351	66	12	by	by	ADP
ijassa-351	66	13	removing	remove	VERB
ijassa-351	66	14	some	some	DET
ijassa-351	66	15	attributes	attribute	NOUN
ijassa-351	66	16	and	and	CCONJ
ijassa-351	66	17	testing	test	VERB
ijassa-351	66	18	the	the	DET
ijassa-351	66	19	effectiveness	effectiveness	NOUN
ijassa-351	66	20	of	of	ADP
ijassa-351	66	21	attribute	attribute	NOUN
ijassa-351	66	22	removing	remove	VERB
ijassa-351	66	23	on	on	ADP
ijassa-351	66	24	the	the	DET
ijassa-351	66	25	prediction	prediction	NOUN
ijassa-351	66	26	metrics	metric	NOUN
ijassa-351	66	27	.	.	PUNCT
ijassa-351	67	1	the	the	DET
ijassa-351	67	2	attribute	attribute	NOUN
ijassa-351	67	3	which	which	PRON
ijassa-351	67	4	make	make	VERB
ijassa-351	67	5	important	important	ADJ
ijassa-351	67	6	difference	difference	NOUN
ijassa-351	67	7	in	in	ADP
ijassa-351	67	8	learning	learning	NOUN
ijassa-351	67	9	process	process	NOUN
ijassa-351	67	10	should	should	AUX
ijassa-351	67	11	be	be	AUX
ijassa-351	67	12	selected	select	VERB
ijassa-351	67	13	as	as	ADP
ijassa-351	67	14	high	high	ADJ
ijassa-351	67	15	quality	quality	NOUN
ijassa-351	67	16	attribute	attribute	NOUN
ijassa-351	67	17	,	,	PUNCT
ijassa-351	67	18	while	while	SCONJ
ijassa-351	67	19	filters	filter	NOUN
ijassa-351	67	20	methods	method	NOUN
ijassa-351	67	21	estimate	estimate	VERB
ijassa-351	67	22	the	the	DET
ijassa-351	67	23	quality	quality	NOUN
ijassa-351	67	24	of	of	ADP
ijassa-351	67	25	selected	select	VERB
ijassa-351	67	26	attributes	attribute	NOUN
ijassa-351	67	27	independently	independently	ADV
ijassa-351	67	28	from	from	ADP
ijassa-351	67	29	the	the	DET
ijassa-351	67	30	learning	learning	NOUN
ijassa-351	67	31	algorithm	algorithm	NOUN
ijassa-351	67	32	.	.	PUNCT
ijassa-351	68	1	it	it	PRON
ijassa-351	68	2	depends	depend	VERB
ijassa-351	68	3	on	on	ADP
ijassa-351	68	4	the	the	DET
ijassa-351	68	5	statistical	statistical	ADJ
ijassa-351	68	6	correlation	correlation	NOUN
ijassa-351	68	7	between	between	ADP
ijassa-351	68	8	the	the	DET
ijassa-351	68	9	set	set	NOUN
ijassa-351	68	10	of	of	ADP
ijassa-351	68	11	attributes	attribute	NOUN
ijassa-351	68	12	and	and	CCONJ
ijassa-351	68	13	the	the	DET
ijassa-351	68	14	target	target	NOUN
ijassa-351	68	15	attribute	attribute	NOUN
ijassa-351	68	16	,	,	PUNCT
ijassa-351	68	17	since	since	SCONJ
ijassa-351	68	18	the	the	DET
ijassa-351	68	19	value	value	NOUN
ijassa-351	68	20	of	of	ADP
ijassa-351	68	21	correlation	correlation	NOUN
ijassa-351	68	22	identify	identify	VERB
ijassa-351	68	23	the	the	DET
ijassa-351	68	24	importance	importance	NOUN
ijassa-351	68	25	of	of	ADP
ijassa-351	68	26	target	target	NOUN
ijassa-351	68	27	attribute	attribute	NOUN
ijassa-351	68	28	[	[	X
ijassa-351	68	29	6,11	6,11	NOUN
ijassa-351	68	30	]	]	PUNCT
ijassa-351	68	31	.	.	PUNCT
ijassa-351	69	1	by	by	ADP
ijassa-351	69	2	using	use	VERB
ijassa-351	69	3	filtering	filter	VERB
ijassa-351	69	4	methods	method	NOUN
ijassa-351	69	5	attributes	attribute	NOUN
ijassa-351	69	6	can	can	AUX
ijassa-351	69	7	be	be	AUX
ijassa-351	69	8	ranked	rank	VERB
ijassa-351	69	9	independently	independently	ADV
ijassa-351	69	10	,	,	PUNCT
ijassa-351	69	11	then	then	ADV
ijassa-351	69	12	according	accord	VERB
ijassa-351	69	13	to	to	ADP
ijassa-351	69	14	the	the	DET
ijassa-351	69	15	ranking	ranking	ADJ
ijassa-351	69	16	result	result	NOUN
ijassa-351	69	17	optimal	optimal	ADJ
ijassa-351	69	18	subset	subset	NOUN
ijassa-351	69	19	of	of	ADP
ijassa-351	69	20	attributes	attribute	NOUN
ijassa-351	69	21	can	can	AUX
ijassa-351	69	22	be	be	AUX
ijassa-351	69	23	selected	select	VERB
ijassa-351	69	24	[	[	X
ijassa-351	69	25	12	12	NUM
ijassa-351	69	26	]	]	PUNCT
ijassa-351	69	27	.	.	PUNCT
ijassa-351	70	1	4	4	X
ijassa-351	70	2	.	.	X
ijassa-351	70	3	the	the	DET
ijassa-351	70	4	proposed	propose	VERB
ijassa-351	70	5	method	method	NOUN
ijassa-351	70	6	4.1	4.1	NUM
ijassa-351	70	7	dataset	dataset	ADJ
ijassa-351	70	8	description	description	NOUN
ijassa-351	70	9	table	table	NOUN
ijassa-351	70	10	1	1	NUM
ijassa-351	70	11	.	.	PUNCT
ijassa-351	70	12	characteristic	characteristic	ADJ
ijassa-351	70	13	of	of	ADP
ijassa-351	70	14	dataset	dataset	NOUN
ijassa-351	70	15	features	feature	NOUN
ijassa-351	70	16	.	.	PUNCT
ijassa-351	71	1	name	name	NOUN
ijassa-351	71	2	description	description	NOUN
ijassa-351	71	3	characteristics	characteristic	VERB
ijassa-351	71	4	dgn	dgn	PROPN
ijassa-351	71	5	diagnosis	diagnosis	NOUN
ijassa-351	71	6	specific	specific	ADJ
ijassa-351	71	7	combination	combination	NOUN
ijassa-351	71	8	of	of	ADP
ijassa-351	71	9	icd-10	icd-10	DET
ijassa-351	71	10	codes	code	NOUN
ijassa-351	71	11	for	for	ADP
ijassa-351	71	12	primary	primary	ADJ
ijassa-351	71	13	and	and	CCONJ
ijassa-351	71	14	secondary	secondary	ADJ
ijassa-351	71	15	as	as	ADP
ijassa-351	71	16	well	well	ADV
ijassa-351	71	17	multiple	multiple	ADJ
ijassa-351	71	18	tumors	tumor	NOUN
ijassa-351	71	19	if	if	SCONJ
ijassa-351	71	20	any	any	DET
ijassa-351	71	21	nominal	nominal	ADJ
ijassa-351	71	22	pre4	pre4	NOUN
ijassa-351	71	23	forced	force	VERB
ijassa-351	71	24	vital	vital	ADJ
ijassa-351	71	25	capacity	capacity	NOUN
ijassa-351	71	26	fvc	fvc	X
ijassa-351	71	27	numeric	numeric	ADP
ijassa-351	71	28	pre5	pre5	NOUN
ijassa-351	71	29	volume	volume	NOUN
ijassa-351	71	30	that	that	PRON
ijassa-351	71	31	has	have	AUX
ijassa-351	71	32	been	be	AUX
ijassa-351	71	33	exhaled	exhale	VERB
ijassa-351	71	34	at	at	ADP
ijassa-351	71	35	the	the	DET
ijassa-351	71	36	end	end	NOUN
ijassa-351	71	37	of	of	ADP
ijassa-351	71	38	the	the	DET
ijassa-351	71	39	first	first	ADJ
ijassa-351	71	40	second	second	NOUN
ijassa-351	71	41	of	of	ADP
ijassa-351	71	42	forced	force	VERB
ijassa-351	71	43	expiration	expiration	NOUN
ijassa-351	71	44	fev1	fev1	PROPN
ijassa-351	71	45	numeric	numeric	PROPN
ijassa-351	71	46	pre6	pre6	PROPN
ijassa-351	71	47	performance	performance	NOUN
ijassa-351	71	48	status	status	NOUN
ijassa-351	71	49	zubrod	zubrod	NOUN
ijassa-351	71	50	scale	scale	NOUN
ijassa-351	71	51	nominal	nominal	ADJ
ijassa-351	71	52	pre7	pre7	NOUN
ijassa-351	71	53	pain	pain	NOUN
ijassa-351	71	54	before	before	ADP
ijassa-351	71	55	surgery	surgery	NOUN
ijassa-351	71	56	binary	binary	PROPN
ijassa-351	71	57	pre8	pre8	PROPN
ijassa-351	71	58	haemoptysis	haemoptysis	NOUN
ijassa-351	71	59	before	before	ADP
ijassa-351	71	60	surgery	surgery	NOUN
ijassa-351	71	61	binary	binary	PROPN
ijassa-351	71	62	pre9	pre9	PROPN
ijassa-351	71	63	dyspnoea	dyspnoea	PROPN
ijassa-351	71	64	before	before	ADP
ijassa-351	71	65	surgery	surgery	NOUN
ijassa-351	71	66	binary	binary	ADJ
ijassa-351	71	67	pre10	pre10	PROPN
ijassa-351	71	68	cough	cough	NOUN
ijassa-351	71	69	before	before	ADP
ijassa-351	71	70	surgery	surgery	NOUN
ijassa-351	71	71	binary	binary	ADJ
ijassa-351	71	72	pre11	pre11	ADJ
ijassa-351	71	73	weakness	weakness	NOUN
ijassa-351	71	74	before	before	ADP
ijassa-351	71	75	surgery	surgery	NOUN
ijassa-351	71	76	binary	binary	PROPN
ijassa-351	71	77	pre14	pre14	PROPN
ijassa-351	71	78	t	t	PROPN
ijassa-351	71	79	in	in	ADP
ijassa-351	71	80	clinical	clinical	ADJ
ijassa-351	71	81	tnm	tnm	NOUN
ijassa-351	71	82	size	size	NOUN
ijassa-351	71	83	of	of	ADP
ijassa-351	71	84	the	the	DET
ijassa-351	71	85	original	original	ADJ
ijassa-351	71	86	tumor	tumor	NOUN
ijassa-351	71	87	,	,	PUNCT
ijassa-351	71	88	from	from	ADP
ijassa-351	71	89	oc11	oc11	PROPN
ijassa-351	71	90	(	(	PUNCT
ijassa-351	71	91	smallest	small	ADJ
ijassa-351	71	92	)	)	PUNCT
ijassa-351	71	93	to	to	ADP
ijassa-351	71	94	oc14	oc14	PROPN
ijassa-351	71	95	(	(	PUNCT
ijassa-351	71	96	largest	large	ADJ
ijassa-351	71	97	)	)	PUNCT
ijassa-351	71	98	nominal	nominal	ADJ
ijassa-351	71	99	pre17	pre17	NOUN
ijassa-351	71	100	type	type	NOUN
ijassa-351	71	101	2	2	NUM
ijassa-351	71	102	dm	dm	NOUN
ijassa-351	71	103	diabetes	diabetes	NOUN
ijassa-351	71	104	mellitus	mellitus	NOUN
ijassa-351	71	105	binary	binary	PROPN
ijassa-351	71	106	pre19	pre19	PROPN
ijassa-351	71	107	mi	mi	PROPN
ijassa-351	71	108	up	up	ADP
ijassa-351	71	109	to	to	PART
ijassa-351	71	110	6	6	NUM
ijassa-351	71	111	months	month	NOUN
ijassa-351	71	112	binary	binary	ADJ
ijassa-351	71	113	pre25	pre25	PROPN
ijassa-351	71	114	pad	pad	NOUN
ijassa-351	71	115	peripheral	peripheral	ADJ
ijassa-351	71	116	arterial	arterial	NOUN
ijassa-351	71	117	diseases	disease	NOUN
ijassa-351	71	118	binary	binary	ADJ
ijassa-351	71	119	pre30	pre30	NOUN
ijassa-351	71	120	smoking	smoke	VERB
ijassa-351	71	121	binary	binary	ADJ
ijassa-351	71	122	pre32	pre32	PROPN
ijassa-351	71	123	asthma	asthma	PROPN
ijassa-351	71	124	binary	binary	PROPN
ijassa-351	71	125	age	age	NOUN
ijassa-351	71	126	age	age	NOUN
ijassa-351	71	127	at	at	ADP
ijassa-351	71	128	surgery	surgery	NOUN
ijassa-351	71	129	numeric	numeric	ADP
ijassa-351	71	130	risk1y	risk1y	ADJ
ijassa-351	71	131	1	1	NUM
ijassa-351	71	132	year	year	NOUN
ijassa-351	71	133	survival	survival	NOUN
ijassa-351	71	134	period	period	NOUN
ijassa-351	71	135	t	t	PROPN
ijassa-351	71	136	value	value	NOUN
ijassa-351	71	137	if	if	SCONJ
ijassa-351	71	138	died	die	VERB
ijassa-351	71	139	binary	binary	PROPN
ijassa-351	71	140	73	73	NUM
ijassa-351	71	141	a.	a.	NOUN
ijassa-351	71	142	s.	s.	PROPN
ijassa-351	71	143	desuky	desuky	PROPN
ijassa-351	71	144	,	,	PUNCT
ijassa-351	71	145	l.m	l.m	PROPN
ijassa-351	71	146	.	.	PROPN
ijassa-351	71	147	el	el	PROPN
ijassa-351	71	148	bakrawy	bakrawy	PROPN
ijassa-351	71	149	copyright	copyright	NOUN
ijassa-351	72	1	©	©	PROPN
ijassa-351	72	2	2016	2016	NUM
ijassa-351	72	3	assa	assa	NOUN
ijassa-351	72	4	.	.	PUNCT
ijassa-351	73	1	adv	adv	PROPN
ijassa-351	73	2	.	.	PUNCT
ijassa-351	74	1	in	in	ADP
ijassa-351	74	2	systems	system	NOUN
ijassa-351	74	3	science	science	NOUN
ijassa-351	74	4	and	and	CCONJ
ijassa-351	74	5	appl	appl	NOUN
ijassa-351	74	6	.	.	PUNCT
ijassa-351	75	1	(	(	PUNCT
ijassa-351	75	2	2016	2016	NUM
ijassa-351	75	3	)	)	PUNCT
ijassa-351	75	4	thoracic	thoracic	NOUN
ijassa-351	75	5	surgery	surgery	NOUN
ijassa-351	75	6	data	datum	NOUN
ijassa-351	75	7	is	be	AUX
ijassa-351	75	8	dedicated	dedicate	VERB
ijassa-351	75	9	mainly	mainly	ADV
ijassa-351	75	10	to	to	PART
ijassa-351	75	11	elicit	elicit	VERB
ijassa-351	75	12	surgical	surgical	ADJ
ijassa-351	75	13	risk	risk	NOUN
ijassa-351	75	14	for	for	ADP
ijassa-351	75	15	real	real	ADJ
ijassa-351	75	16	-	-	PUNCT
ijassa-351	75	17	life	life	NOUN
ijassa-351	75	18	clinical	clinical	ADJ
ijassa-351	75	19	lung	lung	NOUN
ijassa-351	75	20	cancer	cancer	NOUN
ijassa-351	75	21	patients	patient	NOUN
ijassa-351	75	22	.	.	PUNCT
ijassa-351	76	1	the	the	DET
ijassa-351	76	2	data	datum	NOUN
ijassa-351	76	3	was	be	AUX
ijassa-351	76	4	collected	collect	VERB
ijassa-351	76	5	retrospectively	retrospectively	ADV
ijassa-351	76	6	by	by	ADP
ijassa-351	76	7	mareklubicz	mareklubicz	VERB
ijassa-351	76	8	et	et	PROPN
ijassa-351	76	9	al	al	PROPN
ijassa-351	76	10	.	.	PUNCT
ijassa-351	77	1	[	[	X
ijassa-351	77	2	13	13	NUM
ijassa-351	77	3	]	]	PUNCT
ijassa-351	77	4	at	at	ADP
ijassa-351	77	5	wroclaw	wroclaw	PROPN
ijassa-351	77	6	thoracic	thoracic	PROPN
ijassa-351	77	7	surgery	surgery	NOUN
ijassa-351	77	8	centre	centre	NOUN
ijassa-351	77	9	for	for	ADP
ijassa-351	77	10	consecutive	consecutive	ADJ
ijassa-351	77	11	patients	patient	NOUN
ijassa-351	77	12	–	–	PUNCT
ijassa-351	77	13	ages	age	NOUN
ijassa-351	77	14	from	from	ADP
ijassa-351	77	15	21	21	NUM
ijassa-351	77	16	to	to	PART
ijassa-351	77	17	87	87	NUM
ijassa-351	77	18	years	year	NOUN
ijassa-351	77	19	old	old	ADJ
ijassa-351	77	20	who	who	PRON
ijassa-351	77	21	underwent	undergo	VERB
ijassa-351	77	22	major	major	ADJ
ijassa-351	77	23	lung	lung	NOUN
ijassa-351	77	24	resections	resection	NOUN
ijassa-351	77	25	for	for	ADP
ijassa-351	77	26	primary	primary	ADJ
ijassa-351	77	27	lung	lung	NOUN
ijassa-351	77	28	cancer	cancer	NOUN
ijassa-351	77	29	in	in	ADP
ijassa-351	77	30	the	the	DET
ijassa-351	77	31	years	year	NOUN
ijassa-351	77	32	2007–2011	2007–2011	NUM
ijassa-351	77	33	.	.	PUNCT
ijassa-351	78	1	the	the	DET
ijassa-351	78	2	centre	centre	NOUN
ijassa-351	78	3	is	be	AUX
ijassa-351	78	4	associated	associate	VERB
ijassa-351	78	5	with	with	ADP
ijassa-351	78	6	the	the	DET
ijassa-351	78	7	department	department	PROPN
ijassa-351	78	8	of	of	ADP
ijassa-351	78	9	thoracic	thoracic	NOUN
ijassa-351	78	10	surgery	surgery	NOUN
ijassa-351	78	11	of	of	ADP
ijassa-351	78	12	the	the	DET
ijassa-351	78	13	medical	medical	ADJ
ijassa-351	78	14	university	university	PROPN
ijassa-351	78	15	of	of	ADP
ijassa-351	78	16	wroclaw	wroclaw	NOUN
ijassa-351	78	17	and	and	CCONJ
ijassa-351	78	18	lower	low	ADJ
ijassa-351	78	19	-	-	PUNCT
ijassa-351	78	20	silesian	silesian	ADJ
ijassa-351	78	21	centre	centre	NOUN
ijassa-351	78	22	for	for	ADP
ijassa-351	78	23	pulmonary	pulmonary	ADJ
ijassa-351	78	24	diseases	disease	NOUN
ijassa-351	78	25	,	,	PUNCT
ijassa-351	78	26	poland	poland	PROPN
ijassa-351	78	27	,	,	PUNCT
ijassa-351	78	28	while	while	SCONJ
ijassa-351	78	29	the	the	DET
ijassa-351	78	30	research	research	NOUN
ijassa-351	78	31	database	database	NOUN
ijassa-351	78	32	constitutes	constitute	VERB
ijassa-351	78	33	a	a	DET
ijassa-351	78	34	part	part	NOUN
ijassa-351	78	35	of	of	ADP
ijassa-351	78	36	the	the	DET
ijassa-351	78	37	national	national	ADJ
ijassa-351	78	38	lung	lung	PROPN
ijassa-351	78	39	cancer	cancer	NOUN
ijassa-351	78	40	registry	registry	NOUN
ijassa-351	78	41	,	,	PUNCT
ijassa-351	78	42	administered	administer	VERB
ijassa-351	78	43	by	by	ADP
ijassa-351	78	44	the	the	DET
ijassa-351	78	45	institute	institute	NOUN
ijassa-351	78	46	of	of	ADP
ijassa-351	78	47	tuberculosis	tuberculosis	NOUN
ijassa-351	78	48	and	and	CCONJ
ijassa-351	78	49	pulmonary	pulmonary	ADJ
ijassa-351	78	50	diseases	disease	NOUN
ijassa-351	78	51	in	in	ADP
ijassa-351	78	52	warsaw	warsaw	PROPN
ijassa-351	78	53	,	,	PUNCT
ijassa-351	78	54	poland	poland	PROPN
ijassa-351	78	55	.	.	PUNCT
ijassa-351	79	1	the	the	DET
ijassa-351	79	2	dataset	dataset	NOUN
ijassa-351	79	3	includes	include	VERB
ijassa-351	79	4	470	470	NUM
ijassa-351	79	5	instances	instance	NOUN
ijassa-351	79	6	(	(	PUNCT
ijassa-351	79	7	70	70	NUM
ijassa-351	79	8	true	true	ADJ
ijassa-351	79	9	and	and	CCONJ
ijassa-351	79	10	400	400	NUM
ijassa-351	79	11	false	false	ADJ
ijassa-351	79	12	)	)	PUNCT
ijassa-351	79	13	and	and	CCONJ
ijassa-351	79	14	16	16	NUM
ijassa-351	79	15	attributes	attribute	NOUN
ijassa-351	79	16	with	with	ADP
ijassa-351	79	17	no	no	DET
ijassa-351	79	18	missing	miss	VERB
ijassa-351	79	19	values	value	NOUN
ijassa-351	79	20	and	and	CCONJ
ijassa-351	79	21	binary	binary	NOUN
ijassa-351	79	22	valued	value	VERB
ijassa-351	79	23	class	class	NOUN
ijassa-351	79	24	(	(	PUNCT
ijassa-351	79	25	death	death	NOUN
ijassa-351	79	26	within	within	ADP
ijassa-351	79	27	one	one	NUM
ijassa-351	79	28	year	year	NOUN
ijassa-351	79	29	after	after	ADP
ijassa-351	79	30	surgery	surgery	NOUN
ijassa-351	79	31	–	–	PUNCT
ijassa-351	79	32	survival	survival	NOUN
ijassa-351	79	33	)	)	PUNCT
ijassa-351	79	34	.	.	PUNCT
ijassa-351	80	1	4.2	4.2	NUM
ijassa-351	80	2	research	research	NOUN
ijassa-351	80	3	methodology	methodology	NOUN
ijassa-351	80	4	in	in	ADP
ijassa-351	80	5	this	this	DET
ijassa-351	80	6	work	work	NOUN
ijassa-351	80	7	,	,	PUNCT
ijassa-351	80	8	version	version	NOUN
ijassa-351	80	9	3.7.12	3.7.12	NUM
ijassa-351	80	10	of	of	ADP
ijassa-351	80	11	weka	weka	PROPN
ijassa-351	80	12	(	(	PUNCT
ijassa-351	80	13	waikato	waikato	NOUN
ijassa-351	80	14	environment	environment	NOUN
ijassa-351	80	15	for	for	ADP
ijassa-351	80	16	knowledge	knowledge	NOUN
ijassa-351	80	17	analysis	analysis	NOUN
ijassa-351	80	18	)	)	PUNCT
ijassa-351	80	19	toolkit	toolkit	NOUN
ijassa-351	80	20	[	[	X
ijassa-351	80	21	14	14	NUM
ijassa-351	80	22	]	]	PUNCT
ijassa-351	80	23	has	have	AUX
ijassa-351	80	24	been	be	AUX
ijassa-351	80	25	used	use	VERB
ijassa-351	80	26	for	for	ADP
ijassa-351	80	27	analysis	analysis	NOUN
ijassa-351	80	28	.	.	PUNCT
ijassa-351	81	1	it	it	PRON
ijassa-351	81	2	is	be	AUX
ijassa-351	81	3	the	the	DET
ijassa-351	81	4	product	product	NOUN
ijassa-351	81	5	of	of	ADP
ijassa-351	81	6	the	the	DET
ijassa-351	81	7	university	university	NOUN
ijassa-351	81	8	of	of	ADP
ijassa-351	81	9	waikato	waikato	PROPN
ijassa-351	81	10	(	(	PUNCT
ijassa-351	81	11	new	new	PROPN
ijassa-351	81	12	zealand	zealand	PROPN
ijassa-351	81	13	)	)	PUNCT
ijassa-351	81	14	and	and	CCONJ
ijassa-351	81	15	it	it	PRON
ijassa-351	81	16	is	be	AUX
ijassa-351	81	17	licensed	license	VERB
ijassa-351	81	18	under	under	ADP
ijassa-351	81	19	the	the	DET
ijassa-351	81	20	gnu	gnu	PROPN
ijassa-351	81	21	general	general	ADJ
ijassa-351	81	22	public	public	ADJ
ijassa-351	81	23	license	license	NOUN
ijassa-351	81	24	.	.	PUNCT
ijassa-351	82	1	weka	weka	PROPN
ijassa-351	82	2	is	be	AUX
ijassa-351	82	3	a	a	DET
ijassa-351	82	4	popular	popular	ADJ
ijassa-351	82	5	suite	suite	NOUN
ijassa-351	82	6	of	of	ADP
ijassa-351	82	7	machine	machine	NOUN
ijassa-351	82	8	learning	learn	VERB
ijassa-351	82	9	software	software	NOUN
ijassa-351	82	10	written	write	VERB
ijassa-351	82	11	in	in	ADP
ijassa-351	82	12	java	java	PROPN
ijassa-351	82	13	,	,	PUNCT
ijassa-351	82	14	also	also	ADV
ijassa-351	82	15	it	it	PRON
ijassa-351	82	16	provides	provide	VERB
ijassa-351	82	17	access	access	NOUN
ijassa-351	82	18	to	to	ADP
ijassa-351	82	19	sql	sql	NOUN
ijassa-351	82	20	database	database	NOUN
ijassa-351	82	21	and	and	CCONJ
ijassa-351	82	22	process	process	VERB
ijassa-351	82	23	the	the	DET
ijassa-351	82	24	result	result	NOUN
ijassa-351	82	25	retrieved	retrieve	VERB
ijassa-351	82	26	by	by	ADP
ijassa-351	82	27	a	a	DET
ijassa-351	82	28	database	database	NOUN
ijassa-351	82	29	query	query	NOUN
ijassa-351	82	30	.	.	PUNCT
ijassa-351	83	1	we	we	PRON
ijassa-351	83	2	have	have	AUX
ijassa-351	83	3	run	run	VERB
ijassa-351	83	4	our	our	PRON
ijassa-351	83	5	experiments	experiment	NOUN
ijassa-351	83	6	on	on	ADP
ijassa-351	83	7	a	a	DET
ijassa-351	83	8	system	system	NOUN
ijassa-351	83	9	with	with	ADP
ijassa-351	83	10	a	a	DET
ijassa-351	83	11	2.30	2.30	NUM
ijassa-351	83	12	ghz	ghz	NOUN
ijassa-351	83	13	intel(r	intel(r	NOUN
ijassa-351	83	14	)	)	PUNCT
ijassa-351	83	15	coretmi5	coretmi5	NOUN
ijassa-351	83	16	processor	processor	NOUN
ijassa-351	83	17	and	and	CCONJ
ijassa-351	83	18	512	512	NUM
ijassa-351	83	19	mb	mb	NOUN
ijassa-351	83	20	of	of	ADP
ijassa-351	83	21	ram	ram	NOUN
ijassa-351	83	22	running	run	VERB
ijassa-351	83	23	microsoft	microsoft	PROPN
ijassa-351	83	24	windows	windows	PROPN
ijassa-351	83	25	7	7	NUM
ijassa-351	83	26	professional	professional	ADJ
ijassa-351	83	27	(	(	PUNCT
ijassa-351	83	28	sp2	sp2	NOUN
ijassa-351	83	29	)	)	PUNCT
ijassa-351	83	30	.	.	PUNCT
ijassa-351	84	1	cross	cross	ADJ
ijassa-351	84	2	-	-	NOUN
ijassa-351	84	3	validation	validation	ADJ
ijassa-351	84	4	(	(	PUNCT
ijassa-351	84	5	10	10	NUM
ijassa-351	84	6	folds	fold	NOUN
ijassa-351	84	7	)	)	PUNCT
ijassa-351	84	8	has	have	AUX
ijassa-351	84	9	been	be	AUX
ijassa-351	84	10	used	use	VERB
ijassa-351	84	11	in	in	ADP
ijassa-351	84	12	this	this	DET
ijassa-351	84	13	study	study	NOUN
ijassa-351	84	14	to	to	PART
ijassa-351	84	15	validate	validate	VERB
ijassa-351	84	16	the	the	DET
ijassa-351	84	17	results	result	NOUN
ijassa-351	84	18	.	.	PUNCT
ijassa-351	85	1	in	in	ADP
ijassa-351	85	2	this	this	DET
ijassa-351	85	3	model	model	NOUN
ijassa-351	85	4	,	,	PUNCT
ijassa-351	85	5	the	the	DET
ijassa-351	85	6	dataset	dataset	NOUN
ijassa-351	85	7	is	be	AUX
ijassa-351	85	8	partitioned	partition	VERB
ijassa-351	85	9	into	into	ADP
ijassa-351	85	10	complementary	complementary	ADJ
ijassa-351	85	11	10	10	NUM
ijassa-351	85	12	equal	equal	ADJ
ijassa-351	85	13	sized	sized	ADJ
ijassa-351	85	14	subsets	subset	NOUN
ijassa-351	85	15	.	.	PUNCT
ijassa-351	86	1	the	the	DET
ijassa-351	86	2	analysis	analysis	NOUN
ijassa-351	86	3	is	be	AUX
ijassa-351	86	4	performed	perform	VERB
ijassa-351	86	5	on	on	ADP
ijassa-351	86	6	9	9	NUM
ijassa-351	86	7	subsets	subset	NOUN
ijassa-351	86	8	(	(	PUNCT
ijassa-351	86	9	training	training	NOUN
ijassa-351	86	10	)	)	PUNCT
ijassa-351	86	11	and	and	CCONJ
ijassa-351	86	12	validating	validate	VERB
ijassa-351	86	13	the	the	DET
ijassa-351	86	14	analysis	analysis	NOUN
ijassa-351	86	15	on	on	ADP
ijassa-351	86	16	one	one	NUM
ijassa-351	86	17	subset	subset	NOUN
ijassa-351	86	18	(	(	PUNCT
ijassa-351	86	19	testing	testing	NOUN
ijassa-351	86	20	)	)	PUNCT
ijassa-351	86	21	.	.	PUNCT
ijassa-351	87	1	ten	ten	NUM
ijassa-351	87	2	rounds	round	NOUN
ijassa-351	87	3	of	of	ADP
ijassa-351	87	4	cross	cross	NOUN
ijassa-351	87	5	-	-	ADJ
ijassa-351	87	6	validation	validation	NOUN
ijassa-351	87	7	are	be	AUX
ijassa-351	87	8	performed	perform	VERB
ijassa-351	87	9	and	and	CCONJ
ijassa-351	87	10	in	in	ADP
ijassa-351	87	11	each	each	DET
ijassa-351	87	12	round	round	NOUN
ijassa-351	87	13	another	another	DET
ijassa-351	87	14	subset	subset	NOUN
ijassa-351	87	15	2	2	NUM
ijassa-351	87	16	through	through	ADP
ijassa-351	87	17	10	10	NUM
ijassa-351	87	18	used	use	VERB
ijassa-351	87	19	as	as	ADP
ijassa-351	87	20	testing	test	VERB
ijassa-351	87	21	dataset	dataset	NOUN
ijassa-351	87	22	.	.	PUNCT
ijassa-351	88	1	the	the	DET
ijassa-351	88	2	validation	validation	NOUN
ijassa-351	88	3	results	result	NOUN
ijassa-351	88	4	are	be	AUX
ijassa-351	88	5	averaged	average	VERB
ijassa-351	88	6	over	over	ADP
ijassa-351	88	7	the	the	DET
ijassa-351	88	8	ten	ten	NUM
ijassa-351	88	9	rounds	round	NOUN
ijassa-351	88	10	in	in	ADP
ijassa-351	88	11	the	the	DET
ijassa-351	88	12	final	final	ADJ
ijassa-351	88	13	phase	phase	NOUN
ijassa-351	88	14	.	.	PUNCT
ijassa-351	89	1	researchers	researcher	NOUN
ijassa-351	89	2	in	in	ADP
ijassa-351	89	3	the	the	DET
ijassa-351	89	4	machine	machine	NOUN
ijassa-351	89	5	learning	learn	VERB
ijassa-351	89	6	field	field	NOUN
ijassa-351	89	7	have	have	AUX
ijassa-351	89	8	proposed	propose	VERB
ijassa-351	89	9	numerous	numerous	ADJ
ijassa-351	89	10	attribute	attribute	NOUN
ijassa-351	89	11	ranking	ranking	NOUN
ijassa-351	89	12	and	and	CCONJ
ijassa-351	89	13	attribute	attribute	NOUN
ijassa-351	89	14	selection	selection	NOUN
ijassa-351	89	15	methods	method	NOUN
ijassa-351	89	16	.	.	PUNCT
ijassa-351	90	1	the	the	DET
ijassa-351	90	2	main	main	ADJ
ijassa-351	90	3	aim	aim	NOUN
ijassa-351	90	4	of	of	ADP
ijassa-351	90	5	these	these	DET
ijassa-351	90	6	methods	method	NOUN
ijassa-351	90	7	is	be	AUX
ijassa-351	90	8	to	to	PART
ijassa-351	90	9	eliminate	eliminate	VERB
ijassa-351	90	10	redundant	redundant	ADJ
ijassa-351	90	11	or	or	CCONJ
ijassa-351	90	12	irrelevant	irrelevant	ADJ
ijassa-351	90	13	attributes	attribute	NOUN
ijassa-351	90	14	from	from	ADP
ijassa-351	90	15	the	the	DET
ijassa-351	90	16	original	original	ADJ
ijassa-351	90	17	set	set	NOUN
ijassa-351	90	18	of	of	ADP
ijassa-351	90	19	attributes	attribute	NOUN
ijassa-351	90	20	.	.	PUNCT
ijassa-351	91	1	in	in	ADP
ijassa-351	91	2	our	our	PRON
ijassa-351	91	3	work	work	NOUN
ijassa-351	91	4	,	,	PUNCT
ijassa-351	91	5	we	we	PRON
ijassa-351	91	6	use	use	VERB
ijassa-351	91	7	the	the	DET
ijassa-351	91	8	attribute	attribute	NOUN
ijassa-351	91	9	ranking	ranking	NOUN
ijassa-351	91	10	methods	method	NOUN
ijassa-351	91	11	(	(	PUNCT
ijassa-351	91	12	information	information	NOUN
ijassa-351	91	13	gain	gain	NOUN
ijassa-351	91	14	(	(	PUNCT
ijassa-351	91	15	ig	ig	NOUN
ijassa-351	91	16	)	)	PUNCT
ijassa-351	91	17	attribute	attribute	NOUN
ijassa-351	91	18	evaluation	evaluation	NOUN
ijassa-351	91	19	,	,	PUNCT
ijassa-351	91	20	symmetrical	symmetrical	ADJ
ijassa-351	91	21	uncertainty	uncertainty	NOUN
ijassa-351	91	22	(	(	PUNCT
ijassa-351	91	23	su	su	NOUN
ijassa-351	91	24	)	)	PUNCT
ijassa-351	91	25	attribute	attribute	NOUN
ijassa-351	91	26	evaluation	evaluation	NOUN
ijassa-351	91	27	and	and	CCONJ
ijassa-351	91	28	relief	relief	NOUN
ijassa-351	91	29	-	-	PUNCT
ijassa-351	91	30	f	f	NOUN
ijassa-351	91	31	(	(	PUNCT
ijassa-351	91	32	rf	rf	NOUN
ijassa-351	91	33	)	)	PUNCT
ijassa-351	91	34	attribute	attribute	NOUN
ijassa-351	91	35	evaluation	evaluation	NOUN
ijassa-351	91	36	)	)	PUNCT
ijassa-351	91	37	information	information	NOUN
ijassa-351	91	38	gain	gain	NOUN
ijassa-351	91	39	(	(	PUNCT
ijassa-351	91	40	ig	ig	NOUN
ijassa-351	91	41	)	)	PUNCT
ijassa-351	91	42	attribute	attribute	NOUN
ijassa-351	91	43	evaluation	evaluation	NOUN
ijassa-351	92	1	[	[	X
ijassa-351	92	2	15	15	NUM
ijassa-351	92	3	]	]	PUNCT
ijassa-351	92	4	is	be	AUX
ijassa-351	92	5	used	use	VERB
ijassa-351	92	6	to	to	PART
ijassa-351	92	7	evaluate	evaluate	VERB
ijassa-351	92	8	the	the	DET
ijassa-351	92	9	importance	importance	NOUN
ijassa-351	92	10	of	of	ADP
ijassa-351	92	11	an	an	DET
ijassa-351	92	12	attribute	attribute	NOUN
ijassa-351	92	13	by	by	ADP
ijassa-351	92	14	measuring	measure	VERB
ijassa-351	92	15	the	the	DET
ijassa-351	92	16	information	information	NOUN
ijassa-351	92	17	gain	gain	NOUN
ijassa-351	92	18	with	with	ADP
ijassa-351	92	19	regard	regard	NOUN
ijassa-351	92	20	to	to	ADP
ijassa-351	92	21	the	the	DET
ijassa-351	92	22	class	class	NOUN
ijassa-351	92	23	.	.	PUNCT
ijassa-351	93	1	the	the	DET
ijassa-351	93	2	bases	basis	NOUN
ijassa-351	93	3	of	of	ADP
ijassa-351	93	4	ig	ig	PROPN
ijassa-351	93	5	depend	depend	VERB
ijassa-351	93	6	on	on	ADP
ijassa-351	93	7	entropy	entropy	NOUN
ijassa-351	93	8	which	which	PRON
ijassa-351	93	9	measure	measure	VERB
ijassa-351	93	10	the	the	DET
ijassa-351	93	11	randomness	randomness	NOUN
ijassa-351	93	12	of	of	ADP
ijassa-351	93	13	the	the	DET
ijassa-351	93	14	system	system	NOUN
ijassa-351	93	15	.	.	PUNCT
ijassa-351	94	1	information	information	NOUN
ijassa-351	94	2	gain	gain	NOUN
ijassa-351	94	3	can	can	AUX
ijassa-351	94	4	be	be	AUX
ijassa-351	94	5	calculated	calculate	VERB
ijassa-351	94	6	by	by	ADP
ijassa-351	94	7	the	the	DET
ijassa-351	94	8	following	follow	VERB
ijassa-351	94	9	equation	equation	NOUN
ijassa-351	94	10	:	:	PUNCT
ijassa-351	94	11	attribute)|	attribute)|	NOUN
ijassa-351	94	12	h(class	h(class	NOUN
ijassa-351	94	13	-	-	PUNCT
ijassa-351	94	14	h(class	h(class	NOUN
ijassa-351	94	15	)	)	PUNCT
ijassa-351	94	16	=	=	SYM
ijassa-351	94	17	attribute	attribute	NOUN
ijassa-351	94	18	)	)	PUNCT
ijassa-351	94	19	ig(class	ig(class	PROPN
ijassa-351	94	20	,	,	PUNCT
ijassa-351	94	21	(	(	PUNCT
ijassa-351	94	22	1	1	X
ijassa-351	94	23	)	)	PUNCT
ijassa-351	94	24	where	where	SCONJ
ijassa-351	94	25	h	h	NOUN
ijassa-351	94	26	is	be	AUX
ijassa-351	94	27	the	the	DET
ijassa-351	94	28	entropy	entropy	NOUN
ijassa-351	94	29	which	which	PRON
ijassa-351	94	30	stands	stand	VERB
ijassa-351	94	31	for	for	ADP
ijassa-351	94	32	the	the	DET
ijassa-351	94	33	greek	greek	PROPN
ijassa-351	94	34	alphabet	alphabet	PROPN
ijassa-351	94	35	eta	eta	PROPN
ijassa-351	94	36	.	.	PUNCT
ijassa-351	95	1	symmetrical	symmetrical	ADJ
ijassa-351	95	2	uncertainty	uncertainty	NOUN
ijassa-351	95	3	(	(	PUNCT
ijassa-351	95	4	su	su	NOUN
ijassa-351	95	5	)	)	PUNCT
ijassa-351	95	6	attribute	attribute	NOUN
ijassa-351	95	7	evaluation	evaluation	NOUN
ijassa-351	96	1	[	[	X
ijassa-351	96	2	16]is	16]is	NUM
ijassa-351	96	3	used	use	VERB
ijassa-351	96	4	to	to	PART
ijassa-351	96	5	evaluate	evaluate	VERB
ijassa-351	96	6	the	the	DET
ijassa-351	96	7	importance	importance	NOUN
ijassa-351	96	8	of	of	ADP
ijassa-351	96	9	an	an	DET
ijassa-351	96	10	attribute	attribute	NOUN
ijassa-351	96	11	by	by	ADP
ijassa-351	96	12	measuring	measure	VERB
ijassa-351	96	13	the	the	DET
ijassa-351	96	14	symmetrical	symmetrical	ADJ
ijassa-351	96	15	uncertainty	uncertainty	NOUN
ijassa-351	96	16	with	with	ADP
ijassa-351	96	17	respect	respect	NOUN
ijassa-351	96	18	to	to	ADP
ijassa-351	96	19	the	the	DET
ijassa-351	96	20	class	class	NOUN
ijassa-351	96	21	.	.	PUNCT
ijassa-351	97	1	symmetrical	symmetrical	ADJ
ijassa-351	97	2	uncertainty	uncertainty	NOUN
ijassa-351	97	3	compensates	compensate	VERB
ijassa-351	97	4	for	for	ADP
ijassa-351	97	5	the	the	DET
ijassa-351	97	6	inherent	inherent	ADJ
ijassa-351	97	7	bias	bias	NOUN
ijassa-351	97	8	in	in	ADP
ijassa-351	97	9	information	information	NOUN
ijassa-351	97	10	gain	gain	NOUN
ijassa-351	97	11	.	.	PUNCT
ijassa-351	98	1	symmetrical	symmetrical	ADJ
ijassa-351	98	2	uncertainty	uncertainty	NOUN
ijassa-351	98	3	is	be	AUX
ijassa-351	98	4	given	give	VERB
ijassa-351	98	5	by	by	ADP
ijassa-351	98	6	the	the	DET
ijassa-351	98	7	following	follow	VERB
ijassa-351	98	8	equation	equation	NOUN
ijassa-351	98	9	:	:	PUNCT
ijassa-351	98	10	e))h(attribut	e))h(attribut	NOUN
ijassa-351	98	11	-	-	PUNCT
ijassa-351	98	12	h(class	h(class	NOUN
ijassa-351	98	13	)	)	PUNCT
ijassa-351	98	14	/(attribute	/(attribute	PROPN
ijassa-351	98	15	)	)	PUNCT
ijassa-351	98	16	ig(class,*2	ig(class,*2	NOUN
ijassa-351	98	17	=	=	PUNCT
ijassa-351	98	18	attribute	attribute	NOUN
ijassa-351	98	19	)	)	PUNCT
ijassa-351	98	20	su(class	su(class	PROPN
ijassa-351	98	21	,	,	PUNCT
ijassa-351	98	22	(	(	PUNCT
ijassa-351	98	23	2	2	X
ijassa-351	98	24	)	)	PUNCT
ijassa-351	98	25	relief	relief	NOUN
ijassa-351	98	26	-	-	PUNCT
ijassa-351	98	27	f	f	NOUN
ijassa-351	98	28	(	(	PUNCT
ijassa-351	98	29	rf	rf	NOUN
ijassa-351	98	30	)	)	PUNCT
ijassa-351	98	31	attribute	attribute	NOUN
ijassa-351	98	32	evaluation	evaluation	NOUN
ijassa-351	98	33	is	be	AUX
ijassa-351	98	34	used	use	VERB
ijassa-351	98	35	to	to	PART
ijassa-351	98	36	rank	rank	VERB
ijassa-351	98	37	the	the	DET
ijassa-351	98	38	quality	quality	NOUN
ijassa-351	98	39	of	of	ADP
ijassa-351	98	40	features	feature	NOUN
ijassa-351	98	41	depending	depend	VERB
ijassa-351	98	42	on	on	ADP
ijassa-351	98	43	how	how	SCONJ
ijassa-351	98	44	well	well	ADV
ijassa-351	98	45	their	their	PRON
ijassa-351	98	46	values	value	NOUN
ijassa-351	98	47	differ	differ	VERB
ijassa-351	98	48	from	from	ADP
ijassa-351	98	49	the	the	DET
ijassa-351	98	50	cases	case	NOUN
ijassa-351	98	51	that	that	PRON
ijassa-351	98	52	are	be	AUX
ijassa-351	98	53	close	close	ADJ
ijassa-351	98	54	to	to	ADP
ijassa-351	98	55	each	each	DET
ijassa-351	98	56	other	other	ADJ
ijassa-351	98	57	.	.	PUNCT
ijassa-351	99	1	it	it	PRON
ijassa-351	99	2	is	be	AUX
ijassa-351	99	3	sensible	sensible	ADJ
ijassa-351	99	4	to	to	PART
ijassa-351	99	5	predict	predict	VERB
ijassa-351	99	6	that	that	SCONJ
ijassa-351	99	7	a	a	DET
ijassa-351	99	8	valuable	valuable	ADJ
ijassa-351	99	9	feature	feature	NOUN
ijassa-351	99	10	should	should	AUX
ijassa-351	99	11	have	have	VERB
ijassa-351	99	12	different	different	ADJ
ijassa-351	99	13	values	value	NOUN
ijassa-351	99	14	between	between	ADP
ijassa-351	99	15	cases	case	NOUN
ijassa-351	99	16	belong	belong	VERB
ijassa-351	99	17	to	to	ADP
ijassa-351	99	18	different	different	ADJ
ijassa-351	99	19	classes	class	NOUN
ijassa-351	99	20	and	and	CCONJ
ijassa-351	99	21	have	have	VERB
ijassa-351	99	22	the	the	DET
ijassa-351	99	23	same	same	ADJ
ijassa-351	99	24	value	value	NOUN
ijassa-351	99	25	for	for	ADP
ijassa-351	99	26	cases	case	NOUN
ijassa-351	99	27	from	from	ADP
ijassa-351	99	28	the	the	DET
ijassa-351	99	29	same	same	ADJ
ijassa-351	99	30	class	class	NOUN
ijassa-351	100	1	[	[	X
ijassa-351	100	2	17	17	NUM
ijassa-351	100	3	]	]	PUNCT
ijassa-351	100	4	.	.	PUNCT
ijassa-351	101	1	the	the	DET
ijassa-351	101	2	aim	aim	NOUN
ijassa-351	101	3	of	of	ADP
ijassa-351	101	4	this	this	DET
ijassa-351	101	5	paper	paper	NOUN
ijassa-351	101	6	is	be	AUX
ijassa-351	101	7	to	to	PART
ijassa-351	101	8	analyze	analyze	VERB
ijassa-351	101	9	the	the	DET
ijassa-351	101	10	effect	effect	NOUN
ijassa-351	101	11	of	of	ADP
ijassa-351	101	12	number	number	NOUN
ijassa-351	101	13	of	of	ADP
ijassa-351	101	14	attributes	attribute	NOUN
ijassa-351	101	15	on	on	ADP
ijassa-351	101	16	accuracy	accuracy	NOUN
ijassa-351	101	17	of	of	ADP
ijassa-351	101	18	machine	machine	NOUN
ijassa-351	101	19	learning	learn	VERB
ijassa-351	101	20	techniques	technique	NOUN
ijassa-351	101	21	to	to	PART
ijassa-351	101	22	solve	solve	VERB
ijassa-351	101	23	the	the	DET
ijassa-351	101	24	problem	problem	NOUN
ijassa-351	101	25	for	for	ADP
ijassa-351	101	26	prediction	prediction	NOUN
ijassa-351	101	27	of	of	ADP
ijassa-351	101	28	the	the	DET
ijassa-351	101	29	post	post	ADJ
ijassa-351	101	30	-	-	ADJ
ijassa-351	101	31	operative	operative	ADJ
ijassa-351	101	32	life	life	NOUN
ijassa-351	101	33	https://en.wikipedia.org/wiki/gnu_general_public_license	https://en.wikipedia.org/wiki/gnu_general_public_license	NOUN
ijassa-351	101	34	https://en.wikipedia.org/wiki/machine_learning	https://en.wikipedia.org/wiki/machine_learne	VERB
ijassa-351	101	35	https://en.wikipedia.org/wiki/java_(programming_language	https://en.wikipedia.org/wiki/java_(programming_language	NOUN
ijassa-351	101	36	)	)	PUNCT
ijassa-351	101	37	https://en.wikipedia.org/wiki/complement_(set_theory	https://en.wikipedia.org/wiki/complement_(set_theory	NOUN
ijassa-351	101	38	)	)	PUNCT
ijassa-351	101	39	improved	improve	VERB
ijassa-351	101	40	prediction	prediction	NOUN
ijassa-351	101	41	of	of	ADP
ijassa-351	101	42	post	post	ADJ
ijassa-351	101	43	-	-	ADJ
ijassa-351	101	44	operative	operative	ADJ
ijassa-351	101	45	life	life	NOUN
ijassa-351	101	46	expectancy	expectancy	NOUN
ijassa-351	101	47	after	after	ADP
ijassa-351	101	48	thoracic	thoracic	NOUN
ijassa-351	101	49	surgery	surgery	NOUN
ijassa-351	101	50	74	74	NUM
ijassa-351	101	51	copyright	copyright	NOUN
ijassa-351	101	52	©	©	PROPN
ijassa-351	101	53	2016	2016	NUM
ijassa-351	101	54	assa	assa	NOUN
ijassa-351	101	55	.	.	PUNCT
ijassa-351	102	1	adv	adv	PROPN
ijassa-351	102	2	.	.	PUNCT
ijassa-351	103	1	in	in	ADP
ijassa-351	103	2	systems	system	NOUN
ijassa-351	103	3	science	science	NOUN
ijassa-351	103	4	and	and	CCONJ
ijassa-351	103	5	appl	appl	NOUN
ijassa-351	103	6	.	.	PUNCT
ijassa-351	104	1	(	(	PUNCT
ijassa-351	104	2	2016	2016	NUM
ijassa-351	104	3	)	)	PUNCT
ijassa-351	104	4	expectancy	expectancy	NOUN
ijassa-351	104	5	in	in	ADP
ijassa-351	104	6	the	the	DET
ijassa-351	104	7	lung	lung	NOUN
ijassa-351	104	8	cancer	cancer	NOUN
ijassa-351	104	9	patients	patient	NOUN
ijassa-351	104	10	.	.	PUNCT
ijassa-351	105	1	reducing	reduce	VERB
ijassa-351	105	2	the	the	DET
ijassa-351	105	3	number	number	NOUN
ijassa-351	105	4	of	of	ADP
ijassa-351	105	5	attributes	attribute	NOUN
ijassa-351	105	6	and	and	CCONJ
ijassa-351	105	7	increasing	increase	VERB
ijassa-351	105	8	the	the	DET
ijassa-351	105	9	accuracy	accuracy	NOUN
ijassa-351	105	10	is	be	AUX
ijassa-351	105	11	required	require	VERB
ijassa-351	105	12	to	to	PART
ijassa-351	105	13	minimize	minimize	VERB
ijassa-351	105	14	the	the	DET
ijassa-351	105	15	computational	computational	ADJ
ijassa-351	105	16	time	time	NOUN
ijassa-351	105	17	of	of	ADP
ijassa-351	105	18	prediction	prediction	NOUN
ijassa-351	105	19	techniques	technique	NOUN
ijassa-351	105	20	.	.	PUNCT
ijassa-351	106	1	in	in	ADP
ijassa-351	106	2	this	this	DET
ijassa-351	106	3	study	study	NOUN
ijassa-351	106	4	,	,	PUNCT
ijassa-351	106	5	we	we	PRON
ijassa-351	106	6	used	use	VERB
ijassa-351	106	7	information	information	NOUN
ijassa-351	106	8	gain	gain	NOUN
ijassa-351	106	9	,	,	PUNCT
ijassa-351	106	10	symmetrical	symmetrical	ADJ
ijassa-351	106	11	uncertainty	uncertainty	NOUN
ijassa-351	106	12	and	and	CCONJ
ijassa-351	106	13	relief	relief	NOUN
ijassa-351	106	14	-	-	PUNCT
ijassa-351	106	15	f	f	NOUN
ijassa-351	106	16	as	as	ADP
ijassa-351	106	17	attribute	attribute	NOUN
ijassa-351	106	18	ranking	ranking	NOUN
ijassa-351	106	19	methods	method	NOUN
ijassa-351	106	20	to	to	PART
ijassa-351	106	21	reduce	reduce	VERB
ijassa-351	106	22	the	the	DET
ijassa-351	106	23	number	number	NOUN
ijassa-351	106	24	of	of	ADP
ijassa-351	106	25	attributes	attribute	NOUN
ijassa-351	106	26	(	(	PUNCT
ijassa-351	106	27	from	from	ADP
ijassa-351	106	28	16	16	NUM
ijassa-351	106	29	to	to	PART
ijassa-351	106	30	13	13	NUM
ijassa-351	106	31	attributes	attribute	NOUN
ijassa-351	106	32	)	)	PUNCT
ijassa-351	106	33	,	,	PUNCT
ijassa-351	106	34	then	then	ADV
ijassa-351	106	35	we	we	PRON
ijassa-351	106	36	examined	examine	VERB
ijassa-351	106	37	the	the	DET
ijassa-351	106	38	quality	quality	NOUN
ijassa-351	106	39	of	of	ADP
ijassa-351	106	40	techniques	technique	NOUN
ijassa-351	106	41	naïve	naïve	ADJ
ijassa-351	106	42	bayes	bayes	NOUN
ijassa-351	106	43	,	,	PUNCT
ijassa-351	106	44	simple	simple	ADJ
ijassa-351	106	45	logistic	logistic	ADJ
ijassa-351	106	46	regression	regression	NOUN
ijassa-351	106	47	,	,	PUNCT
ijassa-351	106	48	j48	j48	PROPN
ijassa-351	106	49	,	,	PUNCT
ijassa-351	106	50	multilayer	multilayer	ADJ
ijassa-351	106	51	perceptron	perceptron	PROPN
ijassa-351	106	52	,	,	PUNCT
ijassa-351	106	53	and	and	CCONJ
ijassa-351	106	54	svm	svm	VERB
ijassa-351	106	55	after	after	ADP
ijassa-351	106	56	applying	apply	VERB
ijassa-351	106	57	the	the	DET
ijassa-351	106	58	three	three	NUM
ijassa-351	106	59	ranking	ranking	ADJ
ijassa-351	106	60	methods	method	NOUN
ijassa-351	106	61	for	for	ADP
ijassa-351	106	62	prediction	prediction	NOUN
ijassa-351	106	63	of	of	ADP
ijassa-351	106	64	post	post	ADJ
ijassa-351	106	65	-	-	ADJ
ijassa-351	106	66	operative	operative	ADJ
ijassa-351	106	67	life	life	NOUN
ijassa-351	106	68	expectancy	expectancy	NOUN
ijassa-351	106	69	after	after	ADP
ijassa-351	106	70	thoracic	thoracic	NOUN
ijassa-351	106	71	surgery	surgery	NOUN
ijassa-351	106	72	.	.	PUNCT
ijassa-351	107	1	the	the	DET
ijassa-351	107	2	quality	quality	NOUN
ijassa-351	107	3	of	of	ADP
ijassa-351	107	4	the	the	DET
ijassa-351	107	5	proposed	propose	VERB
ijassa-351	107	6	methods	method	NOUN
ijassa-351	107	7	is	be	AUX
ijassa-351	107	8	evaluated	evaluate	VERB
ijassa-351	107	9	by	by	ADP
ijassa-351	107	10	comparing	compare	VERB
ijassa-351	107	11	the	the	DET
ijassa-351	107	12	performance	performance	NOUN
ijassa-351	107	13	of	of	ADP
ijassa-351	107	14	naïve	naïve	ADJ
ijassa-351	107	15	bayes	bayes	NOUN
ijassa-351	107	16	,	,	PUNCT
ijassa-351	107	17	simple	simple	ADJ
ijassa-351	107	18	logistic	logistic	ADJ
ijassa-351	107	19	regression	regression	NOUN
ijassa-351	107	20	,	,	PUNCT
ijassa-351	107	21	j48	j48	ADJ
ijassa-351	107	22	and	and	CCONJ
ijassa-351	107	23	multilayer	multilayer	ADJ
ijassa-351	107	24	perceptron	perceptron	PROPN
ijassa-351	107	25	techniques	technique	NOUN
ijassa-351	107	26	with	with	ADP
ijassa-351	107	27	and	and	CCONJ
ijassa-351	107	28	without	without	ADP
ijassa-351	107	29	using	use	VERB
ijassa-351	107	30	attribute	attribute	NOUN
ijassa-351	107	31	ranking	ranking	NOUN
ijassa-351	107	32	methods	method	NOUN
ijassa-351	107	33	as	as	ADP
ijassa-351	107	34	first	first	ADJ
ijassa-351	107	35	step	step	NOUN
ijassa-351	107	36	.	.	PUNCT
ijassa-351	108	1	also	also	ADV
ijassa-351	108	2	,	,	PUNCT
ijassa-351	108	3	our	our	PRON
ijassa-351	108	4	proposed	propose	VERB
ijassa-351	108	5	`	`	PUNCT
ijassa-351	108	6	methods	method	NOUN
ijassa-351	108	7	is	be	AUX
ijassa-351	108	8	compared	compare	VERB
ijassa-351	108	9	to	to	PART
ijassa-351	108	10	boosted	boost	VERB
ijassa-351	108	11	naïve	naïve	ADJ
ijassa-351	108	12	bayes	bayes	NOUN
ijassa-351	108	13	,	,	PUNCT
ijassa-351	108	14	boosted	boost	VERB
ijassa-351	108	15	simple	simple	ADJ
ijassa-351	108	16	logistic	logistic	ADJ
ijassa-351	108	17	regression	regression	NOUN
ijassa-351	108	18	,	,	PUNCT
ijassa-351	108	19	boosted	boost	VERB
ijassa-351	108	20	j48	j48	PROPN
ijassa-351	108	21	,	,	PUNCT
ijassa-351	108	22	boosted	boost	VERB
ijassa-351	108	23	multilayer	multilayer	ADJ
ijassa-351	108	24	perceptron	perceptron	PROPN
ijassa-351	108	25	and	and	CCONJ
ijassa-351	108	26	boosted	boost	VERB
ijassa-351	108	27	svm	svm	PROPN
ijassa-351	108	28	.	.	PROPN
ijassa-351	108	29	5	5	NUM
ijassa-351	108	30	.	.	X
ijassa-351	108	31	experimental	experimental	ADJ
ijassa-351	108	32	results	result	NOUN
ijassa-351	108	33	performances	performance	NOUN
ijassa-351	108	34	of	of	ADP
ijassa-351	108	35	the	the	DET
ijassa-351	108	36	methods	method	NOUN
ijassa-351	108	37	were	be	AUX
ijassa-351	108	38	analyzed	analyze	VERB
ijassa-351	108	39	by	by	ADP
ijassa-351	108	40	using	use	VERB
ijassa-351	108	41	six	six	NUM
ijassa-351	108	42	metricsaccuracy	metricsaccuracy	NOUN
ijassa-351	108	43	,	,	PUNCT
ijassa-351	108	44	f	f	PROPN
ijassa-351	108	45	measure	measure	NOUN
ijassa-351	108	46	,	,	PUNCT
ijassa-351	108	47	roc	roc	PROPN
ijassa-351	108	48	curve	curve	NOUN
ijassa-351	108	49	,	,	PUNCT
ijassa-351	108	50	gmean	gmean	PROPN
ijassa-351	108	51	,	,	PUNCT
ijassa-351	108	52	tnr	tnr	PROPN
ijassa-351	108	53	and	and	CCONJ
ijassa-351	108	54	tpr	tpr	PROPN
ijassa-351	109	1	[	[	X
ijassa-351	109	2	9	9	NUM
ijassa-351	109	3	,	,	PUNCT
ijassa-351	109	4	10	10	NUM
ijassa-351	109	5	]	]	PUNCT
ijassa-351	109	6	.	.	PUNCT
ijassa-351	110	1	accuracy	accuracy	NOUN
ijassa-351	110	2	is	be	AUX
ijassa-351	110	3	the	the	DET
ijassa-351	110	4	percentage	percentage	NOUN
ijassa-351	110	5	of	of	ADP
ijassa-351	110	6	observations	observation	NOUN
ijassa-351	110	7	that	that	PRON
ijassa-351	110	8	were	be	AUX
ijassa-351	110	9	correctly	correctly	ADV
ijassa-351	110	10	predicted	predict	VERB
ijassa-351	110	11	by	by	ADP
ijassa-351	110	12	the	the	DET
ijassa-351	110	13	method	method	NOUN
ijassa-351	110	14	.	.	PUNCT
ijassa-351	111	1	it	it	PRON
ijassa-351	111	2	was	be	AUX
ijassa-351	111	3	used	use	VERB
ijassa-351	111	4	to	to	PART
ijassa-351	111	5	evaluate	evaluate	VERB
ijassa-351	111	6	the	the	DET
ijassa-351	111	7	performance	performance	NOUN
ijassa-351	111	8	of	of	ADP
ijassa-351	111	9	each	each	DET
ijassa-351	111	10	algorithm	algorithm	NOUN
ijassa-351	111	11	.	.	PUNCT
ijassa-351	112	1	n)+tn/(p+tp	n)+tn/(p+tp	NOUN
ijassa-351	112	2	=	=	ADJ
ijassa-351	112	3	accuracy	accuracy	NOUN
ijassa-351	112	4	(	(	PUNCT
ijassa-351	112	5	3	3	NUM
ijassa-351	112	6	)	)	PUNCT
ijassa-351	112	7	table	table	NOUN
ijassa-351	112	8	2	2	NUM
ijassa-351	112	9	.	.	PUNCT
ijassa-351	112	10	shows	show	VERB
ijassa-351	112	11	the	the	DET
ijassa-351	112	12	confusion	confusion	NOUN
ijassa-351	112	13	matrix	matrix	NOUN
ijassa-351	112	14	which	which	PRON
ijassa-351	112	15	clarifies	clarify	VERB
ijassa-351	112	16	the	the	DET
ijassa-351	112	17	prediction	prediction	NOUN
ijassa-351	112	18	tendencies	tendency	NOUN
ijassa-351	112	19	tp	tp	X
ijassa-351	112	20	(	(	PUNCT
ijassa-351	112	21	true	true	ADJ
ijassa-351	112	22	positive	positive	ADJ
ijassa-351	112	23	)	)	PUNCT
ijassa-351	112	24	,	,	PUNCT
ijassa-351	112	25	tn	tn	PROPN
ijassa-351	112	26	(	(	PUNCT
ijassa-351	112	27	true	true	ADJ
ijassa-351	112	28	negative	negative	NOUN
ijassa-351	112	29	)	)	PUNCT
ijassa-351	112	30	,	,	PUNCT
ijassa-351	112	31	fp	fp	INTJ
ijassa-351	112	32	(	(	PUNCT
ijassa-351	112	33	false	false	ADJ
ijassa-351	112	34	positive	positive	NOUN
ijassa-351	112	35	)	)	PUNCT
ijassa-351	112	36	and	and	CCONJ
ijassa-351	112	37	fn	fn	INTJ
ijassa-351	112	38	(	(	PUNCT
ijassa-351	112	39	false	false	ADJ
ijassa-351	112	40	negative	negative	NOUN
ijassa-351	112	41	)	)	PUNCT
ijassa-351	112	42	of	of	ADP
ijassa-351	112	43	considered	consider	VERB
ijassa-351	112	44	machine	machine	NOUN
ijassa-351	112	45	learning	learning	NOUN
ijassa-351	112	46	technique	technique	NOUN
ijassa-351	112	47	.	.	PUNCT
ijassa-351	113	1	table	table	NOUN
ijassa-351	113	2	2	2	NUM
ijassa-351	113	3	.	.	PUNCT
ijassa-351	113	4	confusion	confusion	NOUN
ijassa-351	113	5	matrix	matrix	NOUN
ijassa-351	113	6	predicted	predict	VERB
ijassa-351	113	7	outcome	outcome	NOUN
ijassa-351	113	8	p	p	NOUN
ijassa-351	113	9	n	n	PRON
ijassa-351	113	10	actual	actual	ADJ
ijassa-351	113	11	value	value	NOUN
ijassa-351	113	12	p	p	X
ijassa-351	113	13	tp	tp	ADP
ijassa-351	113	14	fn	fn	NOUN
ijassa-351	113	15	n	n	CCONJ
ijassa-351	113	16	fp	fp	PROPN
ijassa-351	113	17	tn	tn	PROPN
ijassa-351	113	18	accuracy	accuracy	NOUN
ijassa-351	113	19	is	be	AUX
ijassa-351	113	20	not	not	PART
ijassa-351	113	21	a	a	DET
ijassa-351	113	22	reliable	reliable	ADJ
ijassa-351	113	23	metric	metric	NOUN
ijassa-351	113	24	for	for	ADP
ijassa-351	113	25	the	the	DET
ijassa-351	113	26	real	real	ADJ
ijassa-351	113	27	performance	performance	NOUN
ijassa-351	113	28	of	of	ADP
ijassa-351	113	29	a	a	DET
ijassa-351	113	30	machine	machine	NOUN
ijassa-351	113	31	learning	learning	NOUN
ijassa-351	113	32	technique	technique	NOUN
ijassa-351	113	33	,	,	PUNCT
ijassa-351	113	34	because	because	SCONJ
ijassa-351	113	35	it	it	PRON
ijassa-351	113	36	will	will	AUX
ijassa-351	113	37	yield	yield	VERB
ijassa-351	113	38	misleading	misleading	ADJ
ijassa-351	113	39	results	result	NOUN
ijassa-351	113	40	if	if	SCONJ
ijassa-351	113	41	the	the	DET
ijassa-351	113	42	data	datum	NOUN
ijassa-351	113	43	set	set	VERB
ijassa-351	113	44	is	be	AUX
ijassa-351	113	45	imbalanced	imbalanced	ADJ
ijassa-351	113	46	(	(	PUNCT
ijassa-351	113	47	i.e.	i.e.	X
ijassa-351	113	48	when	when	SCONJ
ijassa-351	113	49	the	the	DET
ijassa-351	113	50	number	number	NOUN
ijassa-351	113	51	of	of	ADP
ijassa-351	113	52	samples	sample	NOUN
ijassa-351	113	53	in	in	ADP
ijassa-351	113	54	different	different	ADJ
ijassa-351	113	55	classes	class	NOUN
ijassa-351	113	56	vary	vary	VERB
ijassa-351	113	57	greatly	greatly	ADV
ijassa-351	113	58	)	)	PUNCT
ijassa-351	113	59	.	.	PUNCT
ijassa-351	114	1	since	since	SCONJ
ijassa-351	114	2	thoracic	thoracic	NOUN
ijassa-351	114	3	surgery	surgery	NOUN
ijassa-351	114	4	data	datum	NOUN
ijassa-351	114	5	is	be	AUX
ijassa-351	114	6	imbalanced	imbalanced	ADJ
ijassa-351	114	7	data	datum	NOUN
ijassa-351	114	8	with	with	ADP
ijassa-351	114	9	70	70	NUM
ijassa-351	114	10	true	true	ADJ
ijassa-351	114	11	and	and	CCONJ
ijassa-351	114	12	400	400	NUM
ijassa-351	114	13	false	false	ADJ
ijassa-351	114	14	instances	instance	NOUN
ijassa-351	114	15	we	we	PRON
ijassa-351	114	16	used	use	VERB
ijassa-351	114	17	f	f	PROPN
ijassa-351	114	18	measure	measure	NOUN
ijassa-351	114	19	(	(	PUNCT
ijassa-351	114	20	f1	f1	NOUN
ijassa-351	114	21	score	score	NOUN
ijassa-351	114	22	)	)	PUNCT
ijassa-351	114	23	,	,	PUNCT
ijassa-351	114	24	roc	roc	PROPN
ijassa-351	114	25	curve	curve	NOUN
ijassa-351	114	26	,	,	PUNCT
ijassa-351	114	27	gmean	gmean	PROPN
ijassa-351	114	28	,	,	PUNCT
ijassa-351	114	29	tnr	tnr	PROPN
ijassa-351	114	30	and	and	CCONJ
ijassa-351	114	31	tpr	tpr	PROPN
ijassa-351	114	32	.	.	PUNCT
ijassa-351	115	1	where	where	SCONJ
ijassa-351	115	2	,	,	PUNCT
ijassa-351	115	3	f	f	PROPN
ijassa-351	115	4	measure	measure	NOUN
ijassa-351	115	5	was	be	AUX
ijassa-351	115	6	used	use	VERB
ijassa-351	115	7	to	to	PART
ijassa-351	115	8	test	test	VERB
ijassa-351	115	9	the	the	DET
ijassa-351	115	10	accuracy	accuracy	NOUN
ijassa-351	115	11	depending	depend	VERB
ijassa-351	115	12	on	on	ADP
ijassa-351	115	13	harmonic	harmonic	ADJ
ijassa-351	115	14	mean	mean	NOUN
ijassa-351	115	15	of	of	ADP
ijassa-351	115	16	precision	precision	NOUN
ijassa-351	115	17	&	&	CCONJ
ijassa-351	115	18	recall	recall	PROPN
ijassa-351	115	19	.	.	PUNCT
ijassa-351	116	1	fn)+fp+2tp/(2tp	fn)+fp+2tp/(2tp	PART
ijassa-351	117	1	=	=	SYM
ijassa-351	117	2	measure	measure	NOUN
ijassa-351	117	3	f	f	X
ijassa-351	117	4	(	(	PUNCT
ijassa-351	117	5	4	4	NUM
ijassa-351	117	6	)	)	PUNCT
ijassa-351	117	7	while	while	SCONJ
ijassa-351	117	8	,	,	PUNCT
ijassa-351	117	9	roc	roc	PROPN
ijassa-351	117	10	curve	curve	NOUN
ijassa-351	117	11	was	be	AUX
ijassa-351	117	12	also	also	ADV
ijassa-351	117	13	used	use	VERB
ijassa-351	117	14	as	as	ADP
ijassa-351	117	15	an	an	DET
ijassa-351	117	16	effective	effective	ADJ
ijassa-351	117	17	method	method	NOUN
ijassa-351	117	18	to	to	PART
ijassa-351	117	19	evaluate	evaluate	VERB
ijassa-351	117	20	the	the	DET
ijassa-351	117	21	performance	performance	NOUN
ijassa-351	117	22	of	of	ADP
ijassa-351	117	23	predicted	predict	VERB
ijassa-351	117	24	models	model	NOUN
ijassa-351	117	25	by	by	ADP
ijassa-351	117	26	plotting	plot	VERB
ijassa-351	117	27	the	the	DET
ijassa-351	117	28	true	true	ADJ
ijassa-351	117	29	positives	positive	NOUN
ijassa-351	117	30	against	against	ADP
ijassa-351	117	31	the	the	DET
ijassa-351	117	32	false	false	ADJ
ijassa-351	117	33	positives	positive	NOUN
ijassa-351	117	34	and	and	CCONJ
ijassa-351	117	35	area	area	NOUN
ijassa-351	117	36	under	under	ADP
ijassa-351	117	37	the	the	DET
ijassa-351	117	38	roc	roc	PROPN
ijassa-351	117	39	curve	curve	NOUN
ijassa-351	117	40	is	be	AUX
ijassa-351	117	41	used	use	VERB
ijassa-351	117	42	for	for	ADP
ijassa-351	117	43	predicting	predict	VERB
ijassa-351	117	44	accuracy	accuracy	NOUN
ijassa-351	117	45	of	of	ADP
ijassa-351	117	46	models	model	NOUN
ijassa-351	117	47	.	.	PUNCT
ijassa-351	118	1	the	the	DET
ijassa-351	118	2	gmean	gmean	ADJ
ijassa-351	118	3	(	(	PUNCT
ijassa-351	118	4	geometric	geometric	ADJ
ijassa-351	118	5	mean	mean	NOUN
ijassa-351	118	6	)	)	PUNCT
ijassa-351	118	7	is	be	AUX
ijassa-351	118	8	a	a	DET
ijassa-351	118	9	widely	widely	ADV
ijassa-351	118	10	used	use	VERB
ijassa-351	118	11	quality	quality	NOUN
ijassa-351	118	12	rate	rate	NOUN
ijassa-351	118	13	and	and	CCONJ
ijassa-351	118	14	is	be	AUX
ijassa-351	118	15	defined	define	VERB
ijassa-351	118	16	as	as	ADP
ijassa-351	118	17	equation	equation	NOUN
ijassa-351	118	18	:	:	PUNCT
ijassa-351	118	19	tnr	tnr	PROPN
ijassa-351	118	20	tpr	tpr	PROPN
ijassa-351	118	21	=	=	PROPN
ijassa-351	118	22	gmean	gmean	ADJ
ijassa-351	118	23			PROPN
ijassa-351	118	24	(	(	PUNCT
ijassa-351	118	25	5	5	NUM
ijassa-351	118	26	)	)	PUNCT
ijassa-351	118	27	where	where	SCONJ
ijassa-351	118	28	tnr	tnr	PROPN
ijassa-351	118	29	(	(	PUNCT
ijassa-351	118	30	specificity	specificity	NOUN
ijassa-351	118	31	or	or	CCONJ
ijassa-351	118	32	true	true	ADJ
ijassa-351	118	33	negative	negative	ADJ
ijassa-351	118	34	rate	rate	NOUN
ijassa-351	118	35	)	)	PUNCT
ijassa-351	118	36	is	be	AUX
ijassa-351	118	37	described	describe	VERB
ijassa-351	118	38	by	by	ADP
ijassa-351	118	39	:	:	PUNCT
ijassa-351	118	40	fp	fp	X
ijassa-351	118	41	)	)	PUNCT
ijassa-351	118	42	+	+	NUM
ijassa-351	118	43	tn/(tn	tn/(tn	NOUN
ijassa-351	118	44	=	=	SYM
ijassa-351	118	45	tnr	tnr	PROPN
ijassa-351	118	46	(	(	PUNCT
ijassa-351	118	47	6	6	NUM
ijassa-351	118	48	)	)	PUNCT
ijassa-351	118	49	and	and	CCONJ
ijassa-351	118	50	tpr	tpr	PROPN
ijassa-351	118	51	(	(	PUNCT
ijassa-351	118	52	sensitivity	sensitivity	NOUN
ijassa-351	118	53	or	or	CCONJ
ijassa-351	118	54	true	true	ADJ
ijassa-351	118	55	positive	positive	ADJ
ijassa-351	118	56	rate	rate	NOUN
ijassa-351	118	57	)	)	PUNCT
ijassa-351	118	58	and	and	CCONJ
ijassa-351	118	59	described	describe	VERB
ijassa-351	118	60	by	by	ADP
ijassa-351	118	61	the	the	DET
ijassa-351	118	62	equation	equation	NOUN
ijassa-351	118	63	:	:	PUNCT
ijassa-351	118	64	fn	fn	X
ijassa-351	118	65	)	)	PUNCT
ijassa-351	119	1	+	+	CCONJ
ijassa-351	119	2	tp	tp	ADP
ijassa-351	119	3	tp/	tp/	PROPN
ijassa-351	119	4	(	(	PUNCT
ijassa-351	119	5	=	=	SYM
ijassa-351	119	6	tpr	tpr	X
ijassa-351	119	7	(	(	PUNCT
ijassa-351	119	8	7	7	NUM
ijassa-351	119	9	)	)	PUNCT
ijassa-351	119	10	75	75	NUM
ijassa-351	119	11	a.	a.	NOUN
ijassa-351	119	12	s.	s.	PROPN
ijassa-351	119	13	desuky	desuky	PROPN
ijassa-351	119	14	,	,	PUNCT
ijassa-351	119	15	l.m	l.m	PROPN
ijassa-351	119	16	.	.	PROPN
ijassa-351	119	17	el	el	PROPN
ijassa-351	119	18	bakrawy	bakrawy	PROPN
ijassa-351	119	19	copyright	copyright	NOUN
ijassa-351	119	20	©	©	PROPN
ijassa-351	119	21	2016	2016	NUM
ijassa-351	119	22	assa	assa	NOUN
ijassa-351	119	23	.	.	PUNCT
ijassa-351	120	1	adv	adv	PROPN
ijassa-351	120	2	.	.	PUNCT
ijassa-351	121	1	in	in	ADP
ijassa-351	121	2	systems	system	NOUN
ijassa-351	121	3	science	science	NOUN
ijassa-351	121	4	and	and	CCONJ
ijassa-351	121	5	appl	appl	NOUN
ijassa-351	121	6	.	.	PUNCT
ijassa-351	122	1	(	(	PUNCT
ijassa-351	122	2	2016	2016	NUM
ijassa-351	122	3	)	)	PUNCT
ijassa-351	122	4	table	table	NOUN
ijassa-351	122	5	3	3	NUM
ijassa-351	122	6	.	.	PUNCT
ijassa-351	122	7	shows	show	VERB
ijassa-351	122	8	the	the	DET
ijassa-351	122	9	accuracy	accuracy	NOUN
ijassa-351	122	10	of	of	ADP
ijassa-351	122	11	naïve	naïve	ADJ
ijassa-351	122	12	bayes	bayes	NOUN
ijassa-351	122	13	,	,	PUNCT
ijassa-351	122	14	simple	simple	ADJ
ijassa-351	122	15	logistic	logistic	ADJ
ijassa-351	122	16	regression	regression	NOUN
ijassa-351	122	17	,	,	PUNCT
ijassa-351	122	18	j48	j48	ADJ
ijassa-351	122	19	and	and	CCONJ
ijassa-351	122	20	multilayer	multilayer	ADJ
ijassa-351	122	21	perceptron	perceptron	PROPN
ijassa-351	122	22	techniques	technique	NOUN
ijassa-351	122	23	with	with	ADP
ijassa-351	122	24	and	and	CCONJ
ijassa-351	122	25	without	without	ADP
ijassa-351	122	26	using	use	VERB
ijassa-351	122	27	attribute	attribute	NOUN
ijassa-351	122	28	ranking	ranking	NOUN
ijassa-351	122	29	methods	method	NOUN
ijassa-351	122	30	.	.	PUNCT
ijassa-351	123	1	also	also	ADV
ijassa-351	123	2	,	,	PUNCT
ijassa-351	123	3	it	it	PRON
ijassa-351	123	4	shows	show	VERB
ijassa-351	123	5	the	the	DET
ijassa-351	123	6	accuracy	accuracy	NOUN
ijassa-351	123	7	of	of	ADP
ijassa-351	123	8	boosted	boosted	ADJ
ijassa-351	123	9	naïve	naïve	ADJ
ijassa-351	123	10	bayes	bayes	NOUN
ijassa-351	123	11	,	,	PUNCT
ijassa-351	123	12	boosted	boost	VERB
ijassa-351	123	13	simple	simple	ADJ
ijassa-351	123	14	logistic	logistic	ADJ
ijassa-351	123	15	regression	regression	NOUN
ijassa-351	123	16	,	,	PUNCT
ijassa-351	123	17	boosted	boost	VERB
ijassa-351	123	18	j48	j48	PROPN
ijassa-351	123	19	,	,	PUNCT
ijassa-351	123	20	boosted	boost	VERB
ijassa-351	123	21	multilayer	multilayer	ADJ
ijassa-351	123	22	perceptron	perceptron	NOUN
ijassa-351	123	23	for	for	ADP
ijassa-351	123	24	prediction	prediction	NOUN
ijassa-351	123	25	of	of	ADP
ijassa-351	123	26	post	post	ADJ
ijassa-351	123	27	-	-	ADJ
ijassa-351	123	28	operative	operative	ADJ
ijassa-351	123	29	life	life	NOUN
ijassa-351	123	30	expectancy	expectancy	NOUN
ijassa-351	123	31	after	after	ADP
ijassa-351	123	32	thoracic	thoracic	NOUN
ijassa-351	123	33	surgery	surgery	NOUN
ijassa-351	123	34	.	.	PUNCT
ijassa-351	124	1	results	result	NOUN
ijassa-351	124	2	show	show	VERB
ijassa-351	124	3	that	that	SCONJ
ijassa-351	124	4	using	use	VERB
ijassa-351	124	5	ig	ig	PROPN
ijassa-351	124	6	and	and	CCONJ
ijassa-351	124	7	su	su	PROPN
ijassa-351	124	8	as	as	ADP
ijassa-351	124	9	ranking	ranking	ADJ
ijassa-351	124	10	methods	method	NOUN
ijassa-351	124	11	before	before	ADP
ijassa-351	124	12	applying	apply	VERB
ijassa-351	124	13	naïve	naïve	ADJ
ijassa-351	124	14	bayes	bayes	NOUN
ijassa-351	124	15	gives	give	VERB
ijassa-351	124	16	the	the	DET
ijassa-351	124	17	better	well	ADJ
ijassa-351	124	18	accuracy	accuracy	NOUN
ijassa-351	124	19	than	than	ADP
ijassa-351	124	20	applying	apply	VERB
ijassa-351	124	21	naïve	naïve	ADJ
ijassa-351	124	22	bayes	baye	NOUN
ijassa-351	124	23	without	without	ADP
ijassa-351	124	24	using	use	VERB
ijassa-351	124	25	ranking	ranking	ADJ
ijassa-351	124	26	methods	method	NOUN
ijassa-351	124	27	and	and	CCONJ
ijassa-351	124	28	with	with	ADP
ijassa-351	124	29	boosted	boost	VERB
ijassa-351	124	30	naïve	naïve	ADJ
ijassa-351	124	31	bayes	baye	NOUN
ijassa-351	124	32	.	.	PUNCT
ijassa-351	125	1	also	also	ADV
ijassa-351	125	2	,	,	PUNCT
ijassa-351	125	3	in	in	ADP
ijassa-351	125	4	the	the	DET
ijassa-351	125	5	case	case	NOUN
ijassa-351	125	6	of	of	ADP
ijassa-351	125	7	applying	apply	VERB
ijassa-351	125	8	simple	simple	ADJ
ijassa-351	125	9	logistic	logistic	NOUN
ijassa-351	125	10	,	,	PUNCT
ijassa-351	125	11	using	use	VERB
ijassa-351	125	12	the	the	DET
ijassa-351	125	13	three	three	NUM
ijassa-351	125	14	ranking	ranking	ADJ
ijassa-351	125	15	methods	method	NOUN
ijassa-351	125	16	gives	give	VERB
ijassa-351	125	17	better	well	ADJ
ijassa-351	125	18	accuracy	accuracy	NOUN
ijassa-351	125	19	than	than	ADP
ijassa-351	125	20	applying	apply	VERB
ijassa-351	125	21	simple	simple	ADJ
ijassa-351	125	22	logistic	logistic	NOUN
ijassa-351	125	23	without	without	ADP
ijassa-351	125	24	using	use	VERB
ijassa-351	125	25	ranking	ranking	ADJ
ijassa-351	125	26	methods	method	NOUN
ijassa-351	125	27	and	and	CCONJ
ijassa-351	125	28	with	with	ADP
ijassa-351	125	29	boosted	boosted	ADJ
ijassa-351	125	30	simple	simple	ADJ
ijassa-351	125	31	logistic	logistic	NOUN
ijassa-351	125	32	.	.	PUNCT
ijassa-351	126	1	similarity	similarity	NOUN
ijassa-351	126	2	,	,	PUNCT
ijassa-351	126	3	in	in	ADP
ijassa-351	126	4	the	the	DET
ijassa-351	126	5	case	case	NOUN
ijassa-351	126	6	of	of	ADP
ijassa-351	126	7	applying	apply	VERB
ijassa-351	126	8	multilayer	multilayer	ADJ
ijassa-351	126	9	perceptron	perceptron	PROPN
ijassa-351	126	10	,	,	PUNCT
ijassa-351	126	11	using	use	VERB
ijassa-351	126	12	the	the	DET
ijassa-351	126	13	three	three	NUM
ijassa-351	126	14	ranking	ranking	ADJ
ijassa-351	126	15	methods	method	NOUN
ijassa-351	126	16	gives	give	VERB
ijassa-351	126	17	better	well	ADJ
ijassa-351	126	18	accuracy	accuracy	NOUN
ijassa-351	126	19	than	than	ADP
ijassa-351	126	20	applying	apply	VERB
ijassa-351	126	21	multilayer	multilayer	ADJ
ijassa-351	126	22	perceptron	perceptron	NOUN
ijassa-351	126	23	without	without	ADP
ijassa-351	126	24	using	use	VERB
ijassa-351	126	25	ranking	ranking	ADJ
ijassa-351	126	26	methods	method	NOUN
ijassa-351	126	27	and	and	CCONJ
ijassa-351	126	28	with	with	ADP
ijassa-351	126	29	boosted	boosted	ADJ
ijassa-351	126	30	multilayer	multilayer	ADJ
ijassa-351	126	31	perceptron	perceptron	PROPN
ijassa-351	126	32	.	.	PUNCT
ijassa-351	127	1	but	but	CCONJ
ijassa-351	127	2	in	in	ADP
ijassa-351	127	3	the	the	DET
ijassa-351	127	4	case	case	NOUN
ijassa-351	127	5	of	of	ADP
ijassa-351	127	6	applying	apply	VERB
ijassa-351	127	7	j48	j48	NOUN
ijassa-351	127	8	without	without	ADP
ijassa-351	127	9	using	use	VERB
ijassa-351	127	10	ranking	ranking	ADJ
ijassa-351	127	11	methods	method	NOUN
ijassa-351	127	12	gives	give	VERB
ijassa-351	127	13	better	well	ADJ
ijassa-351	127	14	accuracy	accuracy	NOUN
ijassa-351	127	15	than	than	ADP
ijassa-351	127	16	applying	apply	VERB
ijassa-351	127	17	j48	j48	NOUN
ijassa-351	127	18	with	with	ADP
ijassa-351	127	19	using	use	VERB
ijassa-351	127	20	ranking	ranking	ADJ
ijassa-351	127	21	methods	method	NOUN
ijassa-351	127	22	and	and	CCONJ
ijassa-351	127	23	applying	apply	VERB
ijassa-351	127	24	j48	j48	NOUN
ijassa-351	127	25	with	with	ADP
ijassa-351	127	26	using	use	VERB
ijassa-351	127	27	ranking	ranking	ADJ
ijassa-351	127	28	methods	method	NOUN
ijassa-351	127	29	gives	give	VERB
ijassa-351	127	30	better	well	ADJ
ijassa-351	127	31	accuracy	accuracy	NOUN
ijassa-351	127	32	than	than	SCONJ
ijassa-351	127	33	boosted	boost	VERB
ijassa-351	127	34	j48	j48	PROPN
ijassa-351	127	35	.	.	PUNCT
ijassa-351	127	36	table	table	NOUN
ijassa-351	128	1	3	3	NUM
ijassa-351	128	2	.	.	PUNCT
ijassa-351	128	3	also	also	ADV
ijassa-351	128	4	shows	show	VERB
ijassa-351	128	5	that	that	SCONJ
ijassa-351	128	6	the	the	DET
ijassa-351	128	7	simple	simple	ADJ
ijassa-351	128	8	logistic	logistic	ADJ
ijassa-351	128	9	technique	technique	NOUN
ijassa-351	128	10	applied	apply	VERB
ijassa-351	128	11	with	with	ADP
ijassa-351	128	12	the	the	DET
ijassa-351	128	13	three	three	NUM
ijassa-351	128	14	ranking	ranking	ADJ
ijassa-351	128	15	methods	method	NOUN
ijassa-351	128	16	gives	give	VERB
ijassa-351	128	17	the	the	DET
ijassa-351	128	18	best	good	ADJ
ijassa-351	128	19	accuracy	accuracy	NOUN
ijassa-351	128	20	.	.	PUNCT
ijassa-351	129	1	table	table	NOUN
ijassa-351	129	2	3	3	NUM
ijassa-351	129	3	.	.	PUNCT
ijassa-351	129	4	prediction	prediction	NOUN
ijassa-351	129	5	accuracy	accuracy	NOUN
ijassa-351	129	6	comparison	comparison	NOUN
ijassa-351	129	7	of	of	ADP
ijassa-351	129	8	machine	machine	NOUN
ijassa-351	129	9	learning	learn	VERB
ijassa-351	129	10	techniques	technique	NOUN
ijassa-351	129	11	using	use	VERB
ijassa-351	129	12	thoracic	thoracic	NOUN
ijassa-351	129	13	surgery	surgery	NOUN
ijassa-351	129	14	data	datum	NOUN
ijassa-351	129	15	set	set	VERB
ijassa-351	129	16	ml	ml	ADP
ijassa-351	129	17	techniques	technique	NOUN
ijassa-351	129	18	method	method	NOUN
ijassa-351	129	19	accuracy	accuracy	NOUN
ijassa-351	129	20	naïve	naïve	ADJ
ijassa-351	129	21	bayes	bayes	NOUN
ijassa-351	129	22	original	original	ADJ
ijassa-351	130	1	[	[	X
ijassa-351	130	2	10	10	NUM
ijassa-351	130	3	]	]	SYM
ijassa-351	130	4	77.74	77.74	NUM
ijassa-351	130	5	boosted	boost	VERB
ijassa-351	130	6	[	[	X
ijassa-351	130	7	10	10	NUM
ijassa-351	130	8	]	]	SYM
ijassa-351	130	9	78.32	78.32	NUM
ijassa-351	130	10	su	su	NOUN
ijassa-351	130	11	82.12	82.12	NUM
ijassa-351	130	12	rf	rf	NUM
ijassa-351	130	13	77.74	77.74	NUM
ijassa-351	130	14	ig	ig	PROPN
ijassa-351	130	15	82.13	82.13	NUM
ijassa-351	130	16	simple	simple	ADJ
ijassa-351	130	17	logistic	logistic	ADJ
ijassa-351	130	18	original	original	ADJ
ijassa-351	130	19	[	[	X
ijassa-351	130	20	10	10	NUM
ijassa-351	130	21	]	]	SYM
ijassa-351	130	22	84.55	84.55	NUM
ijassa-351	130	23	boosted	boost	VERB
ijassa-351	130	24	[	[	X
ijassa-351	130	25	10	10	NUM
ijassa-351	130	26	]	]	SYM
ijassa-351	130	27	84.53	84.53	NUM
ijassa-351	130	28	su	su	NOUN
ijassa-351	130	29	84.68	84.68	NUM
ijassa-351	130	30	rf	rf	NUM
ijassa-351	130	31	84.68	84.68	NUM
ijassa-351	130	32	ig	ig	PROPN
ijassa-351	130	33	84.68	84.68	NUM
ijassa-351	130	34	multilayer	multilayer	NOUN
ijassa-351	130	35	perceptron	perceptron	PROPN
ijassa-351	130	36	original	original	PROPN
ijassa-351	131	1	[	[	X
ijassa-351	131	2	10	10	NUM
ijassa-351	131	3	]	]	SYM
ijassa-351	131	4	80.91	80.91	NUM
ijassa-351	131	5	boosted	boost	VERB
ijassa-351	131	6	[	[	PUNCT
ijassa-351	131	7	10	10	NUM
ijassa-351	131	8	]	]	SYM
ijassa-351	131	9	80.70	80.70	NUM
ijassa-351	131	10	su	su	NOUN
ijassa-351	131	11	81.27	81.27	NUM
ijassa-351	131	12	rf	rf	NUM
ijassa-351	131	13	81.28	81.28	NUM
ijassa-351	131	14	ig	ig	PROPN
ijassa-351	131	15	81.28	81.28	NUM
ijassa-351	131	16	j48	j48	PROPN
ijassa-351	131	17	original	original	ADJ
ijassa-351	131	18	[	[	X
ijassa-351	131	19	10	10	NUM
ijassa-351	131	20	]	]	SYM
ijassa-351	131	21	84.64	84.64	NUM
ijassa-351	131	22	boosted	boost	VERB
ijassa-351	131	23	[	[	X
ijassa-351	131	24	10	10	NUM
ijassa-351	131	25	]	]	SYM
ijassa-351	131	26	79.34	79.34	NUM
ijassa-351	131	27	su	su	NOUN
ijassa-351	131	28	84.46	84.46	NUM
ijassa-351	131	29	rf	rf	NUM
ijassa-351	131	30	84.47	84.47	NUM
ijassa-351	131	31	ig	ig	PROPN
ijassa-351	131	32	84.47	84.47	NUM
ijassa-351	131	33	table	table	NOUN
ijassa-351	131	34	4	4	NUM
ijassa-351	131	35	.	.	PUNCT
ijassa-351	131	36	shows	show	VERB
ijassa-351	131	37	the	the	DET
ijassa-351	131	38	f	f	PROPN
ijassa-351	131	39	measure	measure	NOUN
ijassa-351	131	40	and	and	CCONJ
ijassa-351	131	41	roc	roc	PROPN
ijassa-351	131	42	curve	curve	NOUN
ijassa-351	131	43	of	of	ADP
ijassa-351	131	44	naïve	naïve	ADJ
ijassa-351	131	45	bayes	bayes	NOUN
ijassa-351	131	46	,	,	PUNCT
ijassa-351	131	47	simple	simple	ADJ
ijassa-351	131	48	logistic	logistic	ADJ
ijassa-351	131	49	regression	regression	NOUN
ijassa-351	131	50	,	,	PUNCT
ijassa-351	131	51	j48	j48	ADJ
ijassa-351	131	52	and	and	CCONJ
ijassa-351	131	53	multilayer	multilayer	ADJ
ijassa-351	131	54	perceptron	perceptron	PROPN
ijassa-351	131	55	techniques	technique	NOUN
ijassa-351	131	56	with	with	ADP
ijassa-351	131	57	and	and	CCONJ
ijassa-351	131	58	without	without	ADP
ijassa-351	131	59	using	use	VERB
ijassa-351	131	60	attribute	attribute	NOUN
ijassa-351	131	61	ranking	ranking	NOUN
ijassa-351	131	62	methods	method	NOUN
ijassa-351	131	63	.	.	PUNCT
ijassa-351	132	1	also	also	ADV
ijassa-351	132	2	,	,	PUNCT
ijassa-351	132	3	it	it	PRON
ijassa-351	132	4	shows	show	VERB
ijassa-351	132	5	the	the	DET
ijassa-351	132	6	f	f	PROPN
ijassa-351	132	7	measure	measure	NOUN
ijassa-351	132	8	,	,	PUNCT
ijassa-351	132	9	roc	roc	PROPN
ijassa-351	132	10	curve	curve	NOUN
ijassa-351	132	11	of	of	ADP
ijassa-351	132	12	boosted	boosted	ADJ
ijassa-351	132	13	naïve	naïve	ADJ
ijassa-351	132	14	bayes	bayes	NOUN
ijassa-351	132	15	,	,	PUNCT
ijassa-351	132	16	boosted	boost	VERB
ijassa-351	132	17	simple	simple	ADJ
ijassa-351	132	18	logistic	logistic	ADJ
ijassa-351	132	19	regression	regression	NOUN
ijassa-351	132	20	,	,	PUNCT
ijassa-351	132	21	boosted	boost	VERB
ijassa-351	132	22	j48	j48	PROPN
ijassa-351	132	23	,	,	PUNCT
ijassa-351	132	24	boosted	boost	VERB
ijassa-351	132	25	multilayer	multilayer	ADJ
ijassa-351	132	26	perceptron	perceptron	NOUN
ijassa-351	132	27	for	for	ADP
ijassa-351	132	28	prediction	prediction	NOUN
ijassa-351	132	29	of	of	ADP
ijassa-351	132	30	post	post	ADJ
ijassa-351	132	31	-	-	ADJ
ijassa-351	132	32	operative	operative	ADJ
ijassa-351	132	33	life	life	NOUN
ijassa-351	132	34	expectancy	expectancy	NOUN
ijassa-351	132	35	after	after	ADP
ijassa-351	132	36	thoracic	thoracic	NOUN
ijassa-351	132	37	surgery	surgery	NOUN
ijassa-351	132	38	.	.	PUNCT
ijassa-351	133	1	results	result	NOUN
ijassa-351	133	2	show	show	VERB
ijassa-351	133	3	that	that	SCONJ
ijassa-351	133	4	applying	apply	VERB
ijassa-351	133	5	naïve	naïve	ADJ
ijassa-351	133	6	bayes	baye	NOUN
ijassa-351	133	7	without	without	ADP
ijassa-351	133	8	using	use	VERB
ijassa-351	133	9	ranking	ranking	ADJ
ijassa-351	133	10	methods	method	NOUN
ijassa-351	133	11	gives	give	VERB
ijassa-351	133	12	the	the	DET
ijassa-351	133	13	better	well	ADJ
ijassa-351	133	14	f	f	NOUN
ijassa-351	133	15	measure	measure	NOUN
ijassa-351	133	16	than	than	ADP
ijassa-351	133	17	using	use	VERB
ijassa-351	133	18	the	the	DET
ijassa-351	133	19	three	three	NUM
ijassa-351	133	20	ranking	ranking	ADJ
ijassa-351	133	21	methods	method	NOUN
ijassa-351	133	22	before	before	ADP
ijassa-351	133	23	applying	apply	VERB
ijassa-351	133	24	naïve	naïve	ADJ
ijassa-351	133	25	bayes	baye	NOUN
ijassa-351	133	26	and	and	CCONJ
ijassa-351	133	27	with	with	ADP
ijassa-351	133	28	boosted	boost	VERB
ijassa-351	133	29	naïve	naïve	ADJ
ijassa-351	133	30	bayes	baye	NOUN
ijassa-351	133	31	,	,	PUNCT
ijassa-351	133	32	but	but	CCONJ
ijassa-351	133	33	,	,	PUNCT
ijassa-351	133	34	boosted	boost	VERB
ijassa-351	133	35	naïve	naïve	ADJ
ijassa-351	133	36	bayes	bayes	PROPN
ijassa-351	133	37	gives	give	VERB
ijassa-351	133	38	the	the	DET
ijassa-351	133	39	best	good	ADJ
ijassa-351	133	40	roc	roc	PROPN
ijassa-351	133	41	curve	curve	NOUN
ijassa-351	133	42	.	.	PUNCT
ijassa-351	134	1	in	in	ADP
ijassa-351	134	2	the	the	DET
ijassa-351	134	3	case	case	NOUN
ijassa-351	134	4	of	of	ADP
ijassa-351	134	5	applying	apply	VERB
ijassa-351	134	6	simple	simple	ADJ
ijassa-351	134	7	logistic	logistic	NOUN
ijassa-351	134	8	,	,	PUNCT
ijassa-351	134	9	it	it	PRON
ijassa-351	134	10	gives	give	VERB
ijassa-351	134	11	the	the	DET
ijassa-351	134	12	same	same	ADJ
ijassa-351	134	13	results	result	NOUN
ijassa-351	134	14	for	for	ADP
ijassa-351	134	15	the	the	DET
ijassa-351	134	16	f	f	PROPN
ijassa-351	134	17	measure	measure	NOUN
ijassa-351	134	18	in	in	ADP
ijassa-351	134	19	all	all	DET
ijassa-351	134	20	methods	method	NOUN
ijassa-351	134	21	,	,	PUNCT
ijassa-351	134	22	but	but	CCONJ
ijassa-351	134	23	using	use	VERB
ijassa-351	134	24	the	the	DET
ijassa-351	134	25	three	three	NUM
ijassa-351	134	26	ranking	ranking	ADJ
ijassa-351	134	27	methods	method	NOUN
ijassa-351	134	28	gives	give	VERB
ijassa-351	134	29	the	the	DET
ijassa-351	134	30	best	good	ADJ
ijassa-351	134	31	roc	roc	PROPN
ijassa-351	134	32	curve	curve	NOUN
ijassa-351	134	33	.	.	PUNCT
ijassa-351	135	1	in	in	ADP
ijassa-351	135	2	the	the	DET
ijassa-351	135	3	case	case	NOUN
ijassa-351	135	4	of	of	ADP
ijassa-351	135	5	applying	apply	VERB
ijassa-351	135	6	multilayer	multilayer	ADJ
ijassa-351	135	7	perceptron	perceptron	PROPN
ijassa-351	135	8	,	,	PUNCT
ijassa-351	135	9	using	use	VERB
ijassa-351	135	10	su	su	PROPN
ijassa-351	135	11	and	and	CCONJ
ijassa-351	135	12	ig	ig	PROPN
ijassa-351	135	13	ranking	rank	VERB
ijassa-351	135	14	methods	method	NOUN
ijassa-351	135	15	gives	give	VERB
ijassa-351	135	16	the	the	DET
ijassa-351	135	17	best	good	ADJ
ijassa-351	135	18	f	f	NOUN
ijassa-351	135	19	measure	measure	NOUN
ijassa-351	135	20	and	and	CCONJ
ijassa-351	135	21	the	the	DET
ijassa-351	135	22	best	good	ADJ
ijassa-351	135	23	roc	roc	PROPN
ijassa-351	135	24	curve	curve	NOUN
ijassa-351	135	25	.	.	PUNCT
ijassa-351	136	1	in	in	ADP
ijassa-351	136	2	the	the	DET
ijassa-351	136	3	case	case	NOUN
ijassa-351	136	4	of	of	ADP
ijassa-351	136	5	applying	apply	VERB
ijassa-351	136	6	j48	j48	NOUN
ijassa-351	136	7	,	,	PUNCT
ijassa-351	136	8	boosted	boost	VERB
ijassa-351	136	9	j48	j48	PROPN
ijassa-351	136	10	gives	give	VERB
ijassa-351	136	11	the	the	DET
ijassa-351	136	12	best	good	ADJ
ijassa-351	136	13	f	f	NOUN
ijassa-351	136	14	measure	measure	NOUN
ijassa-351	136	15	but	but	CCONJ
ijassa-351	136	16	applying	apply	VERB
ijassa-351	136	17	j48	j48	NOUN
ijassa-351	136	18	with	with	ADP
ijassa-351	136	19	and	and	CCONJ
ijassa-351	136	20	without	without	ADP
ijassa-351	136	21	using	use	VERB
ijassa-351	136	22	ranking	rank	VERB
ijassa-351	136	23	methods	method	NOUN
ijassa-351	136	24	gives	give	VERB
ijassa-351	136	25	better	well	ADJ
ijassa-351	136	26	roc	roc	PROPN
ijassa-351	136	27	curve	curve	NOUN
ijassa-351	136	28	than	than	SCONJ
ijassa-351	136	29	boosted	boost	VERB
ijassa-351	136	30	j48	j48	PROPN
ijassa-351	136	31	.	.	PUNCT
ijassa-351	137	1	improved	improve	VERB
ijassa-351	137	2	prediction	prediction	NOUN
ijassa-351	137	3	of	of	ADP
ijassa-351	137	4	post	post	ADJ
ijassa-351	137	5	-	-	ADJ
ijassa-351	137	6	operative	operative	ADJ
ijassa-351	137	7	life	life	NOUN
ijassa-351	137	8	expectancy	expectancy	NOUN
ijassa-351	137	9	after	after	ADP
ijassa-351	137	10	thoracic	thoracic	NOUN
ijassa-351	137	11	surgery	surgery	NOUN
ijassa-351	137	12	76	76	NUM
ijassa-351	137	13	copyright	copyright	NOUN
ijassa-351	137	14	©	©	PROPN
ijassa-351	137	15	2016	2016	NUM
ijassa-351	137	16	assa	assa	NOUN
ijassa-351	137	17	.	.	PUNCT
ijassa-351	138	1	adv	adv	PROPN
ijassa-351	138	2	.	.	PUNCT
ijassa-351	139	1	in	in	ADP
ijassa-351	139	2	systems	system	NOUN
ijassa-351	139	3	science	science	NOUN
ijassa-351	139	4	and	and	CCONJ
ijassa-351	139	5	appl	appl	NOUN
ijassa-351	139	6	.	.	PUNCT
ijassa-351	140	1	(	(	PUNCT
ijassa-351	140	2	2016	2016	NUM
ijassa-351	140	3	)	)	PUNCT
ijassa-351	140	4	table	table	NOUN
ijassa-351	140	5	4	4	NUM
ijassa-351	140	6	.	.	PUNCT
ijassa-351	140	7	prediction	prediction	NOUN
ijassa-351	140	8	measures	measure	VERB
ijassa-351	140	9	comparison	comparison	NOUN
ijassa-351	140	10	of	of	ADP
ijassa-351	140	11	machine	machine	NOUN
ijassa-351	140	12	learning	learn	VERB
ijassa-351	140	13	techniques	technique	NOUN
ijassa-351	140	14	using	use	VERB
ijassa-351	140	15	thoracic	thoracic	NOUN
ijassa-351	140	16	surgery	surgery	NOUN
ijassa-351	140	17	data	datum	NOUN
ijassa-351	140	18	set	set	VERB
ijassa-351	140	19	ml	ml	ADP
ijassa-351	140	20	techniques	technique	NOUN
ijassa-351	140	21	method	method	NOUN
ijassa-351	140	22	f	f	PROPN
ijassa-351	140	23	measure	measure	NOUN
ijassa-351	140	24	roc	roc	PROPN
ijassa-351	140	25	naïve	naïve	ADJ
ijassa-351	140	26	bayes	bayes	PROPN
ijassa-351	140	27	original	original	ADJ
ijassa-351	141	1	[	[	X
ijassa-351	141	2	10	10	NUM
ijassa-351	141	3	]	]	SYM
ijassa-351	141	4	0.13	0.13	NUM
ijassa-351	141	5	0.68	0.68	NUM
ijassa-351	141	6	boosted	boost	VERB
ijassa-351	141	7	[	[	X
ijassa-351	141	8	10	10	NUM
ijassa-351	141	9	]	]	SYM
ijassa-351	141	10	0.12	0.12	NUM
ijassa-351	141	11	0.60	0.60	NUM
ijassa-351	141	12	rf	rf	NUM
ijassa-351	141	13	0.06	0.06	NUM
ijassa-351	141	14	0.66	0.66	NUM
ijassa-351	141	15	su	su	PROPN
ijassa-351	141	16	0.06	0.06	NUM
ijassa-351	141	17	0.66	0.66	NUM
ijassa-351	141	18	ig	ig	PROPN
ijassa-351	141	19	0.07	0.07	NUM
ijassa-351	141	20	0.67	0.67	NUM
ijassa-351	141	21	simple	simple	ADJ
ijassa-351	141	22	logistic	logistic	ADJ
ijassa-351	141	23	original	original	ADJ
ijassa-351	141	24	[	[	X
ijassa-351	141	25	10	10	NUM
ijassa-351	141	26	]	]	SYM
ijassa-351	141	27	0.00	0.00	NUM
ijassa-351	141	28	0.53	0.53	NUM
ijassa-351	141	29	boosted	boost	VERB
ijassa-351	141	30	[	[	X
ijassa-351	141	31	10	10	NUM
ijassa-351	141	32	]	]	PUNCT
ijassa-351	141	33	0.00	0.00	NUM
ijassa-351	141	34	0.61	0.61	NUM
ijassa-351	141	35	rf	rf	NUM
ijassa-351	141	36	0.00	0.00	NUM
ijassa-351	141	37	0.50	0.50	NUM
ijassa-351	141	38	su	su	NOUN
ijassa-351	141	39	0.00	0.00	NUM
ijassa-351	141	40	0.50	0.50	NUM
ijassa-351	141	41	ig	ig	PROPN
ijassa-351	141	42	0.00	0.00	NUM
ijassa-351	141	43	0.50	0.50	NUM
ijassa-351	141	44	multilayer	multilayer	NOUN
ijassa-351	141	45	perceptron	perceptron	PROPN
ijassa-351	141	46	original	original	PROPN
ijassa-351	142	1	[	[	X
ijassa-351	142	2	10	10	NUM
ijassa-351	142	3	]	]	SYM
ijassa-351	142	4	0.22	0.22	NUM
ijassa-351	142	5	0.60	0.60	NUM
ijassa-351	142	6	boosted	boost	VERB
ijassa-351	142	7	[	[	X
ijassa-351	142	8	10	10	NUM
ijassa-351	142	9	]	]	PUNCT
ijassa-351	142	10	0.18	0.18	NUM
ijassa-351	142	11	0.56	0.56	NUM
ijassa-351	142	12	rf	rf	NUM
ijassa-351	142	13	0.20	0.20	NUM
ijassa-351	142	14	0.58	0.58	NUM
ijassa-351	142	15	su	su	PROPN
ijassa-351	142	16	0.24	0.24	NUM
ijassa-351	142	17	0.55	0.55	NUM
ijassa-351	142	18	ig	ig	PROPN
ijassa-351	142	19	0.24	0.24	NUM
ijassa-351	142	20	0.55	0.55	NUM
ijassa-351	142	21	j48	j48	PROPN
ijassa-351	142	22	original	original	ADJ
ijassa-351	143	1	[	[	X
ijassa-351	143	2	10	10	NUM
ijassa-351	143	3	]	]	SYM
ijassa-351	143	4	0.00	0.00	NUM
ijassa-351	143	5	0.50	0.50	NUM
ijassa-351	143	6	boosted	boost	VERB
ijassa-351	143	7	[	[	X
ijassa-351	143	8	10	10	NUM
ijassa-351	143	9	]	]	PUNCT
ijassa-351	143	10	0.18	0.18	NUM
ijassa-351	143	11	0.61	0.61	NUM
ijassa-351	143	12	rf	rf	NUM
ijassa-351	143	13	0.02	0.02	NUM
ijassa-351	143	14	0.50	0.50	NUM
ijassa-351	143	15	su	su	NOUN
ijassa-351	143	16	0.00	0.00	NUM
ijassa-351	143	17	0.50	0.50	NUM
ijassa-351	143	18	ig	ig	PROPN
ijassa-351	143	19	0.00	0.00	NUM
ijassa-351	143	20	0.51	0.51	NUM
ijassa-351	143	21	table	table	NOUN
ijassa-351	143	22	5	5	NUM
ijassa-351	143	23	.	.	PUNCT
ijassa-351	144	1	shows	show	VERB
ijassa-351	144	2	the	the	DET
ijassa-351	144	3	tpr	tpr	NOUN
ijassa-351	144	4	,	,	PUNCT
ijassa-351	144	5	tnr	tnr	PROPN
ijassa-351	144	6	,	,	PUNCT
ijassa-351	144	7	and	and	CCONJ
ijassa-351	144	8	gmean	gmean	ADJ
ijassa-351	144	9	for	for	ADP
ijassa-351	144	10	support	support	NOUN
ijassa-351	144	11	vector	vector	NOUN
ijassa-351	144	12	machine	machine	NOUN
ijassa-351	144	13	after	after	ADP
ijassa-351	144	14	applying	apply	VERB
ijassa-351	144	15	the	the	DET
ijassa-351	144	16	three	three	NUM
ijassa-351	144	17	attribute	attribute	NOUN
ijassa-351	144	18	ranking	ranking	NOUN
ijassa-351	144	19	and	and	CCONJ
ijassa-351	144	20	selection	selection	NOUN
ijassa-351	144	21	methods	method	NOUN
ijassa-351	144	22	and	and	CCONJ
ijassa-351	144	23	boosted	boost	VERB
ijassa-351	144	24	support	support	NOUN
ijassa-351	144	25	vector	vector	NOUN
ijassa-351	144	26	machine	machine	NOUN
ijassa-351	144	27	.	.	PUNCT
ijassa-351	145	1	the	the	DET
ijassa-351	145	2	rf	rf	NOUN
ijassa-351	145	3	gives	give	VERB
ijassa-351	145	4	the	the	DET
ijassa-351	145	5	best	good	ADJ
ijassa-351	145	6	prediction	prediction	NOUN
ijassa-351	145	7	quality	quality	NOUN
ijassa-351	145	8	where	where	SCONJ
ijassa-351	145	9	it	it	PRON
ijassa-351	145	10	has	have	VERB
ijassa-351	145	11	the	the	DET
ijassa-351	145	12	higher	high	ADJ
ijassa-351	145	13	gmean	gmean	ADJ
ijassa-351	145	14	value	value	NOUN
ijassa-351	145	15	.	.	PUNCT
ijassa-351	146	1	it	it	PRON
ijassa-351	146	2	shows	show	VERB
ijassa-351	146	3	also	also	ADV
ijassa-351	146	4	that	that	SCONJ
ijassa-351	146	5	the	the	DET
ijassa-351	146	6	proposed	propose	VERB
ijassa-351	146	7	methods	method	NOUN
ijassa-351	146	8	give	give	VERB
ijassa-351	146	9	better	well	ADJ
ijassa-351	146	10	gmean	gmean	ADJ
ijassa-351	146	11	and	and	CCONJ
ijassa-351	146	12	tnr	tnr	PROPN
ijassa-351	146	13	than	than	SCONJ
ijassa-351	146	14	boosted	boost	VERB
ijassa-351	146	15	svm	svm	NOUN
ijassa-351	146	16	but	but	CCONJ
ijassa-351	146	17	boosted	boost	VERB
ijassa-351	146	18	svm	svm	PROPN
ijassa-351	146	19	gives	give	VERB
ijassa-351	146	20	better	well	ADJ
ijassa-351	146	21	tpr	tpr	NOUN
ijassa-351	146	22	.	.	PUNCT
ijassa-351	146	23	table	table	NOUN
ijassa-351	146	24	5	5	NUM
ijassa-351	146	25	.	.	PUNCT
ijassa-351	146	26	performance	performance	NOUN
ijassa-351	146	27	evaluation	evaluation	NOUN
ijassa-351	146	28	of	of	ADP
ijassa-351	146	29	boosted	boosted	ADJ
ijassa-351	146	30	svm	svm	NOUN
ijassa-351	146	31	vs.	vs.	ADP
ijassa-351	146	32	svm	svm	NOUN
ijassa-351	146	33	with	with	ADP
ijassa-351	146	34	ranking	ranking	ADJ
ijassa-351	146	35	methods	method	NOUN
ijassa-351	146	36	method	method	PROPN
ijassa-351	146	37	tpr	tpr	PROPN
ijassa-351	146	38	tnr	tnr	PROPN
ijassa-351	146	39	gmean	gmean	PROPN
ijassa-351	146	40	boosted	boost	VERB
ijassa-351	146	41	svm(bsi	svm(bsi	NOUN
ijassa-351	146	42	)	)	PUNCT
ijassa-351	147	1	[	[	X
ijassa-351	147	2	9	9	NUM
ijassa-351	147	3	]	]	PUNCT
ijassa-351	147	4	60.00	60.00	NUM
ijassa-351	147	5	72.00	72.00	NUM
ijassa-351	147	6	65.73	65.73	NUM
ijassa-351	147	7	svm	svm	NOUN
ijassa-351	147	8	(	(	PUNCT
ijassa-351	147	9	ig	ig	PROPN
ijassa-351	147	10	)	)	PUNCT
ijassa-351	147	11	44.30	44.30	NUM
ijassa-351	147	12	99.80	99.80	NUM
ijassa-351	147	13	66.49	66.49	NUM
ijassa-351	147	14	svm	svm	PROPN
ijassa-351	147	15	(	(	PUNCT
ijassa-351	147	16	su	su	PROPN
ijassa-351	147	17	)	)	PUNCT
ijassa-351	147	18	44.30	44.30	NUM
ijassa-351	147	19	99.80	99.80	NUM
ijassa-351	147	20	66.49	66.49	NUM
ijassa-351	147	21	svm	svm	PROPN
ijassa-351	147	22	(	(	PUNCT
ijassa-351	147	23	rf	rf	NOUN
ijassa-351	147	24	)	)	PUNCT
ijassa-351	147	25	51.40	51.40	NUM
ijassa-351	147	26	99.80	99.80	NUM
ijassa-351	147	27	71.62	71.62	NUM
ijassa-351	147	28	6	6	NUM
ijassa-351	147	29	.	.	PUNCT
ijassa-351	147	30	conclusion	conclusion	NOUN
ijassa-351	147	31	in	in	ADP
ijassa-351	147	32	this	this	DET
ijassa-351	147	33	study	study	NOUN
ijassa-351	147	34	,	,	PUNCT
ijassa-351	147	35	the	the	DET
ijassa-351	147	36	quality	quality	NOUN
ijassa-351	147	37	of	of	ADP
ijassa-351	147	38	three	three	NUM
ijassa-351	147	39	attribute	attribute	NOUN
ijassa-351	147	40	ranking	ranking	NOUN
ijassa-351	147	41	and	and	CCONJ
ijassa-351	147	42	selection	selection	NOUN
ijassa-351	147	43	methods	method	NOUN
ijassa-351	147	44	has	have	AUX
ijassa-351	147	45	been	be	AUX
ijassa-351	147	46	evaluated	evaluate	VERB
ijassa-351	147	47	to	to	PART
ijassa-351	147	48	improve	improve	VERB
ijassa-351	147	49	the	the	DET
ijassa-351	147	50	prediction	prediction	NOUN
ijassa-351	147	51	for	for	ADP
ijassa-351	147	52	life	life	NOUN
ijassa-351	147	53	expectancy	expectancy	NOUN
ijassa-351	147	54	of	of	ADP
ijassa-351	147	55	lung	lung	NOUN
ijassa-351	147	56	cancer	cancer	NOUN
ijassa-351	147	57	patients	patient	NOUN
ijassa-351	147	58	after	after	ADP
ijassa-351	147	59	thoracic	thoracic	NOUN
ijassa-351	147	60	surgery	surgery	NOUN
ijassa-351	147	61	.	.	PUNCT
ijassa-351	148	1	five	five	NUM
ijassa-351	148	2	machine	machine	NOUN
ijassa-351	148	3	learning	learn	VERB
ijassa-351	148	4	techniques	technique	NOUN
ijassa-351	148	5	before	before	ADV
ijassa-351	148	6	and	and	CCONJ
ijassa-351	148	7	after	after	ADP
ijassa-351	148	8	applying	apply	VERB
ijassa-351	148	9	the	the	DET
ijassa-351	148	10	attribute	attribute	NOUN
ijassa-351	148	11	ranking	ranking	NOUN
ijassa-351	148	12	and	and	CCONJ
ijassa-351	148	13	selection	selection	NOUN
ijassa-351	148	14	methods	method	NOUN
ijassa-351	148	15	have	have	AUX
ijassa-351	148	16	been	be	AUX
ijassa-351	148	17	compared	compare	VERB
ijassa-351	148	18	with	with	ADP
ijassa-351	148	19	their	their	PRON
ijassa-351	148	20	boosted	boost	VERB
ijassa-351	148	21	versions	version	NOUN
ijassa-351	148	22	.	.	PUNCT
ijassa-351	149	1	the	the	DET
ijassa-351	149	2	results	result	NOUN
ijassa-351	149	3	show	show	VERB
ijassa-351	149	4	that	that	SCONJ
ijassa-351	149	5	boosting	boost	VERB
ijassa-351	149	6	is	be	AUX
ijassa-351	149	7	not	not	PART
ijassa-351	149	8	always	always	ADV
ijassa-351	149	9	the	the	DET
ijassa-351	149	10	better	well	ADJ
ijassa-351	149	11	choice	choice	NOUN
ijassa-351	149	12	where	where	SCONJ
ijassa-351	149	13	attribute	attribute	NOUN
ijassa-351	149	14	ranking	ranking	NOUN
ijassa-351	149	15	and	and	CCONJ
ijassa-351	149	16	selection	selection	NOUN
ijassa-351	149	17	can	can	AUX
ijassa-351	149	18	perform	perform	VERB
ijassa-351	149	19	better	well	ADV
ijassa-351	149	20	in	in	ADP
ijassa-351	149	21	improving	improve	VERB
ijassa-351	149	22	prediction	prediction	NOUN
ijassa-351	149	23	accuracy	accuracy	NOUN
ijassa-351	149	24	.	.	PUNCT
ijassa-351	150	1	other	other	ADJ
ijassa-351	150	2	attribute	attribute	NOUN
ijassa-351	150	3	selection	selection	NOUN
ijassa-351	150	4	and	and	CCONJ
ijassa-351	150	5	machine	machine	NOUN
ijassa-351	150	6	learning	learning	NOUN
ijassa-351	150	7	techniques	technique	NOUN
ijassa-351	150	8	can	can	AUX
ijassa-351	150	9	be	be	AUX
ijassa-351	150	10	introduced	introduce	VERB
ijassa-351	150	11	in	in	ADP
ijassa-351	150	12	the	the	DET
ijassa-351	150	13	future	future	ADJ
ijassa-351	150	14	work	work	NOUN
ijassa-351	150	15	to	to	PART
ijassa-351	150	16	gain	gain	VERB
ijassa-351	150	17	a	a	DET
ijassa-351	150	18	better	well	ADJ
ijassa-351	150	19	prediction	prediction	NOUN
ijassa-351	150	20	model	model	NOUN
ijassa-351	150	21	performance	performance	NOUN
ijassa-351	150	22	of	of	ADP
ijassa-351	150	23	the	the	DET
ijassa-351	150	24	dataset	dataset	NOUN
ijassa-351	150	25	.	.	PUNCT
ijassa-351	151	1	references	reference	NOUN
ijassa-351	151	2	[	[	X
ijassa-351	151	3	1	1	X
ijassa-351	151	4	]	]	PUNCT
ijassa-351	151	5	v.	v.	CCONJ
ijassa-351	151	6	sindhu	sindhu	PROPN
ijassa-351	151	7	,	,	PUNCT
ijassa-351	151	8	s.	s.	PROPN
ijassa-351	151	9	a.	a.	PROPN
ijassa-351	151	10	s.	s.	PROPN
ijassa-351	151	11	prabha	prabha	PROPN
ijassa-351	151	12	,	,	PUNCT
ijassa-351	151	13	s.	s.	PROPN
ijassa-351	151	14	veni	veni	PROPN
ijassa-351	151	15	,	,	PUNCT
ijassa-351	151	16	and	and	CCONJ
ijassa-351	151	17	m.	m.	NOUN
ijassa-351	151	18	hemalatha	hemalatha	NOUN
ijassa-351	151	19	,	,	PUNCT
ijassa-351	151	20	“	"	PUNCT
ijassa-351	151	21	thoracic	thoracic	NOUN
ijassa-351	151	22	surgery	surgery	NOUN
ijassa-351	151	23	analysis	analysis	NOUN
ijassa-351	151	24	using	use	VERB
ijassa-351	151	25	data	datum	NOUN
ijassa-351	151	26	mining	mining	NOUN
ijassa-351	151	27	techniques	technique	NOUN
ijassa-351	151	28	”	"	PUNCT
ijassa-351	151	29	,	,	PUNCT
ijassa-351	151	30	international	international	ADJ
ijassa-351	151	31	journal	journal	NOUN
ijassa-351	151	32	of	of	ADP
ijassa-351	151	33	computer	computer	NOUN
ijassa-351	151	34	technology	technology	NOUN
ijassa-351	151	35	&	&	CCONJ
ijassa-351	151	36	applications	application	NOUN
ijassa-351	151	37	,	,	PUNCT
ijassa-351	151	38	vol	vol	NOUN
ijassa-351	151	39	.	.	PROPN
ijassa-351	151	40	5	5	NUM
ijassa-351	151	41	,	,	PUNCT
ijassa-351	151	42	pp	pp	ADV
ijassa-351	151	43	578	578	NUM
ijassa-351	151	44	-	-	SYM
ijassa-351	151	45	586	586	NUM
ijassa-351	151	46	,	,	PUNCT
ijassa-351	151	47	may	may	AUX
ijassa-351	151	48	,	,	PUNCT
ijassa-351	151	49	2014	2014	NUM
ijassa-351	151	50	[	[	X
ijassa-351	151	51	2	2	NUM
ijassa-351	151	52	]	]	PUNCT
ijassa-351	151	53	konstantina	konstantina	PROPN
ijassa-351	151	54	kourou	kourou	PROPN
ijassa-351	151	55	,	,	PUNCT
ijassa-351	151	56	themis	themis	PROPN
ijassa-351	151	57	p.	p.	PROPN
ijassa-351	151	58	exarchos	exarchos	PROPN
ijassa-351	151	59	,	,	PUNCT
ijassa-351	151	60	konstantinos	konstantinos	PROPN
ijassa-351	151	61	p.	p.	PROPN
ijassa-351	151	62	exarchos	exarchos	PROPN
ijassa-351	151	63	,	,	PUNCT
ijassa-351	151	64	michalis	michali	VERB
ijassa-351	151	65	v.	v.	ADP
ijassa-351	151	66	karamouzis	karamouzi	NOUN
ijassa-351	151	67	,	,	PUNCT
ijassa-351	151	68	dimitrios	dimitrios	PROPN
ijassa-351	151	69	i.	i.	PROPN
ijassa-351	151	70	fotiadisa	fotiadisa	PROPN
ijassa-351	151	71	,	,	PUNCT
ijassa-351	151	72	“	"	PUNCT
ijassa-351	151	73	machine	machine	NOUN
ijassa-351	151	74	learning	learning	NOUN
ijassa-351	151	75	applications	application	NOUN
ijassa-351	151	76	in	in	ADP
ijassa-351	151	77	cancer	cancer	NOUN
ijassa-351	151	78	prognosis	prognosis	NOUN
ijassa-351	151	79	and	and	CCONJ
ijassa-351	151	80	prediction	prediction	NOUN
ijassa-351	151	81	”	"	PUNCT
ijassa-351	151	82	,	,	PUNCT
ijassa-351	151	83	computational	computational	ADJ
ijassa-351	151	84	and	and	CCONJ
ijassa-351	151	85	structural	structural	ADJ
ijassa-351	151	86	biotechnology	biotechnology	NOUN
ijassa-351	151	87	journal	journal	NOUN
ijassa-351	151	88	,	,	PUNCT
ijassa-351	151	89	vol	vol	NOUN
ijassa-351	151	90	13	13	NUM
ijassa-351	151	91	,	,	PUNCT
ijassa-351	151	92	pp	pp	ADV
ijassa-351	151	93	8	8	NUM
ijassa-351	151	94	-	-	SYM
ijassa-351	151	95	17	17	NUM
ijassa-351	151	96	,	,	PUNCT
ijassa-351	151	97	2015	2015	NUM
ijassa-351	151	98	.	.	PUNCT
ijassa-351	152	1	http://www.sciencedirect.com/science/article/pii/s2001037014000464	http://www.sciencedirect.com/science/article/pii/s2001037014000464	ADP
ijassa-351	152	2	http://www.sciencedirect.com/science/article/pii/s2001037014000464	http://www.sciencedirect.com/science/article/pii/s2001037014000464	ADP
ijassa-351	152	3	http://www.sciencedirect.com/science/article/pii/s2001037014000464	http://www.sciencedirect.com/science/article/pii/s2001037014000464	ADP
ijassa-351	152	4	http://www.sciencedirect.com/science/article/pii/s2001037014000464	http://www.sciencedirect.com/science/article/pii/s2001037014000464	ADP
ijassa-351	152	5	http://www.sciencedirect.com/science/article/pii/s2001037014000464	http://www.sciencedirect.com/science/article/pii/s2001037014000464	ADP
ijassa-351	152	6	http://www.sciencedirect.com/science/article/pii/s2001037014000464	http://www.sciencedirect.com/science/article/pii/s2001037014000464	ADP
ijassa-351	152	7	http://www.sciencedirect.com/science/article/pii/s2001037014000464	http://www.sciencedirect.com/science/article/pii/s2001037014000464	ADP
ijassa-351	152	8	http://www.sciencedirect.com/science/journal/20010370	http://www.sciencedirect.com/science/journal/20010370	NUM
ijassa-351	152	9	77	77	NUM
ijassa-351	152	10	a.	a.	NOUN
ijassa-351	152	11	s.	s.	PROPN
ijassa-351	152	12	desuky	desuky	PROPN
ijassa-351	152	13	,	,	PUNCT
ijassa-351	152	14	l.m	l.m	PROPN
ijassa-351	152	15	.	.	PROPN
ijassa-351	152	16	el	el	PROPN
ijassa-351	152	17	bakrawy	bakrawy	PROPN
ijassa-351	152	18	copyright	copyright	NOUN
ijassa-351	152	19	©	©	PROPN
ijassa-351	152	20	2016	2016	NUM
ijassa-351	152	21	assa	assa	NOUN
ijassa-351	152	22	.	.	PUNCT
ijassa-351	153	1	adv	adv	PROPN
ijassa-351	153	2	.	.	PUNCT
ijassa-351	154	1	in	in	ADP
ijassa-351	154	2	systems	system	NOUN
ijassa-351	154	3	science	science	NOUN
ijassa-351	154	4	and	and	CCONJ
ijassa-351	154	5	appl	appl	NOUN
ijassa-351	154	6	.	.	PUNCT
ijassa-351	155	1	(	(	PUNCT
ijassa-351	155	2	2016	2016	NUM
ijassa-351	155	3	)	)	PUNCT
ijassa-351	156	1	[	[	X
ijassa-351	156	2	3	3	X
ijassa-351	156	3	]	]	X
ijassa-351	156	4	kwetishe	kwetishe	PROPN
ijassa-351	156	5	joro	joro	PROPN
ijassa-351	156	6	danjumal	danjumal	PROPN
ijassa-351	156	7	,	,	PUNCT
ijassa-351	156	8	“	"	PUNCT
ijassa-351	156	9	performance	performance	NOUN
ijassa-351	156	10	evaluation	evaluation	NOUN
ijassa-351	156	11	of	of	ADP
ijassa-351	156	12	machine	machine	NOUN
ijassa-351	156	13	learning	learn	VERB
ijassa-351	156	14	algorithms	algorithm	NOUN
ijassa-351	156	15	in	in	ADP
ijassa-351	156	16	post	post	ADJ
ijassa-351	156	17	-	-	ADJ
ijassa-351	156	18	operative	operative	ADJ
ijassa-351	156	19	life	life	NOUN
ijassa-351	156	20	expectancy	expectancy	NOUN
ijassa-351	156	21	in	in	ADP
ijassa-351	156	22	the	the	DET
ijassa-351	156	23	lung	lung	NOUN
ijassa-351	156	24	cancer	cancer	NOUN
ijassa-351	156	25	patients	patient	NOUN
ijassa-351	156	26	”	"	PUNCT
ijassa-351	156	27	,	,	PUNCT
ijassa-351	156	28	international	international	ADJ
ijassa-351	156	29	journal	journal	NOUN
ijassa-351	156	30	of	of	ADP
ijassa-351	156	31	computer	computer	NOUN
ijassa-351	156	32	science	science	NOUN
ijassa-351	156	33	issues	issue	NOUN
ijassa-351	156	34	,	,	PUNCT
ijassa-351	156	35	vol	vol	NOUN
ijassa-351	156	36	.	.	PROPN
ijassa-351	157	1	12	12	NUM
ijassa-351	157	2	,	,	PUNCT
ijassa-351	157	3	no	no	INTJ
ijassa-351	157	4	.	.	NOUN
ijassa-351	157	5	2	2	NUM
ijassa-351	157	6	,	,	PUNCT
ijassa-351	157	7	pp	pp	ADJ
ijassa-351	157	8	.	.	PUNCT
ijassa-351	158	1	189	189	NUM
ijassa-351	158	2	-	-	SYM
ijassa-351	158	3	199	199	NUM
ijassa-351	158	4	,	,	PUNCT
ijassa-351	158	5	2015	2015	NUM
ijassa-351	158	6	[	[	X
ijassa-351	158	7	4	4	X
ijassa-351	158	8	]	]	X
ijassa-351	158	9	joseph	joseph	PROPN
ijassa-351	158	10	a.	a.	PROPN
ijassa-351	158	11	cruz	cruz	PROPN
ijassa-351	158	12	,	,	PUNCT
ijassa-351	158	13	david	david	PROPN
ijassa-351	158	14	s.	s.	PROPN
ijassa-351	158	15	wishart	wishart	PROPN
ijassa-351	158	16	,	,	PUNCT
ijassa-351	158	17	“	"	PUNCT
ijassa-351	158	18	applications	application	NOUN
ijassa-351	158	19	of	of	ADP
ijassa-351	158	20	machine	machine	NOUN
ijassa-351	158	21	learning	learn	VERB
ijassa-351	158	22	in	in	ADP
ijassa-351	158	23	cancer	cancer	NOUN
ijassa-351	158	24	prediction	prediction	NOUN
ijassa-351	158	25	and	and	CCONJ
ijassa-351	158	26	prognosis	prognosis	NOUN
ijassa-351	158	27	”	"	PUNCT
ijassa-351	158	28	,	,	PUNCT
ijassa-351	158	29	cancer	cancer	NOUN
ijassa-351	158	30	informatics	informatic	NOUN
ijassa-351	158	31	,	,	PUNCT
ijassa-351	158	32	vol	vol	NOUN
ijassa-351	158	33	.	.	PROPN
ijassa-351	158	34	2	2	NUM
ijassa-351	158	35	,	,	PUNCT
ijassa-351	158	36	pp	pp	ADJ
ijassa-351	158	37	.	.	PUNCT
ijassa-351	159	1	59–77	59–77	NUM
ijassa-351	159	2	,	,	PUNCT
ijassa-351	159	3	2006	2006	NUM
ijassa-351	160	1	[	[	X
ijassa-351	160	2	5	5	NUM
ijassa-351	160	3	]	]	X
ijassa-351	160	4	mehdi	mehdi	PROPN
ijassa-351	160	5	naseriparsa	naseriparsa	PROPN
ijassa-351	160	6	,	,	PUNCT
ijassa-351	160	7	amir	amir	NOUN
ijassa-351	160	8	-	-	PUNCT
ijassa-351	160	9	masoudbidgoli	masoudbidgoli	NOUN
ijassa-351	160	10	,	,	PUNCT
ijassa-351	160	11	tourajvaraee	tourajvaraee	ADJ
ijassa-351	160	12	,	,	PUNCT
ijassa-351	160	13	“	"	PUNCT
ijassa-351	160	14	a	a	DET
ijassa-351	160	15	hybrid	hybrid	ADJ
ijassa-351	160	16	feature	feature	NOUN
ijassa-351	160	17	selection	selection	NOUN
ijassa-351	160	18	method	method	NOUN
ijassa-351	160	19	to	to	PART
ijassa-351	160	20	improve	improve	VERB
ijassa-351	160	21	performance	performance	NOUN
ijassa-351	160	22	of	of	ADP
ijassa-351	160	23	a	a	DET
ijassa-351	160	24	group	group	NOUN
ijassa-351	160	25	of	of	ADP
ijassa-351	160	26	classification	classification	NOUN
ijassa-351	160	27	algorithms	algorithm	NOUN
ijassa-351	160	28	”	"	PUNCT
ijassa-351	160	29	,	,	PUNCT
ijassa-351	160	30	international	international	ADJ
ijassa-351	160	31	journal	journal	NOUN
ijassa-351	160	32	of	of	ADP
ijassa-351	160	33	computer	computer	NOUN
ijassa-351	160	34	applications	application	NOUN
ijassa-351	160	35	,	,	PUNCT
ijassa-351	160	36	vol	vol	NOUN
ijassa-351	160	37	.	.	PROPN
ijassa-351	160	38	69	69	NUM
ijassa-351	160	39	,	,	PUNCT
ijassa-351	160	40	no	no	DET
ijassa-351	160	41	17	17	NUM
ijassa-351	160	42	,	,	PUNCT
ijassa-351	160	43	pp	pp	ADV
ijassa-351	160	44	28	28	NUM
ijassa-351	160	45	-	-	SYM
ijassa-351	160	46	35	35	NUM
ijassa-351	160	47	,	,	PUNCT
ijassa-351	160	48	2013	2013	NUM
ijassa-351	161	1	[	[	X
ijassa-351	161	2	6	6	NUM
ijassa-351	161	3	]	]	PUNCT
ijassa-351	161	4	pinar	pinar	PROPN
ijassa-351	161	5	yildirim	yildirim	PROPN
ijassa-351	161	6	,	,	PUNCT
ijassa-351	161	7	“	"	PUNCT
ijassa-351	161	8	filter	filter	NOUN
ijassa-351	161	9	based	base	VERB
ijassa-351	161	10	feature	feature	NOUN
ijassa-351	161	11	selection	selection	NOUN
ijassa-351	161	12	methods	method	NOUN
ijassa-351	161	13	for	for	ADP
ijassa-351	161	14	prediction	prediction	NOUN
ijassa-351	161	15	of	of	ADP
ijassa-351	161	16	risks	risk	NOUN
ijassa-351	161	17	in	in	ADP
ijassa-351	161	18	hepatitis	hepatitis	NOUN
ijassa-351	161	19	disease	disease	NOUN
ijassa-351	161	20	”	"	PUNCT
ijassa-351	161	21	,	,	PUNCT
ijassa-351	161	22	international	international	ADJ
ijassa-351	161	23	journal	journal	NOUN
ijassa-351	161	24	of	of	ADP
ijassa-351	161	25	machine	machine	NOUN
ijassa-351	161	26	learning	learning	NOUN
ijassa-351	161	27	and	and	CCONJ
ijassa-351	161	28	computing	computing	NOUN
ijassa-351	161	29	,	,	PUNCT
ijassa-351	161	30	vol	vol	NOUN
ijassa-351	161	31	.	.	PROPN
ijassa-351	161	32	5	5	NUM
ijassa-351	161	33	,	,	PUNCT
ijassa-351	161	34	no	no	INTJ
ijassa-351	161	35	.	.	NOUN
ijassa-351	161	36	4	4	NUM
ijassa-351	161	37	,	,	PUNCT
ijassa-351	161	38	pp	pp	ADJ
ijassa-351	161	39	.	.	PUNCT
ijassa-351	162	1	258	258	NUM
ijassa-351	162	2	-	-	SYM
ijassa-351	162	3	263	263	NUM
ijassa-351	162	4	,	,	PUNCT
ijassa-351	162	5	august	august	PROPN
ijassa-351	162	6	2015	2015	NUM
ijassa-351	162	7	[	[	X
ijassa-351	162	8	7	7	X
ijassa-351	162	9	]	]	X
ijassa-351	162	10	samina	samina	PROPN
ijassa-351	162	11	khalid	khalid	PROPN
ijassa-351	162	12	,	,	PUNCT
ijassa-351	162	13	tehmina	tehmina	PROPN
ijassa-351	162	14	khalil	khalil	PROPN
ijassa-351	162	15	,	,	PUNCT
ijassa-351	162	16	shamila	shamila	ADJ
ijassa-351	162	17	nasreen	nasreen	ADP
ijassa-351	162	18	,	,	PUNCT
ijassa-351	162	19	“	"	PUNCT
ijassa-351	162	20	a	a	DET
ijassa-351	162	21	survey	survey	NOUN
ijassa-351	162	22	of	of	ADP
ijassa-351	162	23	feature	feature	NOUN
ijassa-351	162	24	selection	selection	NOUN
ijassa-351	162	25	and	and	CCONJ
ijassa-351	162	26	feature	feature	NOUN
ijassa-351	162	27	extraction	extraction	NOUN
ijassa-351	162	28	techniques	technique	NOUN
ijassa-351	162	29	in	in	ADP
ijassa-351	162	30	machine	machine	NOUN
ijassa-351	162	31	learning	learning	NOUN
ijassa-351	162	32	”	"	PUNCT
ijassa-351	162	33	,	,	PUNCT
ijassa-351	162	34	science	science	NOUN
ijassa-351	162	35	and	and	CCONJ
ijassa-351	162	36	information	information	NOUN
ijassa-351	162	37	conference	conference	NOUN
ijassa-351	162	38	(	(	PUNCT
ijassa-351	162	39	sai	sai	NOUN
ijassa-351	162	40	)	)	PUNCT
ijassa-351	162	41	,	,	PUNCT
ijassa-351	162	42	pp	pp	PROPN
ijassa-351	162	43	.	.	PUNCT
ijassa-351	163	1	372	372	NUM
ijassa-351	163	2	-	-	SYM
ijassa-351	163	3	378	378	NUM
ijassa-351	163	4	,	,	PUNCT
ijassa-351	163	5	2014	2014	NUM
ijassa-351	164	1	[	[	X
ijassa-351	164	2	8	8	NUM
ijassa-351	164	3	]	]	X
ijassa-351	164	4	koklu	koklu	PROPN
ijassa-351	164	5	murat	murat	PROPN
ijassa-351	164	6	,	,	PUNCT
ijassa-351	164	7	humar	humar	PROPN
ijassa-351	164	8	kahramanli	kahramanli	PROPN
ijassa-351	164	9	,	,	PUNCT
ijassa-351	164	10	and	and	CCONJ
ijassa-351	164	11	novruz	novruz	PROPN
ijassa-351	164	12	allahverdi	allahverdi	PROPN
ijassa-351	164	13	.	.	PUNCT
ijassa-351	165	1	“	"	PUNCT
ijassa-351	165	2	applications	application	NOUN
ijassa-351	165	3	of	of	ADP
ijassa-351	165	4	rule	rule	NOUN
ijassa-351	165	5	based	base	VERB
ijassa-351	165	6	classification	classification	NOUN
ijassa-351	165	7	techniques	technique	NOUN
ijassa-351	165	8	for	for	ADP
ijassa-351	165	9	thoracic	thoracic	NOUN
ijassa-351	165	10	surgery	surgery	NOUN
ijassa-351	165	11	”	"	PUNCT
ijassa-351	165	12	,	,	PUNCT
ijassa-351	165	13	tiim	tiim	PROPN
ijassa-351	165	14	international	international	ADJ
ijassa-351	165	15	conference	conference	NOUN
ijassa-351	165	16	,	,	PUNCT
ijassa-351	165	17	27	27	NUM
ijassa-351	165	18	-	-	SYM
ijassa-351	165	19	29	29	NUM
ijassa-351	165	20	may	may	PROPN
ijassa-351	165	21	2015	2015	NUM
ijassa-351	165	22	,	,	PUNCT
ijassa-351	165	23	italy	italy	PROPN
ijassa-351	165	24	,	,	PUNCT
ijassa-351	165	25	pp	pp	ADP
ijassa-351	165	26	1991	1991	NUM
ijassa-351	165	27	-	-	SYM
ijassa-351	165	28	1998	1998	NUM
ijassa-351	165	29	.	.	PUNCT
ijassa-351	166	1	[	[	X
ijassa-351	166	2	9	9	NUM
ijassa-351	166	3	]	]	PUNCT
ijassa-351	166	4	m.	m.	NOUN
ijassa-351	166	5	zięba	zięba	PROPN
ijassa-351	166	6	,	,	PUNCT
ijassa-351	166	7	j	j	PROPN
ijassa-351	166	8	.m	.m	NOUN
ijassa-351	166	9	.	.	PUNCT
ijassa-351	167	1	tomczak	tomczak	NOUN
ijassa-351	167	2	,	,	PUNCT
ijassa-351	167	3	m.	m.	NOUN
ijassa-351	167	4	lubicz	lubicz	NOUN
ijassa-351	167	5	,	,	PUNCT
ijassa-351	167	6	and	and	CCONJ
ijassa-351	167	7	j.	j.	PROPN
ijassa-351	167	8	świątek	świątek	PROPN
ijassa-351	167	9	,	,	PUNCT
ijassa-351	167	10	“	"	PUNCT
ijassa-351	167	11	boosted	boost	VERB
ijassa-351	167	12	svm	svm	NOUN
ijassa-351	167	13	for	for	ADP
ijassa-351	167	14	extracting	extract	VERB
ijassa-351	167	15	rules	rule	NOUN
ijassa-351	167	16	from	from	ADP
ijassa-351	167	17	imbalanced	imbalanced	ADJ
ijassa-351	167	18	data	datum	NOUN
ijassa-351	167	19	in	in	ADP
ijassa-351	167	20	application	application	NOUN
ijassa-351	167	21	to	to	ADP
ijassa-351	167	22	prediction	prediction	NOUN
ijassa-351	167	23	of	of	ADP
ijassa-351	167	24	the	the	DET
ijassa-351	167	25	post	post	ADJ
ijassa-351	167	26	-	-	ADJ
ijassa-351	167	27	operative	operative	ADJ
ijassa-351	167	28	life	life	NOUN
ijassa-351	167	29	expectancy	expectancy	NOUN
ijassa-351	167	30	in	in	ADP
ijassa-351	167	31	the	the	DET
ijassa-351	167	32	lung	lung	NOUN
ijassa-351	167	33	cancer	cancer	NOUN
ijassa-351	167	34	patients	patient	NOUN
ijassa-351	167	35	”	"	PUNCT
ijassa-351	167	36	,	,	PUNCT
ijassa-351	167	37	applied	apply	VERB
ijassa-351	167	38	soft	soft	ADJ
ijassa-351	167	39	computing	computing	NOUN
ijassa-351	167	40	,	,	PUNCT
ijassa-351	167	41	vol	vol	NOUN
ijassa-351	167	42	.	.	PROPN
ijassa-351	168	1	14	14	NUM
ijassa-351	168	2	,	,	PUNCT
ijassa-351	168	3	pp	pp	ADV
ijassa-351	168	4	99	99	NUM
ijassa-351	168	5	-	-	SYM
ijassa-351	168	6	108	108	NUM
ijassa-351	168	7	,	,	PUNCT
ijassa-351	168	8	jan	jan	PROPN
ijassa-351	168	9	.	.	PROPN
ijassa-351	168	10	2014	2014	NUM
ijassa-351	168	11	.	.	PUNCT
ijassa-351	169	1	[	[	X
ijassa-351	169	2	10	10	NUM
ijassa-351	169	3	]	]	X
ijassa-351	169	4	md	md	PROPN
ijassa-351	169	5	.	.	PROPN
ijassa-351	169	6	ahasan	ahasan	PROPN
ijassa-351	169	7	uddin	uddin	PROPN
ijassa-351	169	8	harun	harun	PROPN
ijassa-351	169	9	and	and	CCONJ
ijassa-351	169	10	md	md	PROPN
ijassa-351	169	11	.	.	PROPN
ijassa-351	169	12	nure	nure	PROPN
ijassa-351	169	13	alam	alam	PROPN
ijassa-351	169	14	,	,	PUNCT
ijassa-351	169	15	“	"	PUNCT
ijassa-351	169	16	predicting	predict	VERB
ijassa-351	169	17	outcome	outcome	NOUN
ijassa-351	169	18	of	of	ADP
ijassa-351	169	19	thoracic	thoracic	NOUN
ijassa-351	169	20	surgery	surgery	NOUN
ijassa-351	169	21	by	by	ADP
ijassa-351	169	22	data	datum	NOUN
ijassa-351	169	23	mining	mining	NOUN
ijassa-351	169	24	techniques	technique	NOUN
ijassa-351	169	25	”	"	PUNCT
ijassa-351	169	26	,	,	PUNCT
ijassa-351	169	27	ijarcsse	ijarcsse	NOUN
ijassa-351	169	28	,	,	PUNCT
ijassa-351	169	29	vol	vol	NOUN
ijassa-351	169	30	.	.	PROPN
ijassa-351	169	31	5	5	NUM
ijassa-351	169	32	,	,	PUNCT
ijassa-351	169	33	no	no	INTJ
ijassa-351	169	34	.	.	NOUN
ijassa-351	169	35	1	1	NUM
ijassa-351	169	36	,	,	PUNCT
ijassa-351	169	37	pp	pp	ADV
ijassa-351	169	38	7	7	NUM
ijassa-351	169	39	-	-	SYM
ijassa-351	169	40	10	10	NUM
ijassa-351	169	41	,	,	PUNCT
ijassa-351	169	42	2015	2015	NUM
ijassa-351	169	43	[	[	X
ijassa-351	169	44	11	11	NUM
ijassa-351	169	45	]	]	PUNCT
ijassa-351	169	46	mark	mark	PROPN
ijassa-351	169	47	a.	a.	PROPN
ijassa-351	169	48	hall	hall	PROPN
ijassa-351	169	49	,	,	PUNCT
ijassa-351	169	50	“	"	PUNCT
ijassa-351	169	51	correlation	correlation	NOUN
ijassa-351	169	52	-	-	PUNCT
ijassa-351	169	53	based	base	VERB
ijassa-351	169	54	feature	feature	NOUN
ijassa-351	169	55	selection	selection	NOUN
ijassa-351	169	56	for	for	ADP
ijassa-351	169	57	discrete	discrete	ADJ
ijassa-351	169	58	and	and	CCONJ
ijassa-351	169	59	numeric	numeric	ADJ
ijassa-351	169	60	class	class	NOUN
ijassa-351	169	61	machine	machine	NOUN
ijassa-351	169	62	learning	learning	NOUN
ijassa-351	169	63	”	"	PUNCT
ijassa-351	169	64	,	,	PUNCT
ijassa-351	169	65	international	international	ADJ
ijassa-351	169	66	conference	conference	NOUN
ijassa-351	169	67	on	on	ADP
ijassa-351	169	68	machine	machine	NOUN
ijassa-351	169	69	learning	learning	NOUN
ijassa-351	169	70	,	,	PUNCT
ijassa-351	169	71	pp	pp	ADP
ijassa-351	169	72	359	359	NUM
ijassa-351	169	73	-	-	SYM
ijassa-351	169	74	366	366	NUM
ijassa-351	169	75	,	,	PUNCT
ijassa-351	169	76	2000	2000	NUM
ijassa-351	169	77	[	[	X
ijassa-351	169	78	12	12	NUM
ijassa-351	169	79	]	]	X
ijassa-351	169	80	vishal	vishal	PROPN
ijassa-351	169	81	gupta	gupta	PROPN
ijassa-351	169	82	,	,	PUNCT
ijassa-351	169	83	et	et	PROPN
ijassa-351	169	84	al	al	PROPN
ijassa-351	169	85	.	.	PROPN
ijassa-351	169	86	,	,	PUNCT
ijassa-351	169	87	“	"	PUNCT
ijassa-351	169	88	performance	performance	NOUN
ijassa-351	169	89	of	of	ADP
ijassa-351	169	90	various	various	ADJ
ijassa-351	169	91	feature	feature	NOUN
ijassa-351	169	92	selection	selection	NOUN
ijassa-351	169	93	techniques	technique	NOUN
ijassa-351	169	94	under	under	ADP
ijassa-351	169	95	loaded	load	VERB
ijassa-351	169	96	networks	network	NOUN
ijassa-351	169	97	”	"	PUNCT
ijassa-351	169	98	,	,	PUNCT
ijassa-351	169	99	international	international	ADJ
ijassa-351	169	100	journal	journal	NOUN
ijassa-351	169	101	of	of	ADP
ijassa-351	169	102	computer	computer	NOUN
ijassa-351	169	103	applications	application	NOUN
ijassa-351	169	104	,	,	PUNCT
ijassa-351	169	105	vol	vol	NOUN
ijassa-351	169	106	.	.	PROPN
ijassa-351	170	1	78	78	NUM
ijassa-351	170	2	,	,	PUNCT
ijassa-351	170	3	no	no	INTJ
ijassa-351	170	4	.	.	NOUN
ijassa-351	170	5	2	2	NUM
ijassa-351	170	6	,	,	PUNCT
ijassa-351	170	7	september	september	PROPN
ijassa-351	170	8	2013	2013	NUM
ijassa-351	171	1	[	[	X
ijassa-351	171	2	13	13	NUM
ijassa-351	171	3	]	]	X
ijassa-351	171	4	uci	uci	NOUN
ijassa-351	171	5	machine	machine	NOUN
ijassa-351	171	6	learning	learn	VERB
ijassa-351	171	7	repository	repository	NOUN
ijassa-351	171	8	.	.	PUNCT
ijassa-351	172	1	url	url	PROPN
ijassa-351	172	2	:	:	PUNCT
ijassa-351	172	3	https://archive.ics.uci.edu	https://archive.ics.uci.edu	VERB
ijassa-351	173	1	[	[	X
ijassa-351	173	2	14	14	NUM
ijassa-351	173	3	]	]	X
ijassa-351	173	4	machine	machine	NOUN
ijassa-351	173	5	learning	learn	VERB
ijassa-351	173	6	group	group	NOUN
ijassa-351	173	7	at	at	ADP
ijassa-351	173	8	the	the	DET
ijassa-351	173	9	university	university	NOUN
ijassa-351	173	10	of	of	ADP
ijassa-351	173	11	waikato	waikato	PROPN
ijassa-351	173	12	.	.	PUNCT
ijassa-351	174	1	url	url	PROPN
ijassa-351	174	2	:	:	PUNCT
ijassa-351	174	3	http://www.cs.waikato.ac.nz/ml/	http://www.cs.waikato.ac.nz/ml/	PROPN
ijassa-351	175	1	[	[	X
ijassa-351	175	2	15	15	NUM
ijassa-351	175	3	]	]	X
ijassa-351	175	4	c.	c.	PROPN
ijassa-351	175	5	sunil	sunil	PROPN
ijassa-351	175	6	kumar	kumar	PROPN
ijassa-351	175	7	and	and	CCONJ
ijassa-351	175	8	r.j	r.j	PROPN
ijassa-351	175	9	.	.	PROPN
ijassa-351	175	10	rama	rama	PROPN
ijassa-351	175	11	sree	sree	PROPN
ijassa-351	175	12	,	,	PUNCT
ijassa-351	175	13	“	"	PUNCT
ijassa-351	175	14	application	application	NOUN
ijassa-351	175	15	of	of	ADP
ijassa-351	175	16	ranking	rank	VERB
ijassa-351	175	17	based	base	VERB
ijassa-351	175	18	attribute	attribute	NOUN
ijassa-351	175	19	selection	selection	NOUN
ijassa-351	175	20	filters	filter	NOUN
ijassa-351	175	21	to	to	PART
ijassa-351	175	22	perform	perform	VERB
ijassa-351	175	23	automated	automate	VERB
ijassa-351	175	24	evaluation	evaluation	NOUN
ijassa-351	175	25	of	of	ADP
ijassa-351	175	26	descriptive	descriptive	ADJ
ijassa-351	175	27	answers	answer	NOUN
ijassa-351	175	28	through	through	ADP
ijassa-351	175	29	sequential	sequential	ADJ
ijassa-351	175	30	minimal	minimal	ADJ
ijassa-351	175	31	optimization	optimization	NOUN
ijassa-351	175	32	models	model	NOUN
ijassa-351	175	33	”	"	PUNCT
ijassa-351	175	34	,	,	PUNCT
ijassa-351	175	35	ictact	ictact	ADJ
ijassa-351	175	36	journal	journal	NOUN
ijassa-351	175	37	on	on	ADP
ijassa-351	175	38	soft	soft	ADJ
ijassa-351	175	39	computing	computing	NOUN
ijassa-351	175	40	:	:	PUNCT
ijassa-351	175	41	special	special	ADJ
ijassa-351	175	42	issue	issue	NOUN
ijassa-351	175	43	on	on	ADP
ijassa-351	175	44	distributed	distribute	VERB
ijassa-351	175	45	intelligent	intelligent	ADJ
ijassa-351	175	46	systems	system	NOUN
ijassa-351	175	47	and	and	CCONJ
ijassa-351	175	48	applications	application	NOUN
ijassa-351	175	49	,	,	PUNCT
ijassa-351	175	50	vol	vol	NOUN
ijassa-351	175	51	.	.	PROPN
ijassa-351	175	52	5	5	NUM
ijassa-351	175	53	,	,	PUNCT
ijassa-351	175	54	no	no	INTJ
ijassa-351	175	55	.	.	NOUN
ijassa-351	175	56	1	1	NUM
ijassa-351	175	57	,	,	PUNCT
ijassa-351	175	58	october	october	PROPN
ijassa-351	175	59	2014	2014	NUM
ijassa-351	176	1	[	[	X
ijassa-351	176	2	16	16	NUM
ijassa-351	176	3	]	]	X
ijassa-351	176	4	jasmina	jasmina	NOUN
ijassa-351	176	5	novaković	novaković	NOUN
ijassa-351	176	6	,	,	PUNCT
ijassa-351	176	7	perica	perica	PROPN
ijassa-351	176	8	strbac	strbac	PROPN
ijassa-351	176	9	,	,	PUNCT
ijassa-351	176	10	dusan	dusan	PROPN
ijassa-351	176	11	bulatović	bulatović	PROPN
ijassa-351	176	12	,	,	PUNCT
ijassa-351	176	13	“	"	PUNCT
ijassa-351	176	14	toward	toward	ADP
ijassa-351	176	15	optimal	optimal	ADJ
ijassa-351	176	16	feature	feature	NOUN
ijassa-351	176	17	selection	selection	NOUN
ijassa-351	176	18	using	use	VERB
ijassa-351	176	19	ranking	ranking	ADJ
ijassa-351	176	20	methods	method	NOUN
ijassa-351	176	21	and	and	CCONJ
ijassa-351	176	22	classification	classification	NOUN
ijassa-351	176	23	algorithms	algorithm	NOUN
ijassa-351	176	24	”	"	PUNCT
ijassa-351	176	25	,	,	PUNCT
ijassa-351	176	26	yugoslav	yugoslav	ADJ
ijassa-351	176	27	journal	journal	PROPN
ijassa-351	176	28	of	of	ADP
ijassa-351	176	29	operations	operation	NOUN
ijassa-351	176	30	research	research	NOUN
ijassa-351	176	31	,	,	PUNCT
ijassa-351	176	32	vol	vol	NOUN
ijassa-351	176	33	.	.	PROPN
ijassa-351	176	34	21	21	NUM
ijassa-351	176	35	,	,	PUNCT
ijassa-351	176	36	no	no	INTJ
ijassa-351	176	37	.	.	NOUN
ijassa-351	176	38	1	1	NUM
ijassa-351	176	39	,	,	PUNCT
ijassa-351	176	40	pp	pp	ADJ
ijassa-351	176	41	.	.	PUNCT
ijassa-351	177	1	119	119	NUM
ijassa-351	177	2	-	-	SYM
ijassa-351	177	3	135	135	NUM
ijassa-351	177	4	,	,	PUNCT
ijassa-351	177	5	2011	2011	NUM
ijassa-351	177	6	[	[	X
ijassa-351	177	7	17	17	NUM
ijassa-351	177	8	]	]	X
ijassa-351	177	9	niket	niket	PROPN
ijassa-351	177	10	kumar	kumar	PROPN
ijassa-351	177	11	choudhary	choudhary	PROPN
ijassa-351	177	12	,	,	PUNCT
ijassa-351	177	13	yogita	yogita	PROPN
ijassa-351	177	14	shinde	shinde	PROPN
ijassa-351	177	15	,	,	PUNCT
ijassa-351	177	16	rajeswari	rajeswari	ADJ
ijassa-351	177	17	kannan	kannan	PROPN
ijassa-351	177	18	,	,	PUNCT
ijassa-351	177	19	vaithiyanathan	vaithiyanathan	PROPN
ijassa-351	177	20	venkatraman	venkatraman	NOUN
ijassa-351	177	21	,	,	PUNCT
ijassa-351	177	22	“	"	PUNCT
ijassa-351	177	23	impact	impact	NOUN
ijassa-351	177	24	of	of	ADP
ijassa-351	177	25	attribute	attribute	NOUN
ijassa-351	177	26	selection	selection	NOUN
ijassa-351	177	27	on	on	ADP
ijassa-351	177	28	the	the	DET
ijassa-351	177	29	accuracy	accuracy	NOUN
ijassa-351	177	30	of	of	ADP
ijassa-351	177	31	multilayer	multilayer	PROPN
ijassa-351	177	32	perceptron	perceptron	PROPN
ijassa-351	177	33	”	"	PUNCT
ijassa-351	177	34	,	,	PUNCT
ijassa-351	177	35	ijitkmi	ijitkmi	NOUN
ijassa-351	177	36	,	,	PUNCT
ijassa-351	177	37	vol	vol	NOUN
ijassa-351	177	38	.	.	PROPN
ijassa-351	177	39	7	7	NUM
ijassa-351	177	40	,	,	PUNCT
ijassa-351	177	41	no	no	INTJ
ijassa-351	177	42	.	.	NOUN
ijassa-351	177	43	2	2	NUM
ijassa-351	177	44	,	,	PUNCT
ijassa-351	177	45	pp	pp	ADJ
ijassa-351	177	46	.	.	PUNCT
ijassa-351	178	1	32	32	NUM
ijassa-351	178	2	-	-	SYM
ijassa-351	178	3	36	36	NUM
ijassa-351	178	4	,	,	PUNCT
ijassa-351	178	5	2014	2014	NUM
ijassa-351	178	6	http://ieeexplore.ieee.org/search/searchresult.jsp?searchwithin=%22authors%22:.qt.khalil,%20t..qt.&newsearch=true	http://ieeexplore.ieee.org/search/searchresult.jsp?searchwithin=%22authors%22:.qt.khalil,%20t..qt.&newsearch=true	X
ijassa-351	178	7	http://ieeexplore.ieee.org/search/searchresult.jsp?searchwithin=%22authors%22:.qt.nasreen,%20s..qt.&newsearch=true	http://ieeexplore.ieee.org/search/searchresult.jsp?searchwithin=%22authors%22:.qt.nasreen,%20s..qt.&newsearch=true	X
ijassa-351	178	8	https://archive.ics.uci.edu/	https://archive.ics.uci.edu/	PROPN
ijassa-351	178	9	http://www.cs.waikato.ac.nz/ml/	http://www.cs.waikato.ac.nz/ml/	PROPN