id	sid	tid	token	lemma	pos
aiti-345	1	1	advances	advance	NOUN
aiti-345	1	2	in	in	ADP
aiti-345	1	3	technology	technology	NOUN
aiti-345	1	4	innovation	innovation	NOUN
aiti-345	1	5	,	,	PUNCT
aiti-345	1	6	vol	vol	NOUN
aiti-345	1	7	.	.	PROPN
aiti-345	2	1	1	1	NUM
aiti-345	2	2	,	,	PUNCT
aiti-345	2	3	no	no	INTJ
aiti-345	2	4	.	.	NOUN
aiti-345	2	5	2	2	NUM
aiti-345	2	6	,	,	PUNCT
aiti-345	2	7	2016	2016	NUM
aiti-345	2	8	,	,	PUNCT
aiti-345	2	9	pp	pp	ADJ
aiti-345	2	10	.	.	PUNCT
aiti-345	3	1	53	53	NUM
aiti-345	3	2	57	57	NUM
aiti-345	3	3	53	53	NUM
aiti-345	3	4	copyright	copyright	NOUN
aiti-345	3	5	©	©	PROPN
aiti-345	3	6	taeti	taeti	PROPN
aiti-345	3	7	improved	improve	VERB
aiti-345	3	8	svm	svm	ADJ
aiti-345	3	9	classifier	classifier	NOUN
aiti-345	3	10	incorporating	incorporate	VERB
aiti-345	3	11	adaptive	adaptive	ADJ
aiti-345	3	12	condensed	condense	VERB
aiti-345	3	13	instances	instance	NOUN
aiti-345	3	14	based	base	VERB
aiti-345	3	15	on	on	ADP
aiti-345	3	16	hybrid	hybrid	ADJ
aiti-345	3	17	continuous	continuous	ADJ
aiti-345	3	18	-	-	PUNCT
aiti-345	3	19	discrete	discrete	ADJ
aiti-345	3	20	particle	particle	NOUN
aiti-345	3	21	swarm	swarm	NOUN
aiti-345	3	22	optimization	optimization	NOUN
aiti-345	3	23	chun	chun	PROPN
aiti-345	3	24	-	-	PUNCT
aiti-345	3	25	liang	liang	PROPN
aiti-345	3	26	lu1	lu1	PROPN
aiti-345	3	27	,	,	PUNCT
aiti-345	3	28	*	*	PROPN
aiti-345	3	29	,	,	PUNCT
aiti-345	3	30	tsun	tsun	PROPN
aiti-345	3	31	-	-	PUNCT
aiti-345	3	32	chen	chen	PROPN
aiti-345	3	33	lin2	lin2	PROPN
aiti-345	3	34	1department	1department	NUM
aiti-345	3	35	of	of	ADP
aiti-345	3	36	applied	apply	VERB
aiti-345	3	37	information	information	NOUN
aiti-345	3	38	and	and	CCONJ
aiti-345	3	39	multimedia	multimedia	NOUN
aiti-345	3	40	,	,	PUNCT
aiti-345	3	41	ching	ching	PROPN
aiti-345	3	42	kuo	kuo	PROPN
aiti-345	3	43	institute	institute	PROPN
aiti-345	3	44	of	of	ADP
aiti-345	3	45	management	management	PROPN
aiti-345	3	46	and	and	CCONJ
aiti-345	3	47	hea	hea	PROPN
aiti-345	3	48	lth	lth	PROPN
aiti-345	3	49	,	,	PUNCT
aiti-345	3	50	keelung	keelung	PROPN
aiti-345	3	51	,	,	PUNCT
aiti-345	3	52	taiwan	taiwan	PROPN
aiti-345	3	53	.	.	PUNCT
aiti-345	4	1	2department	2department	NUM
aiti-345	4	2	of	of	ADP
aiti-345	4	3	computer	computer	NOUN
aiti-345	4	4	and	and	CCONJ
aiti-345	4	5	communication	communication	NOUN
aiti-345	4	6	engineering	engineering	NOUN
aiti-345	4	7	,	,	PUNCT
aiti-345	4	8	dahan	dahan	PROPN
aiti-345	4	9	institute	institute	PROPN
aiti-345	4	10	of	of	ADP
aiti-345	4	11	technology	technology	PROPN
aiti-345	4	12	,	,	PUNCT
aiti-345	4	13	hualien	hualien	PROPN
aiti-345	4	14	,	,	PUNCT
aiti-345	4	15	taiwan	taiwan	PROPN
aiti-345	4	16	.	.	PUNCT
aiti-345	4	17	received	receive	VERB
aiti-345	4	18	20	20	NUM
aiti-345	4	19	sep	sep	PROPN
aiti-345	4	20	2016	2016	NUM
aiti-345	4	21	;	;	PUNCT
aiti-345	4	22	received	receive	VERB
aiti-345	4	23	in	in	ADP
aiti-345	4	24	revised	revise	VERB
aiti-345	4	25	form	form	NOUN
aiti-345	4	26	24	24	NUM
aiti-345	4	27	sep	sep	PROPN
aiti-345	4	28	2016	2016	NUM
aiti-345	4	29	;	;	PUNCT
aiti-345	4	30	accepted	accept	VERB
aiti-345	4	31	28	28	NUM
aiti-345	4	32	sep	sep	PROPN
aiti-345	4	33	2016	2016	NUM
aiti-345	4	34	abstract	abstract	NOUN
aiti-345	4	35	in	in	ADP
aiti-345	4	36	recent	recent	ADJ
aiti-345	4	37	years	year	NOUN
aiti-345	4	38	,	,	PUNCT
aiti-345	4	39	support	support	NOUN
aiti-345	4	40	vector	vector	NOUN
aiti-345	4	41	machine	machine	NOUN
aiti-345	4	42	(	(	PUNCT
aiti-345	4	43	svm	svm	PROPN
aiti-345	4	44	)	)	PUNCT
aiti-345	4	45	based	base	VERB
aiti-345	4	46	on	on	ADP
aiti-345	4	47	empirical	empirical	ADJ
aiti-345	4	48	risk	risk	NOUN
aiti-345	4	49	minimization	minimization	NOUN
aiti-345	4	50	is	be	AUX
aiti-345	4	51	supervised	supervise	VERB
aiti-345	4	52	learning	learning	NOUN
aiti-345	4	53	model	model	NOUN
aiti-345	4	54	which	which	PRON
aiti-345	4	55	has	have	AUX
aiti-345	4	56	been	be	AUX
aiti-345	4	57	successfully	successfully	ADV
aiti-345	4	58	used	use	VERB
aiti-345	4	59	in	in	ADP
aiti-345	4	60	the	the	DET
aiti-345	4	61	classification	classification	NOUN
aiti-345	4	62	and	and	CCONJ
aiti-345	4	63	regression	regression	NOUN
aiti-345	4	64	.	.	PUNCT
aiti-345	5	1	the	the	DET
aiti-345	5	2	standard	standard	ADJ
aiti-345	5	3	soft	soft	ADJ
aiti-345	5	4	-	-	PUNCT
aiti-345	5	5	margin	margin	NOUN
aiti-345	5	6	svm	svm	NOUN
aiti-345	5	7	trains	train	VERB
aiti-345	5	8	a	a	DET
aiti-345	5	9	classifier	classifier	NOUN
aiti-345	5	10	by	by	ADP
aiti-345	5	11	solving	solve	VERB
aiti-345	5	12	an	an	DET
aiti-345	5	13	optimization	optimization	NOUN
aiti-345	5	14	problem	problem	NOUN
aiti-345	5	15	to	to	PART
aiti-345	5	16	decide	decide	VERB
aiti-345	5	17	which	which	DET
aiti-345	5	18	instances	instance	NOUN
aiti-345	5	19	of	of	ADP
aiti-345	5	20	the	the	DET
aiti-345	5	21	training	training	NOUN
aiti-345	5	22	data	datum	NOUN
aiti-345	5	23	set	set	VERB
aiti-345	5	24	are	be	AUX
aiti-345	5	25	support	support	NOUN
aiti-345	5	26	vectors	vector	NOUN
aiti-345	5	27	.	.	PUNCT
aiti-345	6	1	however	however	ADV
aiti-345	6	2	,	,	PUNCT
aiti-345	6	3	in	in	ADP
aiti-345	6	4	many	many	ADJ
aiti-345	6	5	real	real	ADJ
aiti-345	6	6	applications	application	NOUN
aiti-345	6	7	,	,	PUNCT
aiti-345	6	8	it	it	PRON
aiti-345	6	9	is	be	AUX
aiti-345	6	10	imperative	imperative	ADJ
aiti-345	6	11	to	to	PART
aiti-345	6	12	perform	perform	VERB
aiti-345	6	13	feature	feature	NOUN
aiti-345	6	14	selection	selection	NOUN
aiti-345	6	15	to	to	PART
aiti-345	6	16	detect	detect	VERB
aiti-345	6	17	which	which	DET
aiti-345	6	18	features	feature	NOUN
aiti-345	6	19	are	be	AUX
aiti-345	6	20	actually	actually	ADV
aiti-345	6	21	relevant	relevant	ADJ
aiti-345	6	22	.	.	PUNCT
aiti-345	7	1	in	in	ADP
aiti-345	7	2	order	order	NOUN
aiti-345	7	3	to	to	PART
aiti-345	7	4	further	far	ADV
aiti-345	7	5	improve	improve	VERB
aiti-345	7	6	the	the	DET
aiti-345	7	7	performance	performance	NOUN
aiti-345	7	8	,	,	PUNCT
aiti-345	7	9	we	we	PRON
aiti-345	7	10	propose	propose	VERB
aiti-345	7	11	the	the	DET
aiti-345	7	12	adaptive	adaptive	ADJ
aiti-345	7	13	condensed	condense	VERB
aiti-345	7	14	instances	instance	NOUN
aiti-345	7	15	(	(	PUNCT
aiti-345	7	16	aci	aci	NOUN
aiti-345	7	17	)	)	PUNCT
aiti-345	7	18	strategy	strategy	NOUN
aiti-345	7	19	based	base	VERB
aiti-345	7	20	on	on	ADP
aiti-345	7	21	the	the	DET
aiti-345	7	22	hybrid	hybrid	ADJ
aiti-345	7	23	particle	particle	NOUN
aiti-345	7	24	swarm	swarm	NOUN
aiti-345	7	25	optimization	optimization	NOUN
aiti-345	7	26	(	(	PUNCT
aiti-345	7	27	hpso	hpso	NOUN
aiti-345	7	28	)	)	PUNCT
aiti-345	7	29	algorithm	algorithm	NOUN
aiti-345	7	30	for	for	ADP
aiti-345	7	31	the	the	DET
aiti-345	7	32	svm	svm	PROPN
aiti-345	7	33	classifier	classifier	NOUN
aiti-345	7	34	design	design	PROPN
aiti-345	7	35	.	.	PUNCT
aiti-345	8	1	the	the	DET
aiti-345	8	2	basic	basic	ADJ
aiti-345	8	3	idea	idea	NOUN
aiti-345	8	4	of	of	ADP
aiti-345	8	5	the	the	DET
aiti-345	8	6	proposed	propose	VERB
aiti-345	8	7	method	method	NOUN
aiti-345	8	8	is	be	AUX
aiti-345	8	9	to	to	PART
aiti-345	8	10	adopt	adopt	VERB
aiti-345	8	11	hpso	hpso	NOUN
aiti-345	8	12	to	to	PART
aiti-345	8	13	simultaneously	simultaneously	ADV
aiti-345	8	14	optimize	optimize	VERB
aiti-345	8	15	the	the	DET
aiti-345	8	16	aci	aci	NOUN
aiti-345	8	17	and	and	CCONJ
aiti-345	8	18	svm	svm	ADJ
aiti-345	8	19	kernel	kernel	PROPN
aiti-345	8	20	parameters	parameter	NOUN
aiti-345	8	21	for	for	ADP
aiti-345	8	22	the	the	DET
aiti-345	8	23	classification	classification	NOUN
aiti-345	8	24	accuracy	accuracy	NOUN
aiti-345	8	25	enhancement	enhancement	NOUN
aiti-345	8	26	.	.	PUNCT
aiti-345	9	1	the	the	DET
aiti-345	9	2	numerical	numerical	ADJ
aiti-345	9	3	experiments	experiment	NOUN
aiti-345	9	4	on	on	ADP
aiti-345	9	5	several	several	ADJ
aiti-345	9	6	uci	uci	NOUN
aiti-345	9	7	benchmark	benchmark	NOUN
aiti-345	9	8	datasets	dataset	NOUN
aiti-345	9	9	are	be	AUX
aiti-345	9	10	conducted	conduct	VERB
aiti-345	9	11	to	to	PART
aiti-345	9	12	find	find	VERB
aiti-345	9	13	the	the	DET
aiti-345	9	14	optimal	optimal	ADJ
aiti-345	9	15	parameters	parameter	NOUN
aiti-345	9	16	for	for	ADP
aiti-345	9	17	building	build	VERB
aiti-345	9	18	the	the	DET
aiti-345	9	19	svm	svm	ADJ
aiti-345	9	20	model	model	NOUN
aiti-345	9	21	.	.	PUNCT
aiti-345	10	1	experiment	experiment	NOUN
aiti-345	10	2	results	result	NOUN
aiti-345	10	3	show	show	VERB
aiti-345	10	4	that	that	SCONJ
aiti-345	10	5	the	the	DET
aiti-345	10	6	proposed	propose	VERB
aiti-345	10	7	framework	framework	NOUN
aiti-345	10	8	can	can	AUX
aiti-345	10	9	achieve	achieve	VERB
aiti-345	10	10	better	well	ADJ
aiti-345	10	11	performance	performance	NOUN
aiti-345	10	12	than	than	ADP
aiti-345	10	13	other	other	ADJ
aiti-345	10	14	published	publish	VERB
aiti-345	10	15	methods	method	NOUN
aiti-345	10	16	in	in	ADP
aiti-345	10	17	literature	literature	NOUN
aiti-345	10	18	and	and	CCONJ
aiti-345	10	19	provide	provide	VERB
aiti-345	10	20	a	a	DET
aiti-345	10	21	simple	simple	ADJ
aiti-345	10	22	but	but	CCONJ
aiti-345	10	23	subtle	subtle	ADJ
aiti-345	10	24	strategy	strategy	NOUN
aiti-345	10	25	to	to	PART
aiti-345	10	26	effectively	effectively	ADV
aiti-345	10	27	improve	improve	VERB
aiti-345	10	28	the	the	DET
aiti-345	10	29	classification	classification	NOUN
aiti-345	10	30	accuracy	accuracy	NOUN
aiti-345	10	31	for	for	ADP
aiti-345	10	32	svm	svm	ADJ
aiti-345	10	33	classifier	classifier	NOUN
aiti-345	10	34	.	.	PUNCT
aiti-345	11	1	keywords	keyword	NOUN
aiti-345	11	2	:	:	PUNCT
aiti-345	11	3	hybrid	hybrid	ADJ
aiti-345	11	4	particle	particle	NOUN
aiti-345	11	5	swarm	swarm	NOUN
aiti-345	11	6	optimizat	optimizat	NOUN
aiti-345	11	7	ion	ion	NOUN
aiti-345	11	8	(	(	PUNCT
aiti-345	11	9	hpso	hpso	NOUN
aiti-345	11	10	)	)	PUNCT
aiti-345	11	11	,	,	PUNCT
aiti-345	11	12	adaptive	adaptive	ADJ
aiti-345	11	13	condensed	condense	VERB
aiti-345	11	14	instances	instance	NOUN
aiti-345	11	15	(	(	PUNCT
aiti-345	11	16	aci	aci	PROPN
aiti-345	11	17	)	)	PUNCT
aiti-345	11	18	,	,	PUNCT
aiti-345	11	19	support	support	NOUN
aiti-345	11	20	vector	vector	NOUN
aiti-345	11	21	machine	machine	NOUN
aiti-345	11	22	(	(	PUNCT
aiti-345	11	23	svm	svm	PROPN
aiti-345	11	24	)	)	PUNCT
aiti-345	11	25	1	1	NUM
aiti-345	11	26	.	.	PUNCT
aiti-345	12	1	introduction	introduction	NOUN
aiti-345	12	2	support	support	NOUN
aiti-345	12	3	vector	vector	NOUN
aiti-345	12	4	machine	machine	NOUN
aiti-345	12	5	(	(	PUNCT
aiti-345	12	6	svm	svm	PROPN
aiti-345	12	7	)	)	PUNCT
aiti-345	12	8	was	be	AUX
aiti-345	12	9	originally	originally	ADV
aiti-345	12	10	proposed	propose	VERB
aiti-345	12	11	by	by	ADP
aiti-345	12	12	vapnik	vapnik	X
aiti-345	12	13	[	[	X
aiti-345	12	14	1	1	NUM
aiti-345	12	15	]	]	PUNCT
aiti-345	12	16	which	which	PRON
aiti-345	12	17	is	be	AUX
aiti-345	12	18	a	a	DET
aiti-345	12	19	powerful	powerful	ADJ
aiti-345	12	20	classification	classification	NOUN
aiti-345	12	21	method	method	NOUN
aiti-345	12	22	with	with	ADP
aiti-345	12	23	state	state	NOUN
aiti-345	12	24	-	-	PUNCT
aiti-345	12	25	of	of	ADP
aiti-345	12	26	-	-	PUNCT
aiti-345	12	27	the	the	DET
aiti-345	12	28	-	-	PUNCT
aiti-345	12	29	art	art	NOUN
aiti-345	12	30	performance	performance	NOUN
aiti-345	12	31	in	in	ADP
aiti-345	12	32	machine	machine	NOUN
aiti-345	12	33	learning	learning	NOUN
aiti-345	12	34	theory	theory	NOUN
aiti-345	12	35	,	,	PUNCT
aiti-345	12	36	has	have	AUX
aiti-345	12	37	drawn	draw	VERB
aiti-345	12	38	considerable	considerable	ADJ
aiti-345	12	39	attentions	attention	NOUN
aiti-345	12	40	due	due	ADP
aiti-345	12	41	to	to	ADP
aiti-345	12	42	its	its	PRON
aiti-345	12	43	high	high	ADJ
aiti-345	12	44	generalization	generalization	NOUN
aiti-345	12	45	ability	ability	NOUN
aiti-345	12	46	for	for	ADP
aiti-345	12	47	a	a	DET
aiti-345	12	48	wide	wide	ADJ
aiti-345	12	49	range	range	NOUN
aiti-345	12	50	of	of	ADP
aiti-345	12	51	applications	application	NOUN
aiti-345	12	52	including	include	VERB
aiti-345	12	53	speaker	speaker	NOUN
aiti-345	12	54	recognition	recognition	NOUN
aiti-345	13	1	[	[	X
aiti-345	13	2	2	2	NUM
aiti-345	13	3	]	]	PUNCT
aiti-345	13	4	,	,	PUNCT
aiti-345	13	5	bioinformatics	bioinformatic	NOUN
aiti-345	13	6	[	[	X
aiti-345	13	7	3	3	NUM
aiti-345	13	8	]	]	PUNCT
aiti-345	13	9	and	and	CCONJ
aiti-345	13	10	text	text	NOUN
aiti-345	13	11	categorization	categorization	NOUN
aiti-345	13	12	[	[	X
aiti-345	13	13	4	4	NUM
aiti-345	13	14	]	]	PUNCT
aiti-345	13	15	.	.	PUNCT
aiti-345	14	1	in	in	ADP
aiti-345	14	2	many	many	ADJ
aiti-345	14	3	pattern	pattern	NOUN
aiti-345	14	4	classification	classification	NOUN
aiti-345	14	5	tasks	task	NOUN
aiti-345	14	6	,	,	PUNCT
aiti-345	14	7	we	we	PRON
aiti-345	14	8	are	be	AUX
aiti-345	14	9	confronted	confront	VERB
aiti-345	14	10	with	with	ADP
aiti-345	14	11	the	the	DET
aiti-345	14	12	problem	problem	NOUN
aiti-345	14	13	that	that	SCONJ
aiti-345	14	14	the	the	DET
aiti-345	14	15	input	input	NOUN
aiti-345	14	16	space	space	NOUN
aiti-345	14	17	is	be	AUX
aiti-345	14	18	high	high	ADV
aiti-345	14	19	dimensional	dimensional	ADJ
aiti-345	14	20	and	and	CCONJ
aiti-345	14	21	to	to	PART
aiti-345	14	22	find	find	VERB
aiti-345	14	23	out	out	ADP
aiti-345	14	24	the	the	DET
aiti-345	14	25	combination	combination	NOUN
aiti-345	14	26	of	of	ADP
aiti-345	14	27	original	original	ADJ
aiti-345	14	28	input	input	NOUN
aiti-345	14	29	features	feature	NOUN
aiti-345	14	30	which	which	PRON
aiti-345	14	31	contribute	contribute	VERB
aiti-345	14	32	most	most	ADJ
aiti-345	14	33	to	to	ADP
aiti-345	14	34	the	the	DET
aiti-345	14	35	classification	classification	NOUN
aiti-345	14	36	is	be	AUX
aiti-345	14	37	crucial	crucial	ADJ
aiti-345	14	38	.	.	PUNCT
aiti-345	15	1	the	the	DET
aiti-345	15	2	computational	computational	ADJ
aiti-345	15	3	cost	cost	NOUN
aiti-345	15	4	of	of	ADP
aiti-345	15	5	classification	classification	NOUN
aiti-345	15	6	grows	grow	VERB
aiti-345	15	7	heavily	heavily	ADV
aiti-345	15	8	with	with	ADP
aiti-345	15	9	data	datum	NOUN
aiti-345	15	10	dimension	dimension	NOUN
aiti-345	15	11	size	size	NOUN
aiti-345	15	12	,	,	PUNCT
aiti-345	15	13	making	make	VERB
aiti-345	15	14	feature	feature	NOUN
aiti-345	15	15	selection	selection	NOUN
aiti-345	15	16	an	an	DET
aiti-345	15	17	important	important	ADJ
aiti-345	15	18	issue	issue	NOUN
aiti-345	15	19	for	for	ADP
aiti-345	15	20	the	the	DET
aiti-345	15	21	svm	svm	PROPN
aiti-345	15	22	.	.	PUNCT
aiti-345	16	1	the	the	DET
aiti-345	16	2	feature	feature	NOUN
aiti-345	16	3	selection	selection	NOUN
aiti-345	16	4	mechanism	mechanism	NOUN
aiti-345	16	5	falls	fall	VERB
aiti-345	16	6	into	into	ADP
aiti-345	16	7	three	three	NUM
aiti-345	16	8	categories	category	NOUN
aiti-345	16	9	:	:	PUNCT
aiti-345	16	10	filtering	filter	VERB
aiti-345	16	11	,	,	PUNCT
aiti-345	16	12	wrapper	wrapper	NOUN
aiti-345	16	13	,	,	PUNCT
aiti-345	16	14	and	and	CCONJ
aiti-345	16	15	embedded	embed	VERB
aiti-345	16	16	methods	method	NOUN
aiti-345	16	17	[	[	X
aiti-345	16	18	5	5	NUM
aiti-345	16	19	]	]	PUNCT
aiti-345	16	20	.	.	PUNCT
aiti-345	17	1	filters	filter	NOUN
aiti-345	17	2	generally	generally	ADV
aiti-345	17	3	involve	involve	VERB
aiti-345	17	4	a	a	DET
aiti-345	17	5	non	non	ADJ
aiti-345	17	6	-	-	ADJ
aiti-345	17	7	iterative	iterative	ADJ
aiti-345	17	8	computation	computation	NOUN
aiti-345	17	9	on	on	ADP
aiti-345	17	10	the	the	DET
aiti-345	17	11	original	original	ADJ
aiti-345	17	12	features	feature	NOUN
aiti-345	17	13	,	,	PUNCT
aiti-345	17	14	which	which	PRON
aiti-345	17	15	can	can	AUX
aiti-345	17	16	execute	execute	VERB
aiti-345	17	17	very	very	ADV
aiti-345	17	18	fast	fast	ADV
aiti-345	17	19	,	,	PUNCT
aiti-345	17	20	but	but	CCONJ
aiti-345	17	21	not	not	PART
aiti-345	17	22	usually	usually	ADV
aiti-345	17	23	optimal	optimal	ADJ
aiti-345	17	24	since	since	SCONJ
aiti-345	17	25	the	the	DET
aiti-345	17	26	learning	learning	NOUN
aiti-345	17	27	algorithm	algorithm	NOUN
aiti-345	17	28	are	be	AUX
aiti-345	17	29	not	not	PART
aiti-345	17	30	taken	take	VERB
aiti-345	17	31	into	into	ADP
aiti-345	17	32	account	account	NOUN
aiti-345	17	33	.	.	PUNCT
aiti-345	18	1	wrapper	wrapper	NOUN
aiti-345	18	2	methods	method	NOUN
aiti-345	18	3	usually	usually	ADV
aiti-345	18	4	achieve	achieve	VERB
aiti-345	18	5	better	well	ADJ
aiti-345	18	6	results	result	NOUN
aiti-345	18	7	than	than	ADP
aiti-345	18	8	filters	filter	NOUN
aiti-345	18	9	since	since	SCONJ
aiti-345	18	10	they	they	PRON
aiti-345	18	11	are	be	AUX
aiti-345	18	12	tuned	tune	VERB
aiti-345	18	13	to	to	ADP
aiti-345	18	14	the	the	DET
aiti-345	18	15	specific	specific	ADJ
aiti-345	18	16	interactions	interaction	NOUN
aiti-345	18	17	between	between	ADP
aiti-345	18	18	the	the	DET
aiti-345	18	19	classifier	classifier	NOUN
aiti-345	18	20	with	with	ADP
aiti-345	18	21	original	original	ADJ
aiti-345	18	22	feature	feature	NOUN
aiti-345	18	23	set	set	VERB
aiti-345	18	24	and	and	CCONJ
aiti-345	18	25	very	very	ADV
aiti-345	18	26	computationally	computationally	ADV
aiti-345	18	27	intensive	intensive	ADJ
aiti-345	18	28	.	.	PUNCT
aiti-345	19	1	finally	finally	ADV
aiti-345	19	2	,	,	PUNCT
aiti-345	19	3	unlike	unlike	ADP
aiti-345	19	4	filters	filter	NOUN
aiti-345	19	5	and	and	CCONJ
aiti-345	19	6	wrappers	wrapper	NOUN
aiti-345	19	7	,	,	PUNCT
aiti-345	19	8	the	the	DET
aiti-345	19	9	embedded	embed	VERB
aiti-345	19	10	techniques	technique	NOUN
aiti-345	19	11	simultaneously	simultaneously	ADV
aiti-345	19	12	determine	determine	VERB
aiti-345	19	13	features	feature	NOUN
aiti-345	19	14	and	and	CCONJ
aiti-345	19	15	classifier	classifier	NOUN
aiti-345	19	16	during	during	ADP
aiti-345	19	17	the	the	DET
aiti-345	19	18	training	training	NOUN
aiti-345	19	19	process	process	NOUN
aiti-345	19	20	but	but	CCONJ
aiti-345	19	21	the	the	DET
aiti-345	19	22	computational	computational	ADJ
aiti-345	19	23	time	time	NOUN
aiti-345	19	24	is	be	AUX
aiti-345	19	25	smaller	small	ADJ
aiti-345	19	26	than	than	ADP
aiti-345	19	27	wrapper	wrapper	NOUN
aiti-345	19	28	methods	method	NOUN
aiti-345	19	29	.	.	PUNCT
aiti-345	20	1	feature	feature	NOUN
aiti-345	20	2	selection	selection	NOUN
aiti-345	20	3	problem	problem	NOUN
aiti-345	20	4	is	be	AUX
aiti-345	20	5	a	a	DET
aiti-345	20	6	challenging	challenging	ADJ
aiti-345	20	7	task	task	NOUN
aiti-345	20	8	because	because	SCONJ
aiti-345	20	9	there	there	PRON
aiti-345	20	10	can	can	AUX
aiti-345	20	11	be	be	AUX
aiti-345	20	12	complex	complex	ADJ
aiti-345	20	13	interaction	interaction	NOUN
aiti-345	20	14	among	among	ADP
aiti-345	20	15	features	feature	NOUN
aiti-345	20	16	.	.	PUNCT
aiti-345	21	1	therefore	therefore	ADV
aiti-345	21	2	,	,	PUNCT
aiti-345	21	3	an	an	DET
aiti-345	21	4	exhaustive	exhaustive	ADJ
aiti-345	21	5	search	search	NOUN
aiti-345	21	6	is	be	AUX
aiti-345	21	7	practically	practically	ADV
aiti-345	21	8	impossible	impossible	ADJ
aiti-345	21	9	,	,	PUNCT
aiti-345	21	10	and	and	CCONJ
aiti-345	21	11	the	the	DET
aiti-345	21	12	efficient	efficient	ADJ
aiti-345	21	13	global	global	ADJ
aiti-345	21	14	search	search	NOUN
aiti-345	21	15	technique	technique	NOUN
aiti-345	21	16	is	be	AUX
aiti-345	21	17	needed	need	VERB
aiti-345	21	18	.	.	PUNCT
aiti-345	22	1	evolutionary	evolutionary	ADJ
aiti-345	22	2	computation	computation	NOUN
aiti-345	22	3	(	(	PUNCT
aiti-345	22	4	ec	ec	PROPN
aiti-345	22	5	)	)	PUNCT
aiti-345	22	6	are	be	AUX
aiti-345	22	7	well	well	ADV
aiti-345	22	8	known	know	VERB
aiti-345	22	9	heuristic	heuristic	ADJ
aiti-345	22	10	approaches	approach	VERB
aiti-345	22	11	global	global	ADJ
aiti-345	22	12	search	search	NOUN
aiti-345	22	13	ability	ability	NOUN
aiti-345	22	14	such	such	ADJ
aiti-345	22	15	as	as	ADP
aiti-345	22	16	simulated	simulated	ADJ
aiti-345	22	17	annealing	annealing	NOUN
aiti-345	22	18	(	(	PUNCT
aiti-345	22	19	sa	sa	NOUN
aiti-345	22	20	)	)	PUNCT
aiti-345	23	1	[	[	X
aiti-345	23	2	6	6	NUM
aiti-345	23	3	]	]	PUNCT
aiti-345	23	4	,	,	PUNCT
aiti-345	23	5	genetic	genetic	ADJ
aiti-345	23	6	algorithm	algorithm	NOUN
aiti-345	23	7	(	(	PUNCT
aiti-345	23	8	ga	ga	NOUN
aiti-345	23	9	)	)	PUNCT
aiti-345	24	1	[	[	X
aiti-345	24	2	7	7	NUM
aiti-345	24	3	]	]	PUNCT
aiti-345	24	4	,	,	PUNCT
aiti-345	24	5	and	and	CCONJ
aiti-345	24	6	particle	particle	NOUN
aiti-345	24	7	swarm	swarm	NOUN
aiti-345	24	8	optimization	optimization	NOUN
aiti-345	24	9	(	(	PUNCT
aiti-345	24	10	pso	pso	NOUN
aiti-345	24	11	)	)	PUNCT
aiti-345	25	1	[	[	X
aiti-345	25	2	8	8	NUM
aiti-345	25	3	]	]	PUNCT
aiti-345	25	4	,	,	PUNCT
aiti-345	25	5	have	have	AUX
aiti-345	25	6	gained	gain	VERB
aiti-345	25	7	a	a	DET
aiti-345	25	8	lot	lot	NOUN
aiti-345	25	9	of	of	ADP
aiti-345	25	10	attention	attention	NOUN
aiti-345	25	11	from	from	ADP
aiti-345	25	12	researchers	researcher	NOUN
aiti-345	25	13	in	in	ADP
aiti-345	25	14	the	the	DET
aiti-345	25	15	area	area	NOUN
aiti-345	25	16	.	.	PUNCT
aiti-345	26	1	compared	compare	VERB
aiti-345	26	2	with	with	ADP
aiti-345	26	3	other	other	ADJ
aiti-345	26	4	ec	ec	PROPN
aiti-345	26	5	algorithms	algorithm	NOUN
aiti-345	26	6	such	such	ADJ
aiti-345	26	7	as	as	ADP
aiti-345	26	8	sa	sa	PROPN
aiti-345	26	9	and	and	CCONJ
aiti-345	26	10	ga	ga	PROPN
aiti-345	26	11	,	,	PUNCT
aiti-345	26	12	pso	pso	NOUN
aiti-345	26	13	is	be	AUX
aiti-345	26	14	computationally	computationally	ADV
aiti-345	26	15	less	less	ADV
aiti-345	26	16	expensive	expensive	ADJ
aiti-345	26	17	and	and	CCONJ
aiti-345	26	18	can	can	AUX
aiti-345	26	19	converge	converge	VERB
aiti-345	26	20	more	more	ADV
aiti-345	26	21	quickly	quickly	ADV
aiti-345	26	22	.	.	PUNCT
aiti-345	27	1	a	a	DET
aiti-345	27	2	ga	ga	NOUN
aiti-345	27	3	-	-	PUNCT
aiti-345	27	4	based	base	VERB
aiti-345	27	5	feature	feature	NOUN
aiti-345	27	6	selection	selection	NOUN
aiti-345	27	7	method	method	NOUN
aiti-345	27	8	,	,	PUNCT
aiti-345	27	9	which	which	PRON
aiti-345	27	10	optimized	optimize	VERB
aiti-345	27	11	both	both	CCONJ
aiti-345	27	12	the	the	DET
aiti-345	27	13	feature	feature	NOUN
aiti-345	27	14	selection	selection	NOUN
aiti-345	27	15	and	and	CCONJ
aiti-345	27	16	parameters	parameter	NOUN
aiti-345	27	17	for	for	ADP
aiti-345	27	18	svm	svm	PROPN
aiti-345	27	19	,	,	PUNCT
aiti-345	27	20	was	be	AUX
aiti-345	27	21	proposed	propose	VERB
aiti-345	27	22	by	by	ADP
aiti-345	27	23	huang	huang	PROPN
aiti-345	28	1	[	[	X
aiti-345	28	2	9	9	NUM
aiti-345	28	3	]	]	PUNCT
aiti-345	28	4	,	,	PUNCT
aiti-345	28	5	and	and	CCONJ
aiti-345	28	6	the	the	DET
aiti-345	28	7	authors	author	NOUN
aiti-345	28	8	pointed	point	VERB
aiti-345	28	9	out	out	ADP
aiti-345	28	10	that	that	SCONJ
aiti-345	28	11	the	the	DET
aiti-345	28	12	algorithm	algorithm	NOUN
aiti-345	28	13	may	may	AUX
aiti-345	28	14	work	work	VERB
aiti-345	28	15	superior	superior	ADJ
aiti-345	28	16	to	to	ADP
aiti-345	28	17	the	the	DET
aiti-345	28	18	conventional	conventional	ADJ
aiti-345	28	19	grid	grid	NOUN
aiti-345	28	20	search	search	NOUN
aiti-345	28	21	method	method	NOUN
aiti-345	28	22	.	.	PUNCT
aiti-345	29	1	however	however	ADV
aiti-345	29	2	,	,	PUNCT
aiti-345	29	3	the	the	DET
aiti-345	29	4	treatment	treatment	NOUN
aiti-345	29	5	of	of	ADP
aiti-345	29	6	these	these	DET
aiti-345	29	7	redundant	redundant	ADJ
aiti-345	29	8	or	or	CCONJ
aiti-345	29	9	irrelevant	irrelevant	ADJ
aiti-345	29	10	instances	instance	NOUN
aiti-345	29	11	is	be	AUX
aiti-345	29	12	not	not	PART
aiti-345	29	13	taken	take	VERB
aiti-345	29	14	into	into	ADP
aiti-345	29	15	account	account	NOUN
aiti-345	29	16	in	in	ADP
aiti-345	29	17	the	the	DET
aiti-345	29	18	classification	classification	NOUN
aiti-345	29	19	procedure	procedure	NOUN
aiti-345	29	20	.	.	PUNCT
aiti-345	30	1	in	in	ADP
aiti-345	30	2	this	this	DET
aiti-345	30	3	paper	paper	NOUN
aiti-345	30	4	,	,	PUNCT
aiti-345	30	5	the	the	DET
aiti-345	30	6	effectively	effectively	ADV
aiti-345	30	7	adaptive	adaptive	ADJ
aiti-345	30	8	condensed	condense	VERB
aiti-345	30	9	instances	instance	NOUN
aiti-345	30	10	(	(	PUNCT
aiti-345	30	11	aci	aci	NOUN
aiti-345	30	12	)	)	PUNCT
aiti-345	30	13	strategy	strategy	NOUN
aiti-345	30	14	that	that	PRON
aiti-345	30	15	we	we	PRON
aiti-345	30	16	previously	previously	ADV
aiti-345	30	17	published	publish	VERB
aiti-345	30	18	[	[	PUNCT
aiti-345	30	19	7	7	NUM
aiti-345	30	20	]	]	PUNCT
aiti-345	30	21	is	be	AUX
aiti-345	30	22	applied	apply	VERB
aiti-345	30	23	to	to	PART
aiti-345	30	24	decide	decide	VERB
aiti-345	30	25	which	which	DET
aiti-345	30	26	instances	instance	NOUN
aiti-345	30	27	of	of	ADP
aiti-345	30	28	the	the	DET
aiti-345	30	29	training	training	NOUN
aiti-345	30	30	data	datum	NOUN
aiti-345	30	31	set	set	VERB
aiti-345	30	32	are	be	AUX
aiti-345	30	33	support	support	NOUN
aiti-345	30	34	vectors	vector	NOUN
aiti-345	30	35	for	for	ADP
aiti-345	30	36	coping	cope	VERB
aiti-345	30	37	with	with	ADP
aiti-345	30	38	the	the	DET
aiti-345	30	39	problem	problem	NOUN
aiti-345	30	40	mentioned	mention	VERB
aiti-345	30	41	above	above	ADV
aiti-345	30	42	.	.	PUNCT
aiti-345	31	1	short	short	ADJ
aiti-345	31	2	communications	communication	NOUN
aiti-345	31	3	of	of	ADP
aiti-345	31	4	the	the	DET
aiti-345	31	5	early	early	ADJ
aiti-345	31	6	stages	stage	NOUN
aiti-345	31	7	of	of	ADP
aiti-345	31	8	this	this	DET
aiti-345	31	9	work	work	NOUN
aiti-345	31	10	have	have	AUX
aiti-345	31	11	appeared	appear	VERB
aiti-345	31	12	in	in	ADP
aiti-345	31	13	[	[	X
aiti-345	31	14	7	7	NUM
aiti-345	31	15	]	]	PUNCT
aiti-345	31	16	.	.	PUNCT
aiti-345	32	1	here	here	ADV
aiti-345	32	2	we	we	PRON
aiti-345	32	3	significantly	significantly	ADV
aiti-345	32	4	extend	extend	VERB
aiti-345	32	5	our	our	PRON
aiti-345	32	6	approach	approach	NOUN
aiti-345	32	7	to	to	ADP
aiti-345	32	8	account	account	VERB
aiti-345	32	9	for	for	ADP
aiti-345	32	10	the	the	DET
aiti-345	32	11	relevant	relevant	ADJ
aiti-345	32	12	instances	instance	NOUN
aiti-345	32	13	selection	selection	NOUN
aiti-345	32	14	during	during	ADP
aiti-345	32	15	the	the	DET
aiti-345	32	16	svm	svm	ADJ
aiti-345	32	17	training	training	NOUN
aiti-345	32	18	process	process	NOUN
aiti-345	32	19	.	.	PUNCT
aiti-345	33	1	in	in	ADP
aiti-345	33	2	order	order	NOUN
aiti-345	33	3	to	to	PART
aiti-345	33	4	further	far	ADV
aiti-345	33	5	improve	improve	VERB
aiti-345	33	6	the	the	DET
aiti-345	33	7	classification	classification	NOUN
aiti-345	33	8	performance	performance	NOUN
aiti-345	33	9	,	,	PUNCT
aiti-345	33	10	the	the	DET
aiti-345	33	11	aci	aci	PROPN
aiti-345	33	12	strategy	strategy	NOUN
aiti-345	33	13	based	base	VERB
aiti-345	33	14	on	on	ADP
aiti-345	33	15	the	the	DET
aiti-345	33	16	hybrid	hybrid	ADJ
aiti-345	33	17	particle	particle	NOUN
aiti-345	33	18	swarm	swarm	NOUN
aiti-345	33	19	optimization	optimization	NOUN
aiti-345	33	20	(	(	PUNCT
aiti-345	33	21	hpso	hpso	NOUN
aiti-345	33	22	)	)	PUNCT
aiti-345	33	23	algorithm	algorithm	NOUN
aiti-345	33	24	is	be	AUX
aiti-345	33	25	proposed	propose	VERB
aiti-345	33	26	for	for	ADP
aiti-345	33	27	the	the	DET
aiti-345	33	28	svm	svm	PROPN
aiti-345	33	29	classifier	classifier	NOUN
aiti-345	33	30	design	design	PROPN
aiti-345	33	31	.	.	PUNCT
aiti-345	34	1	several	several	ADJ
aiti-345	34	2	uci	uci	PROPN
aiti-345	34	3	benchmark	benchmark	NOUN
aiti-345	34	4	datasets	dataset	NOUN
aiti-345	34	5	are	be	AUX
aiti-345	34	6	conducted	conduct	VERB
aiti-345	34	7	to	to	PART
aiti-345	34	8	validate	validate	VERB
aiti-345	34	9	the	the	DET
aiti-345	34	10	effectiveness	effectiveness	NOUN
aiti-345	34	11	and	and	CCONJ
aiti-345	34	12	the	the	DET
aiti-345	34	13	experiment	experiment	NOUN
aiti-345	34	14	results	result	NOUN
aiti-345	34	15	show	show	VERB
aiti-345	34	16	that	that	SCONJ
aiti-345	34	17	the	the	DET
aiti-345	34	18	proposed	propose	VERB
aiti-345	34	19	framework	framework	NOUN
aiti-345	34	20	can	can	AUX
aiti-345	34	21	achieve	achieve	VERB
aiti-345	34	22	better	well	ADJ
aiti-345	34	23	performance	performance	NOUN
aiti-345	34	24	than	than	ADP
aiti-345	34	25	other	other	ADJ
aiti-345	34	26	ga	ga	PROPN
aiti-345	34	27	-	-	PUNCT
aiti-345	34	28	based	base	VERB
aiti-345	34	29	existing	exist	VERB
aiti-345	34	30	methods	method	NOUN
aiti-345	34	31	in	in	ADP
aiti-345	34	32	literature	literature	NOUN
aiti-345	34	33	.	.	PUNCT
aiti-345	35	1	the	the	DET
aiti-345	35	2	remainder	remainder	NOUN
aiti-345	35	3	of	of	ADP
aiti-345	35	4	this	this	DET
aiti-345	35	5	paper	paper	NOUN
aiti-345	35	6	is	be	AUX
aiti-345	35	7	organized	organize	VERB
aiti-345	35	8	as	as	SCONJ
aiti-345	35	9	follows	follow	VERB
aiti-345	35	10	.	.	PUNCT
aiti-345	36	1	section	section	NOUN
aiti-345	36	2	2	2	NUM
aiti-345	36	3	describes	describe	VERB
aiti-345	36	4	the	the	DET
aiti-345	36	5	related	related	ADJ
aiti-345	36	6	work	work	NOUN
aiti-345	36	7	including	include	VERB
aiti-345	36	8	the	the	DET
aiti-345	36	9	basic	basic	ADJ
aiti-345	36	10	pso	pso	NOUN
aiti-345	36	11	and	and	CCONJ
aiti-345	36	12	svm	svm	PROPN
aiti-345	36	13	classifier	classifier	NOUN
aiti-345	36	14	.	.	PUNCT
aiti-345	37	1	section	section	NOUN
aiti-345	37	2	3	3	NUM
aiti-345	37	3	illustrates	illustrate	VERB
aiti-345	37	4	particle	particle	NOUN
aiti-345	37	5	representation	representation	NOUN
aiti-345	37	6	,	,	PUNCT
aiti-345	37	7	hybrid	hybrid	ADJ
aiti-345	37	8	pso	pso	NOUN
aiti-345	37	9	with	with	ADP
aiti-345	37	10	disturbance	disturbance	NOUN
aiti-345	37	11	operation	operation	NOUN
aiti-345	37	12	,	,	PUNCT
aiti-345	37	13	aci	aci	PROPN
aiti-345	37	14	scheme	scheme	NOUN
aiti-345	37	15	and	and	CCONJ
aiti-345	37	16	the	the	DET
aiti-345	37	17	proposed	propose	VERB
aiti-345	37	18	framework	framework	NOUN
aiti-345	37	19	for	for	ADP
aiti-345	37	20	the	the	DET
aiti-345	37	21	svm	svm	PROPN
aiti-345	37	22	classifier	classifier	NOUN
aiti-345	37	23	.	.	PUNCT
aiti-345	38	1	section	section	NOUN
aiti-345	38	2	4	4	NUM
aiti-345	38	3	provides	provide	VERB
aiti-345	38	4	the	the	DET
aiti-345	38	5	experiment	experiment	NOUN
aiti-345	38	6	results	result	NOUN
aiti-345	38	7	,	,	PUNCT
aiti-345	38	8	and	and	CCONJ
aiti-345	38	9	conclusions	conclusion	NOUN
aiti-345	38	10	are	be	AUX
aiti-345	38	11	made	make	VERB
aiti-345	38	12	in	in	ADP
aiti-345	38	13	section	section	NOUN
aiti-345	38	14	5	5	NUM
aiti-345	38	15	.	.	NOUN
aiti-345	38	16	2	2	NUM
aiti-345	38	17	.	.	X
aiti-345	38	18	related	relate	VERB
aiti-345	38	19	work	work	NOUN
aiti-345	38	20	2.1	2.1	NUM
aiti-345	38	21	.	.	PUNCT
aiti-345	39	1	particle	particle	NOUN
aiti-345	39	2	swarm	swarm	NOUN
aiti-345	39	3	optimization	optimization	NOUN
aiti-345	39	4	(	(	PUNCT
aiti-345	39	5	pso	pso	NOUN
aiti-345	39	6	)	)	PUNCT
aiti-345	39	7	algorithm	algorithm	NOUN
aiti-345	39	8	the	the	DET
aiti-345	39	9	pso	pso	NOUN
aiti-345	39	10	algorithm	algorithm	NOUN
aiti-345	39	11	,	,	PUNCT
aiti-345	39	12	which	which	PRON
aiti-345	39	13	is	be	AUX
aiti-345	39	14	originally	originally	ADV
aiti-345	39	15	developed	develop	VERB
aiti-345	39	16	by	by	ADP
aiti-345	39	17	kennedy	kennedy	PROPN
aiti-345	39	18	and	and	CCONJ
aiti-345	39	19	eberhart	eberhart	NOUN
aiti-345	39	20	[	[	X
aiti-345	39	21	10	10	NUM
aiti-345	39	22	]	]	PUNCT
aiti-345	39	23	,	,	PUNCT
aiti-345	39	24	is	be	AUX
aiti-345	39	25	a	a	DET
aiti-345	39	26	search	search	NOUN
aiti-345	39	27	algorithm	algorithm	NOUN
aiti-345	39	28	modeling	model	VERB
aiti-345	39	29	the	the	DET
aiti-345	39	30	social	social	ADJ
aiti-345	39	31	behavior	behavior	NOUN
aiti-345	39	32	of	of	ADP
aiti-345	39	33	birds	bird	NOUN
aiti-345	39	34	within	within	ADP
aiti-345	39	35	a	a	DET
aiti-345	39	36	flock	flock	NOUN
aiti-345	39	37	.	.	PUNCT
aiti-345	40	1	in	in	ADP
aiti-345	40	2	the	the	DET
aiti-345	40	3	pso	pso	NOUN
aiti-345	40	4	algorithm	algorithm	NOUN
aiti-345	40	5	,	,	PUNCT
aiti-345	40	6	individuals	individual	NOUN
aiti-345	40	7	referred	refer	VERB
aiti-345	40	8	to	to	ADP
aiti-345	40	9	as	as	ADP
aiti-345	40	10	particles	particle	NOUN
aiti-345	40	11	,	,	PUNCT
aiti-345	40	12	are	be	AUX
aiti-345	40	13	flown	fly	VERB
aiti-345	40	14	through	through	ADP
aiti-345	40	15	hyper	hyper	ADJ
aiti-345	40	16	dimensional	dimensional	ADJ
aiti-345	40	17	search	search	NOUN
aiti-345	40	18	space	space	NOUN
aiti-345	40	19	.	.	PUNCT
aiti-345	41	1	pso	pso	NOUN
aiti-345	41	2	is	be	AUX
aiti-345	41	3	easy	easy	ADJ
aiti-345	41	4	to	to	PART
aiti-345	41	5	implement	implement	VERB
aiti-345	41	6	,	,	PUNCT
aiti-345	41	7	few	few	ADJ
aiti-345	41	8	parameters	parameter	NOUN
aiti-345	41	9	to	to	ADP
aiti-345	41	10	ad	ad	NOUN
aiti-345	41	11	just	just	ADV
aiti-345	41	12	,	,	PUNCT
aiti-345	41	13	and	and	CCONJ
aiti-345	41	14	usually	usually	ADV
aiti-345	41	15	faster	fast	ADJ
aiti-345	41	16	convergence	convergence	NOUN
aiti-345	41	17	rates	rate	NOUN
aiti-345	41	18	*	*	PUNCT
aiti-345	41	19	corresponding	correspond	VERB
aiti-345	41	20	author	author	NOUN
aiti-345	41	21	,	,	PUNCT
aiti-345	41	22	email	email	NOUN
aiti-345	41	23	:	:	PUNCT
aiti-345	41	24	leucl@ems.cku.edu.tw	leucl@ems.cku.edu.tw	NOUN
aiti-345	41	25	advances	advance	VERB
aiti-345	41	26	in	in	ADP
aiti-345	41	27	technology	technology	NOUN
aiti-345	41	28	innovation	innovation	NOUN
aiti-345	41	29	,	,	PUNCT
aiti-345	41	30	vol	vol	NOUN
aiti-345	41	31	.	.	PROPN
aiti-345	41	32	1	1	NUM
aiti-345	41	33	,	,	PUNCT
aiti-345	41	34	no	no	INTJ
aiti-345	41	35	.	.	NOUN
aiti-345	41	36	2	2	NUM
aiti-345	41	37	,	,	PUNCT
aiti-345	41	38	2016	2016	NUM
aiti-345	41	39	,	,	PUNCT
aiti-345	41	40	pp	pp	ADJ
aiti-345	41	41	.	.	PUNCT
aiti-345	42	1	53	53	NUM
aiti-345	42	2	57	57	NUM
aiti-345	42	3	54	54	NUM
aiti-345	42	4	copyright	copyright	NOUN
aiti-345	42	5	©	©	PROPN
aiti-345	42	6	taeti	taeti	PROPN
aiti-345	42	7	copyright	copyright	NOUN
aiti-345	42	8	©	©	PROPN
aiti-345	42	9	taeti	taeti	PROPN
aiti-345	43	1	copyright	copyright	NOUN
aiti-345	43	2	©	©	PROPN
aiti-345	43	3	taeti	taeti	PROPN
aiti-345	44	1	copyright	copyright	NOUN
aiti-345	44	2	©	©	PROPN
aiti-345	44	3	taeti	taeti	PROPN
aiti-345	45	1	copyright	copyright	NOUN
aiti-345	45	2	©	©	PROPN
aiti-345	45	3	taeti	taeti	PROPN
aiti-345	45	4	than	than	ADP
aiti-345	45	5	other	other	ADJ
aiti-345	45	6	evolutionary	evolutionary	ADJ
aiti-345	45	7	algorithms	algorithm	NOUN
aiti-345	45	8	.	.	PUNCT
aiti-345	46	1	during	during	ADP
aiti-345	46	2	the	the	DET
aiti-345	46	3	optimization	optimization	NOUN
aiti-345	46	4	procedure	procedure	NOUN
aiti-345	46	5	,	,	PUNCT
aiti-345	46	6	particles	particle	NOUN
aiti-345	46	7	communicate	communicate	VERB
aiti-345	46	8	good	good	ADJ
aiti-345	46	9	positions	position	NOUN
aiti-345	46	10	to	to	ADP
aiti-345	46	11	each	each	DET
aiti-345	46	12	other	other	ADJ
aiti-345	46	13	and	and	CCONJ
aiti-345	46	14	adjust	adjust	VERB
aiti-345	46	15	position	position	NOUN
aiti-345	46	16	according	accord	VERB
aiti-345	46	17	to	to	ADP
aiti-345	46	18	their	their	PRON
aiti-345	46	19	history	history	NOUN
aiti-345	46	20	experience	experience	NOUN
aiti-345	46	21	and	and	CCONJ
aiti-345	46	22	the	the	DET
aiti-345	46	23	neighboring	neighboring	NOUN
aiti-345	46	24	particles	particle	NOUN
aiti-345	46	25	.	.	PUNCT
aiti-345	47	1	the	the	DET
aiti-345	47	2	basic	basic	ADJ
aiti-345	47	3	concept	concept	NOUN
aiti-345	47	4	of	of	ADP
aiti-345	47	5	the	the	DET
aiti-345	47	6	pso	pso	NOUN
aiti-345	47	7	algorithm	algorithm	NOUN
aiti-345	47	8	is	be	AUX
aiti-345	47	9	illustrated	illustrate	VERB
aiti-345	47	10	as	as	ADP
aiti-345	47	11	follow	follow	NOUN
aiti-345	47	12	:	:	PUNCT
aiti-345	47	13	1	1	NUM
aiti-345	47	14	1	1	NUM
aiti-345	47	15	1	1	NUM
aiti-345	47	16	2	2	NUM
aiti-345	47	17	2	2	NUM
aiti-345	47	18	(	(	PUNCT
aiti-345	47	19	)	)	PUNCT
aiti-345	47	20	(	(	PUNCT
aiti-345	47	21	)	)	PUNCT
aiti-345	48	1	k	k	PROPN
aiti-345	49	1	k	k	PROPN
aiti-345	49	2	k	k	PROPN
aiti-345	50	1	k	k	PROPN
aiti-345	50	2	i	i	PROPN
aiti-345	51	1	d	d	PROPN
aiti-345	51	2	i	i	PROPN
aiti-345	51	3	d	d	PROPN
aiti-345	51	4	i	i	PROPN
aiti-345	51	5	d	d	PROPN
aiti-345	51	6	i	i	PROPN
aiti-345	52	1	d	d	PROPN
aiti-345	52	2	k	k	PROPN
aiti-345	53	1	k	k	PROPN
aiti-345	54	1	i	i	PROPN
aiti-345	55	1	d	d	PROPN
aiti-345	56	1	i	i	PROPN
aiti-345	57	1	d	d	PROPN
aiti-345	57	2	v	v	PROPN
aiti-345	57	3	w	w	PROPN
aiti-345	57	4	v	v	NOUN
aiti-345	57	5	c	c	NOUN
aiti-345	57	6	r	r	NOUN
aiti-345	57	7	pb	pb	X
aiti-345	57	8	x	x	SYM
aiti-345	57	9	c	c	NOUN
aiti-345	57	10	r	r	NOUN
aiti-345	57	11	gb	gb	NOUN
aiti-345	57	12	x	x	PUNCT
aiti-345	57	13			ADJ
aiti-345	57	14			PROPN
aiti-345	57	15			PROPN
aiti-345	57	16			PUNCT
aiti-345	57	17			ADJ
aiti-345	57	18			ADJ
aiti-345	57	19			NOUN
aiti-345	57	20			VERB
aiti-345	57	21			ADJ
aiti-345	57	22			ADJ
aiti-345	57	23			NOUN
aiti-345	57	24	(	(	PUNCT
aiti-345	57	25	1	1	NUM
aiti-345	57	26	)	)	PUNCT
aiti-345	57	27	1	1	NUM
aiti-345	57	28	1k	1k	NOUN
aiti-345	58	1	k	k	PROPN
aiti-345	58	2	k	k	PROPN
aiti-345	59	1	i	i	PROPN
aiti-345	59	2	d	d	PROPN
aiti-345	59	3	i	i	PROPN
aiti-345	59	4	d	d	PROPN
aiti-345	59	5	idx	idx	VERB
aiti-345	59	6	v	v	NUM
aiti-345	59	7	x	x	NOUN
aiti-345	59	8			ADJ
aiti-345	59	9			PUNCT
aiti-345	59	10	(	(	PUNCT
aiti-345	59	11	2	2	X
aiti-345	59	12	)	)	PUNCT
aiti-345	59	13	where	where	SCONJ
aiti-345	59	14	1,2,	1,2,	NUM
aiti-345	59	15	...	...	PUNCT
aiti-345	59	16	,d	,d	PUNCT
aiti-345	59	17	d	d	PROPN
aiti-345	59	18	,	,	PUNCT
aiti-345	59	19	1,2,	1,2,	NUM
aiti-345	59	20	...	...	PUNCT
aiti-345	59	21	,i	,i	PUNCT
aiti-345	59	22	n	n	NOUN
aiti-345	59	23	,	,	PUNCT
aiti-345	59	24	and	and	CCONJ
aiti-345	59	25	d	d	NOUN
aiti-345	59	26	is	be	AUX
aiti-345	59	27	the	the	DET
aiti-345	59	28	dimension	dimension	NOUN
aiti-345	59	29	of	of	ADP
aiti-345	59	30	the	the	DET
aiti-345	59	31	search	search	NOUN
aiti-345	59	32	space	space	NOUN
aiti-345	59	33	,	,	PUNCT
aiti-345	59	34	n	n	X
aiti-345	59	35	is	be	AUX
aiti-345	59	36	the	the	DET
aiti-345	59	37	population	population	NOUN
aiti-345	59	38	size	size	NOUN
aiti-345	59	39	,	,	PUNCT
aiti-345	59	40	k	k	PROPN
aiti-345	59	41	is	be	AUX
aiti-345	59	42	the	the	DET
aiti-345	59	43	iterative	iterative	NOUN
aiti-345	59	44	times	time	NOUN
aiti-345	59	45	;	;	PUNCT
aiti-345	59	46	k	k	PROPN
aiti-345	59	47	idv	idv	PROPN
aiti-345	59	48	is	be	AUX
aiti-345	59	49	the	the	DET
aiti-345	59	50	i	i	PROPN
aiti-345	59	51	-	-	PUNCT
aiti-345	59	52	th	th	X
aiti-345	59	53	particle	particle	NOUN
aiti-345	59	54	velocity	velocity	NOUN
aiti-345	59	55	,	,	PUNCT
aiti-345	59	56	k	k	PROPN
aiti-345	59	57	idx	idx	PROPN
aiti-345	59	58	is	be	AUX
aiti-345	59	59	the	the	DET
aiti-345	59	60	current	current	ADJ
aiti-345	59	61	particle	particle	NOUN
aiti-345	59	62	solution	solution	NOUN
aiti-345	59	63	,	,	PUNCT
aiti-345	59	64	the	the	DET
aiti-345	59	65	i	i	PROPN
aiti-345	59	66	-	-	PUNCT
aiti-345	59	67	th	th	X
aiti-345	59	68	particle	particle	NOUN
aiti-345	59	69	position	position	NOUN
aiti-345	59	70	is	be	AUX
aiti-345	59	71	updated	update	VERB
aiti-345	59	72	by	by	ADP
aiti-345	59	73	equation	equation	NOUN
aiti-345	59	74	(	(	PUNCT
aiti-345	59	75	2	2	NUM
aiti-345	59	76	)	)	PUNCT
aiti-345	59	77	.	.	PUNCT
aiti-345	60	1	k	k	PROPN
aiti-345	60	2	idpb	idpb	PROPN
aiti-345	60	3	is	be	AUX
aiti-345	60	4	the	the	DET
aiti-345	60	5	i	i	PROPN
aiti-345	60	6	-	-	PUNCT
aiti-345	60	7	th	th	X
aiti-345	60	8	particle	particle	NOUN
aiti-345	60	9	best	good	ADJ
aiti-345	60	10	(	(	PUNCT
aiti-345	60	11	bestp	bestp	ADJ
aiti-345	60	12	)	)	PUNCT
aiti-345	60	13	solution	solution	NOUN
aiti-345	60	14	achieved	achieve	VERB
aiti-345	60	15	so	so	ADV
aiti-345	60	16	far	far	ADV
aiti-345	60	17	;	;	PUNCT
aiti-345	60	18	k	k	PROPN
aiti-345	60	19	idgb	idgb	PROPN
aiti-345	60	20	is	be	AUX
aiti-345	60	21	the	the	DET
aiti-345	60	22	global	global	ADJ
aiti-345	60	23	best	good	ADJ
aiti-345	60	24	(	(	PUNCT
aiti-345	60	25	bestg	bestg	NOUN
aiti-345	60	26	)	)	PUNCT
aiti-345	60	27	solution	solution	NOUN
aiti-345	60	28	obtained	obtain	VERB
aiti-345	60	29	by	by	ADP
aiti-345	60	30	any	any	DET
aiti-345	60	31	particle	particle	NOUN
aiti-345	60	32	in	in	ADP
aiti-345	60	33	the	the	DET
aiti-345	60	34	population	population	NOUN
aiti-345	60	35	;	;	PUNCT
aiti-345	60	36	1r	1r	NUM
aiti-345	60	37	and	and	CCONJ
aiti-345	60	38	2r	2r	NUM
aiti-345	60	39	are	be	AUX
aiti-345	60	40	random	random	ADJ
aiti-345	60	41	values	value	NOUN
aiti-345	60	42	in	in	ADP
aiti-345	60	43	the	the	DET
aiti-345	60	44	range	range	NOUN
aiti-345	60	45	[	[	X
aiti-345	60	46	0,1	0,1	NUM
aiti-345	60	47	]	]	PUNCT
aiti-345	60	48	for	for	ADP
aiti-345	60	49	denoting	denote	VERB
aiti-345	60	50	remembrance	remembrance	NOUN
aiti-345	60	51	ability	ability	NOUN
aiti-345	60	52	.	.	PUNCT
aiti-345	61	1	both	both	PRON
aiti-345	61	2	of	of	ADP
aiti-345	61	3	1c	1c	NUM
aiti-345	61	4	and	and	CCONJ
aiti-345	61	5	2c	2c	NOUN
aiti-345	61	6	are	be	AUX
aiti-345	61	7	learning	learn	VERB
aiti-345	61	8	factors	factor	NOUN
aiti-345	61	9	,	,	PUNCT
aiti-345	61	10	w	w	NOUN
aiti-345	61	11	is	be	AUX
aiti-345	61	12	inertia	inertia	NOUN
aiti-345	61	13	factor	factor	NOUN
aiti-345	61	14	.	.	PUNCT
aiti-345	62	1	a	a	DET
aiti-345	62	2	large	large	ADJ
aiti-345	62	3	inertia	inertia	NOUN
aiti-345	62	4	weight	weight	NOUN
aiti-345	62	5	facilitates	facilitate	VERB
aiti-345	62	6	global	global	ADJ
aiti-345	62	7	exploration	exploration	NOUN
aiti-345	62	8	,	,	PUNCT
aiti-345	62	9	while	while	SCONJ
aiti-345	62	10	a	a	DET
aiti-345	62	11	small	small	ADJ
aiti-345	62	12	one	one	NOUN
aiti-345	62	13	tends	tend	VERB
aiti-345	62	14	to	to	ADP
aiti-345	62	15	local	local	ADJ
aiti-345	62	16	exploration	exploration	NOUN
aiti-345	62	17	.	.	PUNCT
aiti-345	63	1	generally	generally	ADV
aiti-345	63	2	,	,	PUNCT
aiti-345	63	3	the	the	DET
aiti-345	63	4	value	value	NOUN
aiti-345	63	5	of	of	ADP
aiti-345	63	6	each	each	DET
aiti-345	63	7	component	component	NOUN
aiti-345	63	8	in	in	ADP
aiti-345	63	9	v	v	NOUN
aiti-345	63	10	can	can	AUX
aiti-345	63	11	be	be	AUX
aiti-345	63	12	clamped	clamp	VERB
aiti-345	63	13	to	to	ADP
aiti-345	63	14	the	the	DET
aiti-345	63	15	range	range	NOUN
aiti-345	64	1	[	[	X
aiti-345	64	2	−	−	X
aiti-345	64	3	maxv	maxv	NOUN
aiti-345	64	4	,	,	PUNCT
aiti-345	64	5	maxv	maxv	NOUN
aiti-345	64	6	]	]	PUNCT
aiti-345	64	7	for	for	ADP
aiti-345	64	8	controlling	control	VERB
aiti-345	64	9	excessive	excessive	ADJ
aiti-345	64	10	roaming	roaming	NOUN
aiti-345	64	11	of	of	ADP
aiti-345	64	12	the	the	DET
aiti-345	64	13	particle	particle	NOUN
aiti-345	64	14	outside	outside	ADP
aiti-345	64	15	the	the	DET
aiti-345	64	16	search	search	NOUN
aiti-345	64	17	space	space	NOUN
aiti-345	64	18	.	.	PUNCT
aiti-345	65	1	the	the	DET
aiti-345	65	2	pso	pso	NOUN
aiti-345	65	3	procedure	procedure	NOUN
aiti-345	65	4	is	be	AUX
aiti-345	65	5	organized	organize	VERB
aiti-345	65	6	in	in	ADP
aiti-345	65	7	the	the	DET
aiti-345	65	8	following	follow	VERB
aiti-345	65	9	sequence	sequence	NOUN
aiti-345	65	10	of	of	ADP
aiti-345	65	11	steps	step	NOUN
aiti-345	65	12	.	.	PUNCT
aiti-345	66	1	step	step	NOUN
aiti-345	66	2	1	1	NUM
aiti-345	66	3	:	:	PUNCT
aiti-345	66	4	initialize	initialize	VERB
aiti-345	66	5	:	:	PUNCT
aiti-345	66	6	randomly	randomly	ADV
aiti-345	66	7	generating	generate	VERB
aiti-345	66	8	initial	initial	ADJ
aiti-345	66	9	particles	particle	NOUN
aiti-345	66	10	.	.	PUNCT
aiti-345	67	1	step	step	NOUN
aiti-345	67	2	2	2	NUM
aiti-345	67	3	:	:	PUNCT
aiti-345	67	4	fitness	fitness	NOUN
aiti-345	67	5	evaluation	evaluation	NOUN
aiti-345	67	6	:	:	PUNCT
aiti-345	67	7	calculate	calculate	VERB
aiti-345	67	8	the	the	DET
aiti-345	67	9	fitness	fitness	NOUN
aiti-345	67	10	values	value	NOUN
aiti-345	67	11	of	of	ADP
aiti-345	67	12	each	each	DET
aiti-345	67	13	particle	particle	NOUN
aiti-345	67	14	in	in	ADP
aiti-345	67	15	the	the	DET
aiti-345	67	16	population	population	NOUN
aiti-345	67	17	.	.	PUNCT
aiti-345	68	1	step	step	VERB
aiti-345	68	2	3	3	NUM
aiti-345	68	3	:	:	PUNCT
aiti-345	68	4	update	update	NOUN
aiti-345	68	5	:	:	PUNCT
aiti-345	68	6	compare	compare	VERB
aiti-345	68	7	fitness	fitness	NOUN
aiti-345	68	8	values	value	NOUN
aiti-345	68	9	of	of	ADP
aiti-345	68	10	each	each	DET
aiti-345	68	11	particle	particle	NOUN
aiti-345	68	12	to	to	PART
aiti-345	68	13	update	update	VERB
aiti-345	68	14	the	the	DET
aiti-345	68	15	velocity	velocity	NOUN
aiti-345	68	16	and	and	CCONJ
aiti-345	68	17	position	position	NOUN
aiti-345	68	18	by	by	ADP
aiti-345	68	19	using	use	VERB
aiti-345	68	20	equation	equation	NOUN
aiti-345	68	21	(	(	PUNCT
aiti-345	68	22	1	1	NUM
aiti-345	68	23	)	)	PUNCT
aiti-345	68	24	and	and	CCONJ
aiti-345	68	25	(	(	PUNCT
aiti-345	68	26	2	2	NUM
aiti-345	68	27	)	)	PUNCT
aiti-345	68	28	.	.	PUNCT
aiti-345	69	1	step	step	NOUN
aiti-345	69	2	4	4	NUM
aiti-345	69	3	:	:	PUNCT
aiti-345	69	4	termination	termination	NOUN
aiti-345	69	5	criterion	criterion	NOUN
aiti-345	69	6	:	:	PUNCT
aiti-345	69	7	repeat	repeat	VERB
aiti-345	69	8	the	the	DET
aiti-345	69	9	step	step	NOUN
aiti-345	69	10	2	2	NUM
aiti-345	69	11	to	to	PART
aiti-345	69	12	step	step	VERB
aiti-345	69	13	3	3	NUM
aiti-345	69	14	until	until	SCONJ
aiti-345	69	15	the	the	DET
aiti-345	69	16	number	number	NOUN
aiti-345	69	17	of	of	ADP
aiti-345	69	18	iteration	iteration	NOUN
aiti-345	69	19	reaches	reach	VERB
aiti-345	69	20	the	the	DET
aiti-345	69	21	pre	pre	ADJ
aiti-345	69	22	-	-	VERB
aiti-345	69	23	defined	define	VERB
aiti-345	69	24	maximum	maximum	ADJ
aiti-345	69	25	number	number	NOUN
aiti-345	69	26	or	or	CCONJ
aiti-345	69	27	a	a	DET
aiti-345	69	28	termination	termination	NOUN
aiti-345	69	29	criterion	criterion	NOUN
aiti-345	69	30	is	be	AUX
aiti-345	69	31	satisfied	satisfied	ADJ
aiti-345	69	32	,	,	PUNCT
aiti-345	69	33	and	and	CCONJ
aiti-345	69	34	the	the	DET
aiti-345	69	35	best	good	ADJ
aiti-345	69	36	solution	solution	NOUN
aiti-345	69	37	bestg	bestg	NOUN
aiti-345	69	38	is	be	AUX
aiti-345	69	39	displayed	display	VERB
aiti-345	69	40	.	.	PUNCT
aiti-345	70	1	2.2	2.2	NUM
aiti-345	70	2	.	.	PUNCT
aiti-345	71	1	support	support	NOUN
aiti-345	71	2	vector	vector	NOUN
aiti-345	71	3	machine	machine	NOUN
aiti-345	71	4	(	(	PUNCT
aiti-345	71	5	svm	svm	ADJ
aiti-345	71	6	)	)	PUNCT
aiti-345	71	7	classifier	classifier	NOUN
aiti-345	71	8	support	support	NOUN
aiti-345	71	9	vector	vector	NOUN
aiti-345	71	10	machine	machine	NOUN
aiti-345	71	11	(	(	PUNCT
aiti-345	71	12	svm	svm	PROPN
aiti-345	71	13	)	)	PUNCT
aiti-345	71	14	have	have	AUX
aiti-345	71	15	drawn	draw	VERB
aiti-345	71	16	much	much	ADJ
aiti-345	71	17	attention	attention	NOUN
aiti-345	71	18	due	due	ADP
aiti-345	71	19	to	to	ADP
aiti-345	71	20	their	their	PRON
aiti-345	71	21	good	good	ADJ
aiti-345	71	22	performance	performance	NOUN
aiti-345	71	23	and	and	CCONJ
aiti-345	71	24	solid	solid	ADJ
aiti-345	71	25	theoretical	theoretical	ADJ
aiti-345	71	26	foundations	foundation	NOUN
aiti-345	71	27	[	[	X
aiti-345	71	28	11	11	NUM
aiti-345	71	29	]	]	PUNCT
aiti-345	71	30	.	.	PUNCT
aiti-345	72	1	the	the	DET
aiti-345	72	2	main	main	ADJ
aiti-345	72	3	concepts	concept	NOUN
aiti-345	72	4	of	of	ADP
aiti-345	72	5	svm	svm	PROPN
aiti-345	72	6	are	be	AUX
aiti-345	72	7	to	to	PART
aiti-345	72	8	first	first	ADV
aiti-345	72	9	transform	transform	VERB
aiti-345	72	10	input	input	NOUN
aiti-345	72	11	data	datum	NOUN
aiti-345	72	12	into	into	ADP
aiti-345	72	13	a	a	DET
aiti-345	72	14	higher	high	ADJ
aiti-345	72	15	dimensional	dimensional	ADJ
aiti-345	72	16	space	space	NOUN
aiti-345	72	17	by	by	ADP
aiti-345	72	18	means	mean	NOUN
aiti-345	72	19	of	of	ADP
aiti-345	72	20	a	a	DET
aiti-345	72	21	kernel	kernel	NOUN
aiti-345	72	22	function	function	NOUN
aiti-345	72	23	and	and	CCONJ
aiti-345	72	24	then	then	ADV
aiti-345	72	25	to	to	PART
aiti-345	72	26	find	find	VERB
aiti-345	72	27	an	an	DET
aiti-345	72	28	optimal	optimal	ADJ
aiti-345	72	29	separating	separate	VERB
aiti-345	72	30	hyper	hyper	ADJ
aiti-345	72	31	-	-	NOUN
aiti-345	72	32	plane	plane	NOUN
aiti-345	72	33	between	between	ADP
aiti-345	72	34	the	the	DET
aiti-345	72	35	two	two	NUM
aiti-345	72	36	data	data	NOUN
aiti-345	72	37	sets	set	NOUN
aiti-345	72	38	.	.	PUNCT
aiti-345	73	1	a	a	DET
aiti-345	73	2	practical	practical	ADJ
aiti-345	73	3	difficulty	difficulty	NOUN
aiti-345	73	4	of	of	ADP
aiti-345	73	5	using	use	VERB
aiti-345	73	6	svm	svm	PROPN
aiti-345	73	7	is	be	AUX
aiti-345	73	8	the	the	DET
aiti-345	73	9	selection	selection	NOUN
aiti-345	73	10	of	of	ADP
aiti-345	73	11	parameters	parameter	NOUN
aiti-345	73	12	such	such	ADJ
aiti-345	73	13	as	as	ADP
aiti-345	73	14	the	the	DET
aiti-345	73	15	penalty	penalty	NOUN
aiti-345	73	16	parameter	parameter	NOUN
aiti-345	73	17	c	c	PROPN
aiti-345	73	18	of	of	ADP
aiti-345	73	19	the	the	DET
aiti-345	73	20	error	error	NOUN
aiti-345	73	21	term	term	NOUN
aiti-345	73	22	and	and	CCONJ
aiti-345	73	23	the	the	DET
aiti-345	73	24	kernel	kernel	PROPN
aiti-345	73	25	parameter	parameter	NOUN
aiti-345	73	26			PROPN
aiti-345	73	27	in	in	ADP
aiti-345	73	28	rbf	rbf	PROPN
aiti-345	73	29	kernel	kernel	PROPN
aiti-345	73	30	function	function	PROPN
aiti-345	73	31	.	.	PUNCT
aiti-345	74	1	the	the	DET
aiti-345	74	2	appropriate	appropriate	ADJ
aiti-345	74	3	choice	choice	NOUN
aiti-345	74	4	of	of	ADP
aiti-345	74	5	parameters	parameter	NOUN
aiti-345	74	6	is	be	AUX
aiti-345	74	7	to	to	PART
aiti-345	74	8	get	get	VERB
aiti-345	74	9	the	the	DET
aiti-345	74	10	better	well	ADJ
aiti-345	74	11	generalization	generalization	NOUN
aiti-345	74	12	performance	performance	NOUN
aiti-345	74	13	.	.	PUNCT
aiti-345	75	1	the	the	DET
aiti-345	75	2	description	description	NOUN
aiti-345	75	3	of	of	ADP
aiti-345	75	4	svm	svm	PROPN
aiti-345	75	5	is	be	AUX
aiti-345	75	6	as	as	SCONJ
aiti-345	75	7	follows	follow	VERB
aiti-345	75	8	.	.	PUNCT
aiti-345	76	1	given	give	VERB
aiti-345	76	2	a	a	DET
aiti-345	76	3	set	set	NOUN
aiti-345	76	4	of	of	ADP
aiti-345	76	5	training	training	NOUN
aiti-345	76	6	data	datum	NOUN
aiti-345	76	7	1	1	NUM
aiti-345	76	8	(	(	PUNCT
aiti-345	76	9	,	,	PUNCT
aiti-345	76	10	,	,	PUNCT
aiti-345	76	11	)	)	PUNCT
aiti-345	76	12	px	px	X
aiti-345	76	13	x	x	PUNCT
aiti-345	76	14	with	with	ADP
aiti-345	76	15	corresponding	correspond	VERB
aiti-345	76	16	class	class	NOUN
aiti-345	76	17	labels	label	NOUN
aiti-345	76	18	1	1	NUM
aiti-345	76	19	(	(	PUNCT
aiti-345	76	20	,	,	PUNCT
aiti-345	76	21	,	,	PUNCT
aiti-345	76	22	)	)	PUNCT
aiti-345	76	23	py	py	PROPN
aiti-345	76	24	y	y	PROPN
aiti-345	76	25	and	and	CCONJ
aiti-345	76	26	{	{	PUNCT
aiti-345	76	27	1	1	NUM
aiti-345	76	28	,	,	PUNCT
aiti-345	76	29	1}iy	1}iy	ADJ
aiti-345	76	30			NOUN
aiti-345	76	31			VERB
aiti-345	76	32			PROPN
aiti-345	76	33	.	.	PUNCT
aiti-345	77	1	the	the	DET
aiti-345	77	2	svm	svm	PROPN
aiti-345	77	3	attempts	attempt	VERB
aiti-345	77	4	to	to	PART
aiti-345	77	5	find	find	VERB
aiti-345	77	6	a	a	DET
aiti-345	77	7	decision	decision	NOUN
aiti-345	77	8	surface	surface	NOUN
aiti-345	77	9	(	(	PUNCT
aiti-345	77	10	)	)	PUNCT
aiti-345	77	11	f	f	PROPN
aiti-345	77	12	x	x	X
aiti-345	77	13	,	,	PUNCT
aiti-345	77	14	to	to	PART
aiti-345	77	15	jointly	jointly	ADV
aiti-345	77	16	maximize	maximize	VERB
aiti-345	77	17	the	the	DET
aiti-345	77	18	margin	margin	NOUN
aiti-345	77	19	between	between	ADP
aiti-345	77	20	the	the	DET
aiti-345	77	21	two	two	NUM
aiti-345	77	22	classes	class	NOUN
aiti-345	77	23	and	and	CCONJ
aiti-345	77	24	minimize	minimize	VERB
aiti-345	77	25	the	the	DET
aiti-345	77	26	classification	classification	NOUN
aiti-345	77	27	error	error	NOUN
aiti-345	77	28	on	on	ADP
aiti-345	77	29	the	the	DET
aiti-345	77	30	training	training	NOUN
aiti-345	77	31	set	set	NOUN
aiti-345	77	32	.	.	PUNCT
aiti-345	78	1	1	1	NUM
aiti-345	78	2	(	(	PUNCT
aiti-345	78	3	)	)	PUNCT
aiti-345	78	4	(	(	PUNCT
aiti-345	78	5	,	,	PUNCT
aiti-345	78	6	)	)	PUNCT
aiti-345	79	1	p	p	NOUN
aiti-345	80	1	i	i	PRON
aiti-345	80	2	i	i	NOUN
aiti-345	80	3	ii	ii	VERB
aiti-345	81	1	f	f	NOUN
aiti-345	81	2	x	x	X
aiti-345	81	3	y	y	PROPN
aiti-345	81	4	k	k	NOUN
aiti-345	81	5	x	x	PUNCT
aiti-345	81	6	x	x	PUNCT
aiti-345	81	7	b	b	VERB
aiti-345	81	8			PROPN
aiti-345	81	9			PROPN
aiti-345	81	10			NOUN
aiti-345	81	11	(	(	PUNCT
aiti-345	81	12	3	3	NUM
aiti-345	81	13	)	)	PUNCT
aiti-345	81	14	where	where	SCONJ
aiti-345	81	15	k	k	PROPN
aiti-345	81	16	is	be	AUX
aiti-345	81	17	a	a	DET
aiti-345	81	18	kernel	kernel	NOUN
aiti-345	81	19	function	function	NOUN
aiti-345	81	20	,	,	PUNCT
aiti-345	81	21	i	i	PROPN
aiti-345	81	22	is	be	AUX
aiti-345	81	23	the	the	DET
aiti-345	81	24	lagrange	lagrange	NOUN
aiti-345	81	25	multiplier	multiplier	ADV
aiti-345	81	26	corresponding	correspond	VERB
aiti-345	81	27	to	to	ADP
aiti-345	81	28	the	the	DET
aiti-345	81	29	i	i	PROPN
aiti-345	81	30	-	-	PUNCT
aiti-345	81	31	th	th	VERB
aiti-345	81	32	training	training	NOUN
aiti-345	81	33	data	datum	NOUN
aiti-345	81	34	ix	ix	ADV
aiti-345	81	35	and	and	CCONJ
aiti-345	81	36	b	b	NOUN
aiti-345	81	37	is	be	AUX
aiti-345	81	38	the	the	DET
aiti-345	81	39	bias	bias	NOUN
aiti-345	81	40	.	.	PUNCT
aiti-345	82	1	the	the	DET
aiti-345	82	2	simple	simple	ADJ
aiti-345	82	3	kernel	kernel	NOUN
aiti-345	82	4	is	be	AUX
aiti-345	82	5	the	the	DET
aiti-345	82	6	inner	inner	ADJ
aiti-345	82	7	product	product	NOUN
aiti-345	82	8	function	function	VERB
aiti-345	82	9	ˆ	ˆ	ADP
aiti-345	82	10	ˆ	ˆ	ADJ
aiti-345	82	11	(	(	PUNCT
aiti-345	82	12	,	,	PUNCT
aiti-345	82	13	)	)	PUNCT
aiti-345	82	14	,	,	PUNCT
aiti-345	82	15	k	k	NOUN
aiti-345	82	16	x	x	PUNCT
aiti-345	82	17	x	x	PUNCT
aiti-345	82	18	x	x	X
aiti-345	82	19	x	x	PRON
aiti-345	82	20	which	which	PRON
aiti-345	82	21	produces	produce	VERB
aiti-345	82	22	the	the	DET
aiti-345	82	23	linear	linear	ADJ
aiti-345	82	24	decision	decision	NOUN
aiti-345	82	25	boundaries	boundary	NOUN
aiti-345	82	26	.	.	PUNCT
aiti-345	83	1	nonlinear	nonlinear	ADJ
aiti-345	83	2	kernel	kernel	PROPN
aiti-345	83	3	function	function	PROPN
aiti-345	83	4	maps	map	VERB
aiti-345	83	5	data	datum	NOUN
aiti-345	83	6	points	point	NOUN
aiti-345	83	7	to	to	ADP
aiti-345	83	8	a	a	DET
aiti-345	83	9	high	high	ADV
aiti-345	83	10	-	-	PUNCT
aiti-345	83	11	dimensional	dimensional	ADJ
aiti-345	83	12	feature	feature	NOUN
aiti-345	83	13	space	space	NOUN
aiti-345	83	14	as	as	ADP
aiti-345	83	15	linear	linear	ADJ
aiti-345	83	16	decision	decision	NOUN
aiti-345	83	17	spaces	space	NOUN
aiti-345	83	18	.	.	PUNCT
aiti-345	84	1	two	two	NUM
aiti-345	84	2	commonly	commonly	ADV
aiti-345	84	3	used	use	VERB
aiti-345	84	4	kernels	kernel	NOUN
aiti-345	84	5	are	be	AUX
aiti-345	84	6	the	the	DET
aiti-345	84	7	polynomial	polynomial	ADJ
aiti-345	84	8	kernel	kernel	NOUN
aiti-345	84	9	ˆ	ˆ	PROPN
aiti-345	84	10	(	(	PUNCT
aiti-345	84	11	,	,	PUNCT
aiti-345	84	12	)	)	PUNCT
aiti-345	84	13	pk	pk	NOUN
aiti-345	84	14	x	x	SYM
aiti-345	84	15	x	x	X
aiti-345	84	16	and	and	CCONJ
aiti-345	84	17	the	the	DET
aiti-345	84	18	radial	radial	ADJ
aiti-345	84	19	basis	basis	NOUN
aiti-345	84	20	function	function	NOUN
aiti-345	84	21	(	(	PUNCT
aiti-345	84	22	rbf	rbf	PROPN
aiti-345	84	23	)	)	PUNCT
aiti-345	84	24	kernel	kernel	PROPN
aiti-345	84	25	ˆ	ˆ	PROPN
aiti-345	84	26	(	(	PUNCT
aiti-345	84	27	,	,	PUNCT
aiti-345	84	28	)	)	PUNCT
aiti-345	84	29	rk	rk	NOUN
aiti-345	84	30	x	x	NOUN
aiti-345	85	1	x	x	X
aiti-345	85	2	.	.	PUNCT
aiti-345	86	1	the	the	DET
aiti-345	86	2	integer	integer	NOUN
aiti-345	86	3	polynomial	polynomial	ADJ
aiti-345	86	4	order	order	NOUN
aiti-345	86	5			PROPN
aiti-345	86	6	in	in	ADP
aiti-345	86	7	pk	pk	NOUN
aiti-345	86	8	and	and	CCONJ
aiti-345	86	9	the	the	DET
aiti-345	86	10	width	width	ADJ
aiti-345	86	11	factor	factor	NOUN
aiti-345	86	12			NUM
aiti-345	86	13	in	in	ADP
aiti-345	86	14	rk	rk	NOUN
aiti-345	86	15	are	be	AUX
aiti-345	86	16	hyper	hyper	ADJ
aiti-345	86	17	-	-	NOUN
aiti-345	86	18	parameters	parameter	NOUN
aiti-345	86	19	which	which	PRON
aiti-345	86	20	are	be	AUX
aiti-345	86	21	tuned	tune	VERB
aiti-345	86	22	to	to	ADP
aiti-345	86	23	a	a	DET
aiti-345	86	24	specific	specific	ADJ
aiti-345	86	25	classification	classification	NOUN
aiti-345	86	26	problem	problem	NOUN
aiti-345	86	27	.	.	PUNCT
aiti-345	87	1			NOUN
aiti-345	87	2	ˆ	ˆ	NOUN
aiti-345	87	3	ˆ	ˆ	PROPN
aiti-345	87	4	(	(	PUNCT
aiti-345	87	5	,	,	PUNCT
aiti-345	87	6	)	)	PUNCT
aiti-345	87	7	1	1	NUM
aiti-345	87	8	,	,	PUNCT
aiti-345	87	9	pk	pk	NOUN
aiti-345	87	10	x	x	X
aiti-345	87	11	x	x	PUNCT
aiti-345	87	12	x	x	PUNCT
aiti-345	87	13	x	x	PUNCT
aiti-345	87	14			X
aiti-345	87	15			NUM
aiti-345	87	16			PUNCT
aiti-345	87	17	(	(	PUNCT
aiti-345	87	18	4	4	NUM
aiti-345	87	19	)	)	PUNCT
aiti-345	87	20	2ˆ	2ˆ	NOUN
aiti-345	87	21	ˆ	ˆ	PROPN
aiti-345	87	22	(	(	PUNCT
aiti-345	87	23	,	,	PUNCT
aiti-345	87	24	)	)	PUNCT
aiti-345	87	25	x	x	PUNCT
aiti-345	87	26	x	x	PUNCT
aiti-345	87	27	rk	rk	VERB
aiti-345	87	28	x	x	PUNCT
aiti-345	87	29	x	x	X
aiti-345	87	30	e	e	X
aiti-345	87	31			NUM
aiti-345	87	32			PROPN
aiti-345	87	33			NUM
aiti-345	87	34	(	(	PUNCT
aiti-345	87	35	5	5	NUM
aiti-345	87	36	)	)	PUNCT
aiti-345	87	37	3	3	NUM
aiti-345	87	38	.	.	NOUN
aiti-345	87	39	method	method	NOUN
aiti-345	87	40	since	since	SCONJ
aiti-345	87	41	the	the	DET
aiti-345	87	42	optimal	optimal	ADJ
aiti-345	87	43	hyper	hyper	ADJ
aiti-345	87	44	-	-	ADJ
aiti-345	87	45	plane	plane	NOUN
aiti-345	87	46	obtained	obtain	VERB
aiti-345	87	47	by	by	ADP
aiti-345	87	48	the	the	DET
aiti-345	87	49	svm	svm	NOUN
aiti-345	87	50	depends	depend	VERB
aiti-345	87	51	on	on	ADP
aiti-345	87	52	only	only	ADV
aiti-345	87	53	a	a	DET
aiti-345	87	54	small	small	ADJ
aiti-345	87	55	part	part	NOUN
aiti-345	87	56	of	of	ADP
aiti-345	87	57	the	the	DET
aiti-345	87	58	data	data	NOUN
aiti-345	87	59	points	point	NOUN
aiti-345	87	60	(	(	PUNCT
aiti-345	87	61	support	support	NOUN
aiti-345	87	62	vectors	vector	NOUN
aiti-345	87	63	)	)	PUNCT
aiti-345	87	64	,	,	PUNCT
aiti-345	87	65	it	it	PRON
aiti-345	87	66	may	may	AUX
aiti-345	87	67	become	become	VERB
aiti-345	87	68	sensitive	sensitive	ADJ
aiti-345	87	69	to	to	ADP
aiti-345	87	70	noises	noise	NOUN
aiti-345	87	71	or	or	CCONJ
aiti-345	87	72	outliers	outlier	NOUN
aiti-345	87	73	in	in	ADP
aiti-345	87	74	the	the	DET
aiti-345	87	75	training	training	NOUN
aiti-345	87	76	set	set	NOUN
aiti-345	87	77	.	.	PUNCT
aiti-345	88	1	in	in	ADP
aiti-345	88	2	this	this	DET
aiti-345	88	3	section	section	NOUN
aiti-345	88	4	,	,	PUNCT
aiti-345	88	5	the	the	DET
aiti-345	88	6	aci	aci	PROPN
aiti-345	88	7	scheme	scheme	NOUN
aiti-345	88	8	based	base	VERB
aiti-345	88	9	on	on	ADP
aiti-345	88	10	hybrid	hybrid	ADJ
aiti-345	88	11	pso	pso	NOUN
aiti-345	88	12	(	(	PUNCT
aiti-345	88	13	hpso	hpso	NOUN
aiti-345	88	14	)	)	PUNCT
aiti-345	88	15	is	be	AUX
aiti-345	88	16	proposed	propose	VERB
aiti-345	88	17	to	to	PART
aiti-345	88	18	tackle	tackle	VERB
aiti-345	88	19	feature	feature	NOUN
aiti-345	88	20	selection	selection	NOUN
aiti-345	88	21	,	,	PUNCT
aiti-345	88	22	condensed	condense	VERB
aiti-345	88	23	instances	instance	NOUN
aiti-345	88	24	extraction	extraction	NOUN
aiti-345	88	25	and	and	CCONJ
aiti-345	88	26	parameters	parameter	NOUN
aiti-345	88	27	setting	set	VERB
aiti-345	88	28	simultaneously	simultaneously	ADV
aiti-345	88	29	for	for	ADP
aiti-345	88	30	svm	svm	PROPN
aiti-345	88	31	.	.	PUNCT
aiti-345	89	1	the	the	DET
aiti-345	89	2	particle	particle	NOUN
aiti-345	89	3	representation	representation	NOUN
aiti-345	89	4	,	,	PUNCT
aiti-345	89	5	fitness	fitness	NOUN
aiti-345	89	6	definition	definition	NOUN
aiti-345	89	7	,	,	PUNCT
aiti-345	89	8	disturbance	disturbance	NOUN
aiti-345	89	9	strategy	strategy	NOUN
aiti-345	89	10	for	for	ADP
aiti-345	89	11	pso	pso	NOUN
aiti-345	89	12	operation	operation	NOUN
aiti-345	89	13	,	,	PUNCT
aiti-345	89	14	aci	aci	PROPN
aiti-345	89	15	scheme	scheme	NOUN
aiti-345	89	16	and	and	CCONJ
aiti-345	89	17	the	the	DET
aiti-345	89	18	proposed	propose	VERB
aiti-345	89	19	hybrid	hybrid	NOUN
aiti-345	89	20	framework	framework	NOUN
aiti-345	89	21	for	for	ADP
aiti-345	89	22	svm	svm	NOUN
aiti-345	89	23	are	be	AUX
aiti-345	89	24	described	describe	VERB
aiti-345	89	25	as	as	ADP
aiti-345	89	26	follows	follow	VERB
aiti-345	89	27	.	.	PUNCT
aiti-345	90	1	3.1	3.1	NUM
aiti-345	90	2	.	.	PUNCT
aiti-345	90	3	particle	particle	NOUN
aiti-345	90	4	representation	representation	NOUN
aiti-345	90	5	this	this	DET
aiti-345	90	6	study	study	NOUN
aiti-345	90	7	used	use	VERB
aiti-345	90	8	the	the	DET
aiti-345	90	9	rbf	rbf	PROPN
aiti-345	90	10	kernel	kernel	PROPN
aiti-345	90	11	function	function	PROPN
aiti-345	90	12	for	for	SCONJ
aiti-345	90	13	the	the	DET
aiti-345	90	14	svm	svm	ADJ
aiti-345	90	15	classifier	classifier	NOUN
aiti-345	90	16	to	to	PART
aiti-345	90	17	implement	implement	VERB
aiti-345	90	18	our	our	PRON
aiti-345	90	19	proposed	propose	VERB
aiti-345	90	20	method	method	NOUN
aiti-345	90	21	.	.	PUNCT
aiti-345	91	1	the	the	DET
aiti-345	91	2	rbf	rbf	PROPN
aiti-345	91	3	kernel	kernel	PROPN
aiti-345	91	4	function	function	PROPN
aiti-345	91	5	requires	require	VERB
aiti-345	91	6	two	two	NUM
aiti-345	91	7	parameters	parameter	NOUN
aiti-345	91	8	c	c	NOUN
aiti-345	91	9	and	and	CCONJ
aiti-345	91	10			PROPN
aiti-345	91	11	should	should	AUX
aiti-345	91	12	be	be	AUX
aiti-345	91	13	set	set	VERB
aiti-345	91	14	.	.	PUNCT
aiti-345	92	1	using	use	VERB
aiti-345	92	2	the	the	DET
aiti-345	92	3	adaptively	adaptively	ADV
aiti-345	92	4	condensed	condense	VERB
aiti-345	92	5	instance	instance	NOUN
aiti-345	92	6	rate	rate	NOUN
aiti-345	92	7	for	for	ADP
aiti-345	92	8	aci	aci	PROPN
aiti-345	92	9	scheme	scheme	PROPN
aiti-345	92	10	and	and	CCONJ
aiti-345	92	11	the	the	DET
aiti-345	92	12	rbf	rbf	PROPN
aiti-345	92	13	kernel	kernel	PROPN
aiti-345	92	14	for	for	ADP
aiti-345	92	15	svm	svm	PROPN
aiti-345	92	16	,	,	PUNCT
aiti-345	92	17	these	these	DET
aiti-345	92	18	three	three	NUM
aiti-345	92	19	parameters	parameter	NOUN
aiti-345	92	20	cond	cond	PROPN
aiti-345	92	21	,	,	PUNCT
aiti-345	92	22	c	c	NOUN
aiti-345	92	23	,	,	PUNCT
aiti-345	92	24			PROPN
aiti-345	92	25	and	and	CCONJ
aiti-345	92	26	features	feature	NOUN
aiti-345	92	27	used	use	VERB
aiti-345	92	28	as	as	ADP
aiti-345	92	29	input	input	NOUN
aiti-345	92	30	attributes	attribute	NOUN
aiti-345	92	31	must	must	AUX
aiti-345	92	32	be	be	AUX
aiti-345	92	33	optimized	optimize	VERB
aiti-345	92	34	simultaneously	simultaneously	ADV
aiti-345	92	35	for	for	ADP
aiti-345	92	36	our	our	PRON
aiti-345	92	37	proposed	propose	VERB
aiti-345	92	38	hybrid	hybrid	NOUN
aiti-345	92	39	system	system	NOUN
aiti-345	92	40	.	.	PUNCT
aiti-345	93	1	the	the	DET
aiti-345	93	2	particle	particle	NOUN
aiti-345	93	3	,	,	PUNCT
aiti-345	93	4	therefore	therefore	ADV
aiti-345	93	5	,	,	PUNCT
aiti-345	93	6	is	be	AUX
aiti-345	93	7	comprised	comprise	VERB
aiti-345	93	8	of	of	ADP
aiti-345	93	9	four	four	NUM
aiti-345	93	10	parts	part	NOUN
aiti-345	93	11	,	,	PUNCT
aiti-345	93	12	cond	cond	NOUN
aiti-345	93	13	,	,	PUNCT
aiti-345	93	14	c	c	PROPN
aiti-345	93	15	,	,	PUNCT
aiti-345	93	16			PROPN
aiti-345	93	17	are	be	AUX
aiti-345	93	18	the	the	DET
aiti-345	93	19	continuous	continuous	ADJ
aiti-345	93	20	variables	variable	NOUN
aiti-345	93	21	and	and	CCONJ
aiti-345	93	22	the	the	DET
aiti-345	93	23	features	feature	NOUN
aiti-345	93	24	mask	mask	NOUN
aiti-345	93	25	are	be	AUX
aiti-345	93	26	the	the	DET
aiti-345	93	27	discrete	discrete	ADJ
aiti-345	93	28	variables	variable	NOUN
aiti-345	93	29	.	.	PUNCT
aiti-345	94	1	table	table	NOUN
aiti-345	94	2	1	1	NUM
aiti-345	94	3	shows	show	VERB
aiti-345	94	4	the	the	DET
aiti-345	94	5	particle	particle	NOUN
aiti-345	94	6	representation	representation	NOUN
aiti-345	94	7	of	of	ADP
aiti-345	94	8	our	our	PRON
aiti-345	94	9	design	design	NOUN
aiti-345	94	10	.	.	PUNCT
aiti-345	95	1	table	table	NOUN
aiti-345	95	2	1	1	NUM
aiti-345	95	3	the	the	DET
aiti-345	95	4	hybrid	hybrid	ADJ
aiti-345	95	5	particle	particle	NOUN
aiti-345	95	6	representation	representation	NOUN
aiti-345	95	7	as	as	SCONJ
aiti-345	95	8	shown	show	VERB
aiti-345	95	9	in	in	ADP
aiti-345	95	10	table	table	NOUN
aiti-345	95	11	1	1	NUM
aiti-345	95	12	,	,	PUNCT
aiti-345	95	13	the	the	DET
aiti-345	95	14	representation	representation	NOUN
aiti-345	95	15	of	of	ADP
aiti-345	95	16	particle	particle	NOUN
aiti-345	95	17	i	i	PROPN
aiti-345	95	18	with	with	ADP
aiti-345	95	19	dimension	dimension	NOUN
aiti-345	95	20	of	of	ADP
aiti-345	95	21	3fn	3fn	ADJ
aiti-345	95	22			X
aiti-345	95	23	,	,	PUNCT
aiti-345	95	24	where	where	SCONJ
aiti-345	95	25	fn	fn	NOUN
aiti-345	95	26	is	be	AUX
aiti-345	95	27	the	the	DET
aiti-345	95	28	number	number	NOUN
aiti-345	95	29	of	of	ADP
aiti-345	95	30	features	feature	NOUN
aiti-345	95	31	that	that	PRON
aiti-345	95	32	varies	vary	VERB
aiti-345	95	33	from	from	ADP
aiti-345	95	34	different	different	ADJ
aiti-345	95	35	datasets	dataset	NOUN
aiti-345	95	36	,	,	PUNCT
aiti-345	95	37	,	,	PUNCT
aiti-345	95	38	1	1	NUM
aiti-345	95	39	,	,	PUNCT
aiti-345	95	40	~	~	PUNCT
aiti-345	95	41	fi	fi	NOUN
aiti-345	96	1	i	i	PRON
aiti-345	96	2	nx	nx	NUM
aiti-345	96	3	x	x	VERB
aiti-345	96	4	are	be	AUX
aiti-345	96	5	the	the	DET
aiti-345	96	6	features	feature	NOUN
aiti-345	96	7	mask	mask	NOUN
aiti-345	96	8	,	,	PUNCT
aiti-345	96	9	,	,	PUNCT
aiti-345	96	10	1fi	1fi	PROPN
aiti-345	96	11	nx	nx	PROPN
aiti-345	96	12			PUNCT
aiti-345	96	13	indicates	indicate	VERB
aiti-345	96	14	the	the	DET
aiti-345	96	15	parameter	parameter	NOUN
aiti-345	96	16	value	value	NOUN
aiti-345	96	17	cond	cond	PROPN
aiti-345	96	18	,	,	PUNCT
aiti-345	96	19	,	,	PUNCT
aiti-345	96	20	2fi	2fi	ADJ
aiti-345	96	21	nx	nx	PROPN
aiti-345	96	22			PUNCT
aiti-345	96	23	represents	represent	VERB
aiti-345	96	24	the	the	DET
aiti-345	96	25	parameter	parameter	NOUN
aiti-345	96	26	value	value	NOUN
aiti-345	96	27	c	c	PROPN
aiti-345	96	28	and	and	CCONJ
aiti-345	96	29	,	,	PUNCT
aiti-345	96	30	3fi	3fi	ADJ
aiti-345	96	31	nx	nx	X
aiti-345	96	32			PROPN
aiti-345	96	33	denotes	denote	VERB
aiti-345	96	34	the	the	DET
aiti-345	96	35	parameter	parameter	PROPN
aiti-345	96	36	value	value	PROPN
aiti-345	96	37	.	.	PUNCT
aiti-345	97	1	fitness	fitness	NOUN
aiti-345	97	2	function	function	PROPN
aiti-345	97	3	f	f	PROPN
aiti-345	97	4	is	be	AUX
aiti-345	97	5	the	the	DET
aiti-345	97	6	guide	guide	NOUN
aiti-345	97	7	of	of	ADP
aiti-345	97	8	hpso	hpso	NOUN
aiti-345	97	9	operation	operation	NOUN
aiti-345	97	10	to	to	PART
aiti-345	97	11	maximize	maximize	VERB
aiti-345	97	12	the	the	DET
aiti-345	97	13	classification	classification	NOUN
aiti-345	97	14	accuracy	accuracy	NOUN
aiti-345	97	15	and	and	CCONJ
aiti-345	97	16	minimize	minimize	VERB
aiti-345	97	17	the	the	DET
aiti-345	97	18	number	number	NOUN
aiti-345	97	19	of	of	ADP
aiti-345	97	20	selected	select	VERB
aiti-345	97	21	features	feature	NOUN
aiti-345	97	22	.	.	PUNCT
aiti-345	98	1	cca	cca	PROPN
aiti-345	98	2	is	be	AUX
aiti-345	98	3	the	the	DET
aiti-345	98	4	svm	svm	ADJ
aiti-345	98	5	classification	classification	NOUN
aiti-345	98	6	accuracy	accuracy	NOUN
aiti-345	98	7	,	,	PUNCT
aiti-345	98	8	if	if	SCONJ
aiti-345	98	9	denotes	denote	NOUN
aiti-345	98	10	the	the	DET
aiti-345	98	11	feature	feature	NOUN
aiti-345	98	12	mask	mask	NOUN
aiti-345	98	13	which	which	PRON
aiti-345	98	14	1	1	NUM
aiti-345	98	15	represents	represent	VERB
aiti-345	98	16	the	the	DET
aiti-345	98	17	feature	feature	NOUN
aiti-345	98	18	i	i	PRON
aiti-345	98	19	is	be	AUX
aiti-345	98	20	selected	select	VERB
aiti-345	98	21	and	and	CCONJ
aiti-345	98	22	0	0	NUM
aiti-345	98	23	indicates	indicate	VERB
aiti-345	98	24	that	that	SCONJ
aiti-345	98	25	feature	feature	NOUN
aiti-345	98	26	i	i	PRON
aiti-345	98	27	is	be	AUX
aiti-345	98	28	not	not	PART
aiti-345	98	29	selected	select	VERB
aiti-345	98	30	.	.	PUNCT
aiti-345	99	1	thus	thus	ADV
aiti-345	99	2	,	,	PUNCT
aiti-345	99	3	the	the	DET
aiti-345	99	4	particles	particle	NOUN
aiti-345	99	5	with	with	ADP
aiti-345	99	6	high	high	ADJ
aiti-345	99	7	classification	classification	NOUN
aiti-345	99	8	accuracy	accuracy	NOUN
aiti-345	99	9	and	and	CCONJ
aiti-345	99	10	a	a	DET
aiti-345	99	11	small	small	ADJ
aiti-345	99	12	number	number	NOUN
aiti-345	99	13	of	of	ADP
aiti-345	99	14	features	feature	NOUN
aiti-345	99	15	produce	produce	VERB
aiti-345	99	16	a	a	DET
aiti-345	99	17	high	high	ADJ
aiti-345	99	18	fitness	fitness	NOUN
aiti-345	99	19	value	value	NOUN
aiti-345	99	20	and	and	CCONJ
aiti-345	99	21	affect	affect	VERB
aiti-345	99	22	particle	particle	NOUN
aiti-345	99	23	’s	’s	PART
aiti-345	99	24	positions	position	NOUN
aiti-345	99	25	on	on	ADP
aiti-345	99	26	the	the	DET
aiti-345	99	27	next	next	ADJ
aiti-345	99	28	iteration	iteration	NOUN
aiti-345	99	29	.	.	PUNCT
aiti-345	100	1	considering	consider	VERB
aiti-345	100	2	the	the	DET
aiti-345	100	3	tradeoff	tradeoff	NOUN
aiti-345	100	4	between	between	ADP
aiti-345	100	5	the	the	DET
aiti-345	100	6	classification	classification	NOUN
aiti-345	100	7	accuracy	accuracy	NOUN
aiti-345	100	8	and	and	CCONJ
aiti-345	100	9	selected	select	VERB
aiti-345	100	10	feature	feature	NOUN
aiti-345	100	11	number	number	NOUN
aiti-345	100	12	,	,	PUNCT
aiti-345	100	13	these	these	DET
aiti-345	100	14	two	two	NUM
aiti-345	100	15	weight	weight	NOUN
aiti-345	100	16	1w	1w	NOUN
aiti-345	100	17	and	and	CCONJ
aiti-345	100	18	2w	2w	NUM
aiti-345	100	19	values	value	NOUN
aiti-345	100	20	can	can	AUX
aiti-345	100	21	be	be	AUX
aiti-345	100	22	adjusted	adjust	VERB
aiti-345	100	23	according	accord	VERB
aiti-345	100	24	to	to	ADP
aiti-345	100	25	the	the	DET
aiti-345	100	26	preference	preference	NOUN
aiti-345	100	27	for	for	ADP
aiti-345	100	28	svm	svm	ADJ
aiti-345	100	29	classifier	classifier	NOUN
aiti-345	100	30	design	design	PROPN
aiti-345	100	31	.	.	PUNCT
aiti-345	101	1	advances	advance	NOUN
aiti-345	101	2	in	in	ADP
aiti-345	101	3	technology	technology	NOUN
aiti-345	101	4	innovation	innovation	NOUN
aiti-345	101	5	,	,	PUNCT
aiti-345	101	6	vol	vol	NOUN
aiti-345	101	7	.	.	PROPN
aiti-345	102	1	1	1	NUM
aiti-345	102	2	,	,	PUNCT
aiti-345	102	3	no	no	INTJ
aiti-345	102	4	.	.	NOUN
aiti-345	102	5	2	2	NUM
aiti-345	102	6	,	,	PUNCT
aiti-345	102	7	2016	2016	NUM
aiti-345	102	8	,	,	PUNCT
aiti-345	102	9	pp	pp	ADJ
aiti-345	102	10	.	.	PUNCT
aiti-345	103	1	53	53	NUM
aiti-345	103	2	57	57	NUM
aiti-345	103	3	55	55	NUM
aiti-345	103	4	copyright	copyright	NOUN
aiti-345	103	5	©	©	PROPN
aiti-345	103	6	taeti	taeti	PROPN
aiti-345	103	7	1	1	NUM
aiti-345	103	8	1	1	NUM
aiti-345	103	9	2	2	NUM
aiti-345	103	10	1	1	NUM
aiti-345	103	11	fn	fn	NOUN
aiti-345	104	1	i	i	PRON
aiti-345	105	1	i	i	PRON
aiti-345	105	2	f	f	PROPN
aiti-345	106	1	w	w	PROPN
aiti-345	106	2	acc	acc	PROPN
aiti-345	106	3	w	w	PROPN
aiti-345	106	4	f	f	PROPN
aiti-345	106	5			PROPN
aiti-345	106	6			PROPN
aiti-345	106	7			NOUN
aiti-345	106	8			NOUN
aiti-345	106	9			PROPN
aiti-345	106	10			PROPN
aiti-345	106	11			PROPN
aiti-345	106	12			PROPN
aiti-345	106	13			X
aiti-345	106	14			PROPN
aiti-345	106	15			ADJ
aiti-345	106	16			NOUN
aiti-345	106	17			X
aiti-345	106	18	(	(	PUNCT
aiti-345	106	19	6	6	NUM
aiti-345	106	20	)	)	PUNCT
aiti-345	106	21	3.2	3.2	NUM
aiti-345	106	22	.	.	PUNCT
aiti-345	107	1	pso	pso	NOUN
aiti-345	107	2	with	with	ADP
aiti-345	107	3	disturbance	disturbance	NOUN
aiti-345	107	4	operation	operation	NOUN
aiti-345	107	5	in	in	ADP
aiti-345	107	6	the	the	DET
aiti-345	107	7	discrete	discrete	ADJ
aiti-345	107	8	pso	pso	NOUN
aiti-345	107	9	,	,	PUNCT
aiti-345	107	10	the	the	DET
aiti-345	107	11	particle	particle	NOUN
aiti-345	107	12	’s	’s	PART
aiti-345	107	13	personal	personal	ADJ
aiti-345	107	14	best	good	ADJ
aiti-345	107	15	and	and	CCONJ
aiti-345	107	16	global	global	ADJ
aiti-345	107	17	best	good	ADJ
aiti-345	107	18	is	be	AUX
aiti-345	107	19	updated	update	VERB
aiti-345	107	20	as	as	ADP
aiti-345	107	21	in	in	ADP
aiti-345	107	22	continuous	continuous	ADJ
aiti-345	107	23	value	value	NOUN
aiti-345	107	24	.	.	PUNCT
aiti-345	108	1	the	the	DET
aiti-345	108	2	major	major	ADJ
aiti-345	108	3	different	different	ADJ
aiti-345	108	4	between	between	ADP
aiti-345	108	5	discrete	discrete	ADJ
aiti-345	108	6	pso	pso	NOUN
aiti-345	108	7	with	with	ADP
aiti-345	108	8	continuous	continuous	ADJ
aiti-345	108	9	version	version	NOUN
aiti-345	108	10	is	be	AUX
aiti-345	108	11	that	that	SCONJ
aiti-345	108	12	velocities	velocity	NOUN
aiti-345	108	13	of	of	ADP
aiti-345	108	14	the	the	DET
aiti-345	108	15	particles	particle	NOUN
aiti-345	108	16	are	be	AUX
aiti-345	108	17	rather	rather	ADV
aiti-345	108	18	defined	define	VERB
aiti-345	108	19	in	in	ADP
aiti-345	108	20	terms	term	NOUN
aiti-345	108	21	of	of	ADP
aiti-345	108	22	probabilities	probability	NOUN
aiti-345	108	23	that	that	PRON
aiti-345	108	24	a	a	DET
aiti-345	108	25	bit	bit	NOUN
aiti-345	108	26	whether	whether	SCONJ
aiti-345	108	27	change	change	NOUN
aiti-345	108	28	to	to	ADP
aiti-345	108	29	one	one	NUM
aiti-345	108	30	.	.	PUNCT
aiti-345	109	1	by	by	ADP
aiti-345	109	2	this	this	DET
aiti-345	109	3	definition	definition	NOUN
aiti-345	109	4	,	,	PUNCT
aiti-345	109	5	a	a	DET
aiti-345	109	6	velocity	velocity	NOUN
aiti-345	109	7	must	must	AUX
aiti-345	109	8	be	be	AUX
aiti-345	109	9	restricted	restrict	VERB
aiti-345	109	10	within	within	ADP
aiti-345	109	11	the	the	DET
aiti-345	109	12	range	range	NOUN
aiti-345	109	13	min	min	PROPN
aiti-345	109	14	max	max	PROPN
aiti-345	109	15	[	[	PUNCT
aiti-345	109	16	,	,	PUNCT
aiti-345	109	17	]	]	X
aiti-345	109	18	v	v	X
aiti-345	109	19	v	v	NOUN
aiti-345	109	20	.	.	PUNCT
aiti-345	110	1	this	this	PRON
aiti-345	110	2	can	can	AUX
aiti-345	110	3	be	be	AUX
aiti-345	110	4	accomplished	accomplish	VERB
aiti-345	110	5	by	by	ADP
aiti-345	110	6	a	a	DET
aiti-345	110	7	sigmoid	sigmoid	NOUN
aiti-345	110	8	function	function	NOUN
aiti-345	110	9	(	(	PUNCT
aiti-345	110	10	)	)	PUNCT
aiti-345	110	11	s	s	NOUN
aiti-345	110	12	v	v	NOUN
aiti-345	110	13	,	,	PUNCT
aiti-345	110	14	and	and	CCONJ
aiti-345	110	15	the	the	DET
aiti-345	110	16	new	new	ADJ
aiti-345	110	17	particle	particle	NOUN
aiti-345	110	18	position	position	NOUN
aiti-345	110	19	is	be	AUX
aiti-345	110	20	calculated	calculate	VERB
aiti-345	110	21	using	use	VERB
aiti-345	110	22	the	the	DET
aiti-345	110	23	following	follow	VERB
aiti-345	110	24	rule	rule	NOUN
aiti-345	110	25	:	:	PUNCT
aiti-345	110	26	if	if	SCONJ
aiti-345	110	27	1	1	NUM
aiti-345	110	28	min	min	NOUN
aiti-345	110	29	max	max	PROPN
aiti-345	110	30	(	(	PUNCT
aiti-345	110	31	,	,	PUNCT
aiti-345	110	32	)	)	PUNCT
aiti-345	110	33	k	k	PROPN
aiti-345	110	34	idv	idv	PROPN
aiti-345	110	35	v	v	ADP
aiti-345	110	36	v	v	NUM
aiti-345	110	37			NOUN
aiti-345	110	38	then	then	ADV
aiti-345	110	39	1	1	NUM
aiti-345	110	40	1	1	NUM
aiti-345	110	41	max	max	PROPN
aiti-345	110	42	minmax(min	minmax(min	PROPN
aiti-345	110	43	(	(	PUNCT
aiti-345	110	44	,	,	PUNCT
aiti-345	110	45	)	)	PUNCT
aiti-345	110	46	,	,	PUNCT
aiti-345	110	47	)	)	PUNCT
aiti-345	110	48	k	k	PROPN
aiti-345	111	1	k	k	PROPN
aiti-345	111	2	i	i	PROPN
aiti-345	111	3	d	d	PROPN
aiti-345	111	4	idv	idv	PROPN
aiti-345	111	5	v	v	NOUN
aiti-345	111	6	v	v	ADJ
aiti-345	111	7	v	v	NOUN
aiti-345	111	8			ADJ
aiti-345	111	9	,	,	PUNCT
aiti-345	111	10	where	where	SCONJ
aiti-345	111	11	1	1	NUM
aiti-345	111	12	1	1	NUM
aiti-345	111	13	1	1	NUM
aiti-345	111	14	(	(	PUNCT
aiti-345	111	15	)	)	PUNCT
aiti-345	111	16	1	1	NUM
aiti-345	111	17	k	k	X
aiti-345	112	1	i	i	PROPN
aiti-345	112	2	d	d	PROPN
aiti-345	112	3	k	k	PROPN
aiti-345	113	1	i	i	PROPN
aiti-345	113	2	d	d	PROPN
aiti-345	113	3	v	v	PROPN
aiti-345	113	4	s	s	X
aiti-345	113	5	v	v	ADP
aiti-345	113	6	e	e	NOUN
aiti-345	113	7			ADV
aiti-345	113	8			VERB
aiti-345	113	9			PROPN
aiti-345	113	10			PROPN
aiti-345	113	11			PUNCT
aiti-345	113	12	(	(	PUNCT
aiti-345	113	13	7	7	X
aiti-345	113	14	)	)	PUNCT
aiti-345	113	15	if	if	SCONJ
aiti-345	113	16	1	1	NUM
aiti-345	113	17	(	(	PUNCT
aiti-345	113	18	)	)	PUNCT
aiti-345	113	19	(	(	PUNCT
aiti-345	113	20	)	)	PUNCT
aiti-345	113	21	k	k	PROPN
aiti-345	113	22	idrand	idrand	PROPN
aiti-345	113	23	s	s	PROPN
aiti-345	113	24	v	v	NOUN
aiti-345	113	25			PROPN
aiti-345	113	26	,	,	PUNCT
aiti-345	113	27	then	then	ADV
aiti-345	113	28	1	1	NUM
aiti-345	113	29	1k	1k	NUM
aiti-345	113	30	idx	idx	NOUN
aiti-345	113	31			PUNCT
aiti-345	113	32			NUM
aiti-345	113	33	;	;	PUNCT
aiti-345	113	34	else	else	ADV
aiti-345	113	35	1	1	NUM
aiti-345	113	36	0k	0k	NOUN
aiti-345	113	37	idx	idx	NOUN
aiti-345	113	38			PROPN
aiti-345	113	39			PROPN
aiti-345	113	40	.	.	PUNCT
aiti-345	114	1	the	the	DET
aiti-345	114	2	function	function	NOUN
aiti-345	114	3	(	(	PUNCT
aiti-345	114	4	)	)	PUNCT
aiti-345	114	5	ids	ids	NOUN
aiti-345	114	6	v	v	NOUN
aiti-345	114	7	is	be	AUX
aiti-345	114	8	a	a	DET
aiti-345	114	9	sigmoid	sigmoid	NOUN
aiti-345	114	10	limiting	limit	VERB
aiti-345	114	11	transformation	transformation	NOUN
aiti-345	114	12	and	and	CCONJ
aiti-345	114	13	(	(	PUNCT
aiti-345	114	14	)	)	PUNCT
aiti-345	114	15	rand	rand	NOUN
aiti-345	114	16	is	be	AUX
aiti-345	114	17	a	a	DET
aiti-345	114	18	random	random	ADJ
aiti-345	114	19	number	number	NOUN
aiti-345	114	20	selected	select	VERB
aiti-345	114	21	from	from	ADP
aiti-345	114	22	a	a	DET
aiti-345	114	23	uniform	uniform	ADJ
aiti-345	114	24	distribution	distribution	NOUN
aiti-345	114	25	in	in	ADP
aiti-345	114	26	[	[	X
aiti-345	114	27	0	0	NUM
aiti-345	114	28	,	,	PUNCT
aiti-345	114	29	1	1	NUM
aiti-345	114	30	]	]	PUNCT
aiti-345	114	31	.	.	PUNCT
aiti-345	115	1	note	note	VERB
aiti-345	115	2	that	that	SCONJ
aiti-345	115	3	the	the	DET
aiti-345	115	4	discrete	discrete	ADJ
aiti-345	115	5	pso	pso	NOUN
aiti-345	115	6	is	be	AUX
aiti-345	115	7	susceptible	susceptible	ADJ
aiti-345	115	8	to	to	ADP
aiti-345	115	9	a	a	DET
aiti-345	115	10	sigmoid	sigmoid	NOUN
aiti-345	115	11	function	function	NOUN
aiti-345	115	12	(	(	PUNCT
aiti-345	115	13	)	)	PUNCT
aiti-345	115	14	s	s	NOUN
aiti-345	115	15	v	v	NOUN
aiti-345	115	16	saturation	saturation	NOUN
aiti-345	115	17	which	which	PRON
aiti-345	115	18	occurs	occur	VERB
aiti-345	115	19	when	when	SCONJ
aiti-345	115	20	velocity	velocity	NOUN
aiti-345	115	21	values	value	NOUN
aiti-345	115	22	are	be	AUX
aiti-345	115	23	either	either	CCONJ
aiti-345	115	24	too	too	ADV
aiti-345	115	25	large	large	ADJ
aiti-345	115	26	or	or	CCONJ
aiti-345	115	27	too	too	ADV
aiti-345	115	28	small	small	ADJ
aiti-345	115	29	.	.	PUNCT
aiti-345	116	1	for	for	ADP
aiti-345	116	2	a	a	DET
aiti-345	116	3	velocity	velocity	NOUN
aiti-345	116	4	of	of	ADP
aiti-345	116	5	zero	zero	NUM
aiti-345	116	6	,	,	PUNCT
aiti-345	116	7	it	it	PRON
aiti-345	116	8	is	be	AUX
aiti-345	116	9	a	a	DET
aiti-345	116	10	probability	probability	NOUN
aiti-345	116	11	of	of	ADP
aiti-345	116	12	50	50	NUM
aiti-345	116	13	%	%	NOUN
aiti-345	116	14	for	for	ADP
aiti-345	116	15	the	the	DET
aiti-345	116	16	bit	bit	NOUN
aiti-345	116	17	to	to	PART
aiti-345	116	18	flip	flip	VERB
aiti-345	116	19	.	.	PUNCT
aiti-345	117	1	according	accord	VERB
aiti-345	117	2	to	to	ADP
aiti-345	117	3	the	the	DET
aiti-345	117	4	searching	search	VERB
aiti-345	117	5	behavior	behavior	NOUN
aiti-345	117	6	of	of	ADP
aiti-345	117	7	pso	pso	NOUN
aiti-345	117	8	,	,	PUNCT
aiti-345	117	9	the	the	DET
aiti-345	117	10	bestg	bestg	NOUN
aiti-345	117	11	value	value	NOUN
aiti-345	117	12	will	will	AUX
aiti-345	117	13	be	be	AUX
aiti-345	117	14	an	an	DET
aiti-345	117	15	important	important	ADJ
aiti-345	117	16	clue	clue	NOUN
aiti-345	117	17	in	in	ADP
aiti-345	117	18	leading	lead	VERB
aiti-345	117	19	particles	particle	NOUN
aiti-345	117	20	to	to	ADP
aiti-345	117	21	the	the	DET
aiti-345	117	22	global	global	ADJ
aiti-345	117	23	optimal	optimal	ADJ
aiti-345	117	24	solution	solution	NOUN
aiti-345	117	25	.	.	PUNCT
aiti-345	118	1	it	it	PRON
aiti-345	118	2	is	be	AUX
aiti-345	118	3	unavoidable	unavoidable	ADJ
aiti-345	118	4	for	for	SCONJ
aiti-345	118	5	the	the	DET
aiti-345	118	6	solution	solution	NOUN
aiti-345	118	7	to	to	PART
aiti-345	118	8	fall	fall	VERB
aiti-345	118	9	into	into	ADP
aiti-345	118	10	the	the	DET
aiti-345	118	11	local	local	ADJ
aiti-345	118	12	minimum	minimum	NOUN
aiti-345	118	13	while	while	SCONJ
aiti-345	118	14	particles	particle	NOUN
aiti-345	118	15	try	try	VERB
aiti-345	118	16	to	to	PART
aiti-345	118	17	find	find	VERB
aiti-345	118	18	better	well	ADJ
aiti-345	118	19	solutions	solution	NOUN
aiti-345	118	20	.	.	PUNCT
aiti-345	119	1	in	in	ADP
aiti-345	119	2	order	order	NOUN
aiti-345	119	3	to	to	PART
aiti-345	119	4	allow	allow	VERB
aiti-345	119	5	the	the	DET
aiti-345	119	6	solution	solution	NOUN
aiti-345	119	7	exploration	exploration	NOUN
aiti-345	119	8	in	in	ADP
aiti-345	119	9	the	the	DET
aiti-345	119	10	area	area	NOUN
aiti-345	119	11	to	to	PART
aiti-345	119	12	produce	produce	VERB
aiti-345	119	13	more	more	ADJ
aiti-345	119	14	potential	potential	ADJ
aiti-345	119	15	solutions	solution	NOUN
aiti-345	119	16	,	,	PUNCT
aiti-345	119	17	a	a	DET
aiti-345	119	18	mutation	mutation	NOUN
aiti-345	119	19	-	-	PUNCT
aiti-345	119	20	like	like	ADJ
aiti-345	119	21	disturbance	disturbance	NOUN
aiti-345	119	22	operation	operation	NOUN
aiti-345	119	23	is	be	AUX
aiti-345	119	24	inserted	insert	VERB
aiti-345	119	25	between	between	ADP
aiti-345	119	26	eq	eq	PROPN
aiti-345	119	27	.	.	PUNCT
aiti-345	120	1	(	(	PUNCT
aiti-345	120	2	1	1	NUM
aiti-345	120	3	)	)	PUNCT
aiti-345	120	4	and	and	CCONJ
aiti-345	120	5	eq	eq	NOUN
aiti-345	120	6	.	.	PUNCT
aiti-345	121	1	(	(	PUNCT
aiti-345	121	2	2	2	NUM
aiti-345	121	3	)	)	PUNCT
aiti-345	121	4	.	.	PUNCT
aiti-345	122	1	the	the	DET
aiti-345	122	2	disturbance	disturbance	NOUN
aiti-345	122	3	operation	operation	NOUN
aiti-345	122	4	random	random	ADJ
aiti-345	122	5	selects	select	NOUN
aiti-345	122	6	k	k	PROPN
aiti-345	122	7	dimensions	dimension	NOUN
aiti-345	122	8	(	(	PUNCT
aiti-345	122	9	1	1	X
aiti-345	122	10	k	k	PROPN
aiti-345	122	11	problem	problem	NOUN
aiti-345	122	12	dimensions	dimension	NOUN
aiti-345	122	13	)	)	PUNCT
aiti-345	122	14	of	of	ADP
aiti-345	122	15	m	m	PROPN
aiti-345	122	16	particles	particle	NOUN
aiti-345	122	17	(	(	PUNCT
aiti-345	122	18	1m	1m	NUM
aiti-345	122	19	particle	particle	NOUN
aiti-345	122	20	numbers	number	NOUN
aiti-345	122	21	)	)	PUNCT
aiti-345	122	22	to	to	PART
aiti-345	122	23	put	put	VERB
aiti-345	122	24	gaussian	gaussian	ADJ
aiti-345	122	25	noise	noise	NOUN
aiti-345	122	26	into	into	ADP
aiti-345	122	27	their	their	PRON
aiti-345	122	28	moving	move	VERB
aiti-345	122	29	vectors	vector	NOUN
aiti-345	122	30	(	(	PUNCT
aiti-345	122	31	velocities	velocity	NOUN
aiti-345	122	32	)	)	PUNCT
aiti-345	122	33	.	.	PUNCT
aiti-345	123	1	the	the	DET
aiti-345	123	2	disturbance	disturbance	NOUN
aiti-345	123	3	operation	operation	NOUN
aiti-345	123	4	will	will	AUX
aiti-345	123	5	affect	affect	VERB
aiti-345	123	6	particles	particle	NOUN
aiti-345	123	7	moving	move	VERB
aiti-345	123	8	toward	toward	ADV
aiti-345	123	9	to	to	ADP
aiti-345	123	10	unexpected	unexpected	ADJ
aiti-345	123	11	direction	direction	NOUN
aiti-345	123	12	in	in	ADP
aiti-345	123	13	selected	select	VERB
aiti-345	123	14	dimensions	dimension	NOUN
aiti-345	123	15	but	but	CCONJ
aiti-345	123	16	not	not	PART
aiti-345	123	17	previous	previous	ADJ
aiti-345	123	18	experience	experience	NOUN
aiti-345	123	19	.	.	PUNCT
aiti-345	124	1	it	it	PRON
aiti-345	124	2	will	will	AUX
aiti-345	124	3	lead	lead	VERB
aiti-345	124	4	particle	particle	NOUN
aiti-345	124	5	jump	jump	VERB
aiti-345	124	6	out	out	ADP
aiti-345	124	7	from	from	ADP
aiti-345	124	8	local	local	ADJ
aiti-345	124	9	search	search	NOUN
aiti-345	124	10	and	and	CCONJ
aiti-345	124	11	further	far	ADV
aiti-345	124	12	can	can	AUX
aiti-345	124	13	explore	explore	VERB
aiti-345	124	14	more	more	ADJ
aiti-345	124	15	diversity	diversity	NOUN
aiti-345	124	16	of	of	ADP
aiti-345	124	17	searching	search	VERB
aiti-345	124	18	space	space	NOUN
aiti-345	124	19	.	.	PUNCT
aiti-345	125	1	3.3	3.3	NUM
aiti-345	125	2	.	.	PUNCT
aiti-345	126	1	adaptive	adaptive	ADJ
aiti-345	126	2	condensed	condense	VERB
aiti-345	126	3	instances	instance	NOUN
aiti-345	126	4	(	(	PUNCT
aiti-345	126	5	aci	aci	NOUN
aiti-345	126	6	)	)	PUNCT
aiti-345	126	7	scheme	scheme	NOUN
aiti-345	126	8	the	the	DET
aiti-345	126	9	effectively	effectively	ADV
aiti-345	126	10	aci	aci	PROPN
aiti-345	126	11	scheme	scheme	NOUN
aiti-345	126	12	that	that	SCONJ
aiti-345	126	13	we	we	PRON
aiti-345	126	14	previously	previously	ADV
aiti-345	126	15	published	publish	VERB
aiti-345	126	16	in	in	ADP
aiti-345	126	17	[	[	X
aiti-345	126	18	7	7	NUM
aiti-345	126	19	]	]	PUNCT
aiti-345	126	20	is	be	AUX
aiti-345	126	21	extended	extend	VERB
aiti-345	126	22	to	to	PART
aiti-345	126	23	decide	decide	VERB
aiti-345	126	24	which	which	DET
aiti-345	126	25	instances	instance	NOUN
aiti-345	126	26	of	of	ADP
aiti-345	126	27	the	the	DET
aiti-345	126	28	training	training	NOUN
aiti-345	126	29	data	datum	NOUN
aiti-345	126	30	set	set	VERB
aiti-345	126	31	are	be	AUX
aiti-345	126	32	support	support	NOUN
aiti-345	126	33	vectors	vector	NOUN
aiti-345	126	34	.	.	PUNCT
aiti-345	127	1	the	the	DET
aiti-345	127	2	adaptively	adaptively	ADV
aiti-345	127	3	condensed	condense	VERB
aiti-345	127	4	instances	instance	NOUN
aiti-345	127	5	coefficient	coefficient	NOUN
aiti-345	127	6	in	in	ADP
aiti-345	127	7	the	the	DET
aiti-345	127	8	data	datum	NOUN
aiti-345	127	9	reduced	reduce	VERB
aiti-345	127	10	process	process	NOUN
aiti-345	127	11	is	be	AUX
aiti-345	127	12	flexible	flexible	ADJ
aiti-345	127	13	to	to	PART
aiti-345	127	14	edit	edit	VERB
aiti-345	127	15	out	out	ADP
aiti-345	127	16	noisy	noisy	ADJ
aiti-345	127	17	samples	sample	NOUN
aiti-345	127	18	,	,	PUNCT
aiti-345	127	19	reduce	reduce	VERB
aiti-345	127	20	the	the	DET
aiti-345	127	21	superfluous	superfluous	ADJ
aiti-345	127	22	data	datum	NOUN
aiti-345	127	23	points	point	NOUN
aiti-345	127	24	and	and	CCONJ
aiti-345	127	25	make	make	VERB
aiti-345	127	26	the	the	DET
aiti-345	127	27	svm	svm	NOUN
aiti-345	127	28	less	less	ADV
aiti-345	127	29	sensitive	sensitive	ADJ
aiti-345	127	30	to	to	ADP
aiti-345	127	31	noises	noise	NOUN
aiti-345	127	32	and	and	CCONJ
aiti-345	127	33	outliners	outliner	NOUN
aiti-345	127	34	.	.	PUNCT
aiti-345	128	1	the	the	DET
aiti-345	128	2	adaptively	adaptively	ADV
aiti-345	128	3	condensed	condense	VERB
aiti-345	128	4	instances	instance	NOUN
aiti-345	128	5	coefficient	coefficient	VERB
aiti-345	128	6	[	[	X
aiti-345	128	7	0,1]cond	0,1]cond	NOUN
aiti-345	128	8			NOUN
aiti-345	128	9	is	be	AUX
aiti-345	128	10	defined	define	VERB
aiti-345	128	11	as	as	ADP
aiti-345	128	12	the	the	DET
aiti-345	128	13	ratio	ratio	NOUN
aiti-345	128	14	of	of	ADP
aiti-345	128	15	the	the	DET
aiti-345	128	16	selected	select	VERB
aiti-345	128	17	number	number	NOUN
aiti-345	128	18	of	of	ADP
aiti-345	128	19	condensed	condense	VERB
aiti-345	128	20	instances	instance	NOUN
aiti-345	128	21	to	to	ADP
aiti-345	128	22	overall	overall	ADJ
aiti-345	128	23	dataset	dataset	NOUN
aiti-345	128	24	.	.	PUNCT
aiti-345	129	1	the	the	DET
aiti-345	129	2	aci	aci	PROPN
aiti-345	129	3	scheme	scheme	NOUN
aiti-345	129	4	is	be	AUX
aiti-345	129	5	described	describe	VERB
aiti-345	129	6	as	as	ADP
aiti-345	129	7	follows	follow	VERB
aiti-345	129	8	.	.	PUNCT
aiti-345	130	1	step	step	NOUN
aiti-345	130	2	1	1	NUM
aiti-345	130	3	:	:	PUNCT
aiti-345	130	4	initialize	initialize	VERB
aiti-345	130	5	:	:	PUNCT
aiti-345	130	6	randomly	randomly	ADV
aiti-345	130	7	generating	generate	VERB
aiti-345	130	8	initial	initial	ADJ
aiti-345	130	9	instance	instance	NOUN
aiti-345	130	10	set	set	VERB
aiti-345	130	11	.	.	PUNCT
aiti-345	131	1	step	step	NOUN
aiti-345	131	2	2	2	NUM
aiti-345	131	3	:	:	PUNCT
aiti-345	131	4	condense	condense	VERB
aiti-345	131	5	:	:	PUNCT
aiti-345	131	6	to	to	PART
aiti-345	131	7	decide	decide	VERB
aiti-345	131	8	whether	whether	SCONJ
aiti-345	131	9	all	all	DET
aiti-345	131	10	samples	sample	NOUN
aiti-345	131	11	have	have	AUX
aiti-345	131	12	been	be	AUX
aiti-345	131	13	achieved	achieve	VERB
aiti-345	131	14	the	the	DET
aiti-345	131	15	user	user	NOUN
aiti-345	131	16	defined	define	VERB
aiti-345	131	17	threshold	threshold	NOUN
aiti-345	131	18	cond	cond	PROPN
aiti-345	131	19	.	.	PUNCT
aiti-345	132	1	if	if	SCONJ
aiti-345	132	2	so	so	ADV
aiti-345	132	3	,	,	PUNCT
aiti-345	132	4	terminate	terminate	VERB
aiti-345	132	5	the	the	DET
aiti-345	132	6	process	process	NOUN
aiti-345	132	7	;	;	PUNCT
aiti-345	132	8	otherwise	otherwise	ADV
aiti-345	132	9	,	,	PUNCT
aiti-345	132	10	go	go	VERB
aiti-345	132	11	to	to	PART
aiti-345	132	12	step	step	VERB
aiti-345	132	13	3	3	NUM
aiti-345	132	14	.	.	PUNCT
aiti-345	133	1	step	step	NOUN
aiti-345	133	2	3	3	NUM
aiti-345	133	3	:	:	PUNCT
aiti-345	133	4	extend	extend	VERB
aiti-345	133	5	:	:	PUNCT
aiti-345	133	6	if	if	SCONJ
aiti-345	133	7	there	there	PRON
aiti-345	133	8	are	be	VERB
aiti-345	133	9	any	any	DET
aiti-345	133	10	un	un	ADJ
aiti-345	133	11	-	-	ADJ
aiti-345	133	12	condensed	condensed	ADJ
aiti-345	133	13	instances	instance	NOUN
aiti-345	133	14	,	,	PUNCT
aiti-345	133	15	using	use	VERB
aiti-345	133	16	the	the	DET
aiti-345	133	17	nearest	near	ADJ
aiti-345	133	18	neighbor	neighbor	NOUN
aiti-345	133	19	voting	vote	VERB
aiti-345	133	20	to	to	PART
aiti-345	133	21	renew	renew	VERB
aiti-345	133	22	the	the	DET
aiti-345	133	23	condensed	condense	VERB
aiti-345	133	24	instances	instance	NOUN
aiti-345	133	25	;	;	PUNCT
aiti-345	133	26	otherwise	otherwise	ADV
aiti-345	133	27	,	,	PUNCT
aiti-345	133	28	no	no	PRON
aiti-345	133	29	more	more	ADV
aiti-345	133	30	new	new	ADJ
aiti-345	133	31	data	datum	NOUN
aiti-345	133	32	is	be	AUX
aiti-345	133	33	joined	join	VERB
aiti-345	133	34	and	and	CCONJ
aiti-345	133	35	go	go	VERB
aiti-345	133	36	to	to	PART
aiti-345	133	37	step	step	VERB
aiti-345	133	38	2	2	NUM
aiti-345	133	39	.	.	NUM
aiti-345	133	40	3.4	3.4	NUM
aiti-345	133	41	.	.	PUNCT
aiti-345	134	1	the	the	DET
aiti-345	134	2	proposed	propose	VERB
aiti-345	134	3	framework	framework	NOUN
aiti-345	134	4	for	for	ADP
aiti-345	134	5	svm	svm	ADJ
aiti-345	134	6	classifier	classifier	NOUN
aiti-345	134	7	based	base	VERB
aiti-345	134	8	on	on	ADP
aiti-345	134	9	the	the	DET
aiti-345	134	10	particle	particle	NOUN
aiti-345	134	11	representation	representation	NOUN
aiti-345	134	12	,	,	PUNCT
aiti-345	134	13	fitness	fitness	NOUN
aiti-345	134	14	definition	definition	NOUN
aiti-345	134	15	,	,	PUNCT
aiti-345	134	16	pso	pso	NOUN
aiti-345	134	17	with	with	ADP
aiti-345	134	18	disturbance	disturbance	NOUN
aiti-345	134	19	operation	operation	NOUN
aiti-345	134	20	and	and	CCONJ
aiti-345	134	21	aci	aci	PROPN
aiti-345	134	22	scheme	scheme	NOUN
aiti-345	134	23	mentioned	mention	VERB
aiti-345	134	24	above	above	ADV
aiti-345	134	25	,	,	PUNCT
aiti-345	134	26	details	detail	NOUN
aiti-345	134	27	of	of	ADP
aiti-345	134	28	the	the	DET
aiti-345	134	29	proposed	propose	VERB
aiti-345	134	30	hybrid	hybrid	NOUN
aiti-345	134	31	framework	framework	NOUN
aiti-345	134	32	for	for	ADP
aiti-345	134	33	svm	svm	ADJ
aiti-345	134	34	procedure	procedure	NOUN
aiti-345	134	35	from	from	ADP
aiti-345	134	36	step	step	NOUN
aiti-345	134	37	1	1	NUM
aiti-345	134	38	to	to	PART
aiti-345	134	39	step	step	VERB
aiti-345	134	40	9	9	NUM
aiti-345	134	41	are	be	AUX
aiti-345	134	42	described	describe	VERB
aiti-345	134	43	as	as	ADP
aiti-345	134	44	follows	follow	VERB
aiti-345	134	45	.	.	PUNCT
aiti-345	135	1	step	step	NOUN
aiti-345	135	2	1	1	NUM
aiti-345	135	3	:	:	PUNCT
aiti-345	135	4	data	datum	NOUN
aiti-345	135	5	preparation	preparation	NOUN
aiti-345	135	6	given	give	VERB
aiti-345	135	7	a	a	DET
aiti-345	135	8	dataset	dataset	NOUN
aiti-345	135	9	d	d	NOUN
aiti-345	135	10	is	be	AUX
aiti-345	135	11	considered	consider	VERB
aiti-345	135	12	using	use	VERB
aiti-345	135	13	the	the	DET
aiti-345	135	14	10	10	NUM
aiti-345	135	15	fold	fold	PROPN
aiti-345	135	16	cross	cross	NOUN
aiti-345	135	17	validation	validation	NOUN
aiti-345	135	18	to	to	PART
aiti-345	135	19	split	split	VERB
aiti-345	135	20	the	the	DET
aiti-345	135	21	data	datum	NOUN
aiti-345	135	22	into	into	ADP
aiti-345	135	23	10	10	NUM
aiti-345	135	24	groups	group	NOUN
aiti-345	135	25	.	.	PUNCT
aiti-345	136	1	each	each	DET
aiti-345	136	2	group	group	NOUN
aiti-345	136	3	contains	contain	VERB
aiti-345	136	4	training	training	NOUN
aiti-345	136	5	and	and	CCONJ
aiti-345	136	6	testing	testing	NOUN
aiti-345	136	7	sets	set	NOUN
aiti-345	136	8	.	.	PUNCT
aiti-345	137	1	the	the	DET
aiti-345	137	2	training	training	NOUN
aiti-345	137	3	and	and	CCONJ
aiti-345	137	4	testing	testing	NOUN
aiti-345	137	5	sets	set	NOUN
aiti-345	137	6	are	be	AUX
aiti-345	137	7	represented	represent	VERB
aiti-345	137	8	as	as	ADP
aiti-345	137	9	traind	traind	NOUN
aiti-345	137	10	and	and	CCONJ
aiti-345	137	11	testd	testd	NOUN
aiti-345	137	12	,	,	PUNCT
aiti-345	137	13	respectively	respectively	ADV
aiti-345	137	14	.	.	PUNCT
aiti-345	138	1	step	step	NOUN
aiti-345	138	2	2	2	NUM
aiti-345	138	3	:	:	PUNCT
aiti-345	138	4	hybrid	hybrid	ADJ
aiti-345	138	5	pso	pso	NOUN
aiti-345	138	6	initialization	initialization	NOUN
aiti-345	138	7	and	and	CCONJ
aiti-345	138	8	parameters	parameter	NOUN
aiti-345	138	9	setting	set	VERB
aiti-345	138	10	set	set	VERB
aiti-345	138	11	the	the	DET
aiti-345	138	12	pso	pso	NOUN
aiti-345	138	13	parameters	parameter	NOUN
aiti-345	138	14	including	include	VERB
aiti-345	138	15	the	the	DET
aiti-345	138	16	number	number	NOUN
aiti-345	138	17	of	of	ADP
aiti-345	138	18	iterations	iteration	NOUN
aiti-345	138	19	,	,	PUNCT
aiti-345	138	20	number	number	NOUN
aiti-345	138	21	of	of	ADP
aiti-345	138	22	particles	particle	NOUN
aiti-345	138	23	,	,	PUNCT
aiti-345	138	24	velocity	velocity	NOUN
aiti-345	138	25	,	,	PUNCT
aiti-345	138	26	particle	particle	NOUN
aiti-345	138	27	dimension	dimension	NOUN
aiti-345	138	28	,	,	PUNCT
aiti-345	138	29	disturbance	disturbance	NOUN
aiti-345	138	30	rate	rate	NOUN
aiti-345	138	31	,	,	PUNCT
aiti-345	138	32	and	and	CCONJ
aiti-345	138	33	weight	weight	NOUN
aiti-345	138	34	for	for	ADP
aiti-345	138	35	fitness	fitness	NOUN
aiti-345	138	36	.	.	PUNCT
aiti-345	139	1	generate	generate	VERB
aiti-345	139	2	initial	initial	ADJ
aiti-345	139	3	hybrid	hybrid	ADJ
aiti-345	139	4	particles	particle	NOUN
aiti-345	139	5	comprised	comprise	VERB
aiti-345	139	6	of	of	ADP
aiti-345	139	7	the	the	DET
aiti-345	139	8	features	feature	NOUN
aiti-345	139	9	mask	mask	PROPN
aiti-345	139	10	,	,	PUNCT
aiti-345	139	11	cond	cond	PROPN
aiti-345	139	12	,	,	PUNCT
aiti-345	139	13	c	c	PROPN
aiti-345	139	14	and	and	CCONJ
aiti-345	139	15			PROPN
aiti-345	139	16	.	.	PUNCT
aiti-345	140	1	step	step	NOUN
aiti-345	140	2	3	3	NUM
aiti-345	140	3	:	:	PUNCT
aiti-345	140	4	condensed	condense	VERB
aiti-345	140	5	instances	instance	NOUN
aiti-345	140	6	selection	selection	NOUN
aiti-345	140	7	via	via	ADP
aiti-345	140	8	the	the	DET
aiti-345	140	9	aci	aci	PROPN
aiti-345	140	10	scheme	scheme	NOUN
aiti-345	140	11	according	accord	VERB
aiti-345	140	12	to	to	ADP
aiti-345	140	13	the	the	DET
aiti-345	140	14	condensed	condense	VERB
aiti-345	140	15	instances	instance	NOUN
aiti-345	140	16	coefficient	coefficient	NOUN
aiti-345	140	17	cond	cond	PROPN
aiti-345	140	18	represented	represent	VERB
aiti-345	140	19	in	in	ADP
aiti-345	140	20	the	the	DET
aiti-345	140	21	particle	particle	NOUN
aiti-345	140	22	and	and	CCONJ
aiti-345	140	23	calculated	calculate	VERB
aiti-345	140	24	from	from	ADP
aiti-345	140	25	step	step	NOUN
aiti-345	140	26	2	2	NUM
aiti-345	140	27	,	,	PUNCT
aiti-345	140	28	when	when	SCONJ
aiti-345	140	29	all	all	DET
aiti-345	140	30	the	the	DET
aiti-345	140	31	condensed	condense	VERB
aiti-345	140	32	instances	instance	NOUN
aiti-345	140	33	are	be	AUX
aiti-345	140	34	computed	compute	VERB
aiti-345	140	35	,	,	PUNCT
aiti-345	140	36	the	the	DET
aiti-345	140	37	nearest	near	ADJ
aiti-345	140	38	neighbor	neighbor	NOUN
aiti-345	140	39	voting	voting	NOUN
aiti-345	140	40	is	be	AUX
aiti-345	140	41	used	use	VERB
aiti-345	140	42	to	to	PART
aiti-345	140	43	renew	renew	VERB
aiti-345	140	44	the	the	DET
aiti-345	140	45	condensed	condense	VERB
aiti-345	140	46	instances	instance	NOUN
aiti-345	140	47	set	set	VERB
aiti-345	140	48	.	.	PUNCT
aiti-345	141	1	step	step	NOUN
aiti-345	141	2	4	4	NUM
aiti-345	141	3	:	:	PUNCT
aiti-345	141	4	feature	feature	NOUN
aiti-345	141	5	scaling	scale	VERB
aiti-345	141	6	feature	feature	NOUN
aiti-345	141	7	scaling	scale	VERB
aiti-345	141	8	is	be	AUX
aiti-345	141	9	to	to	PART
aiti-345	141	10	properly	properly	ADV
aiti-345	141	11	reveal	reveal	VERB
aiti-345	141	12	the	the	DET
aiti-345	141	13	interactions	interaction	NOUN
aiti-345	141	14	between	between	ADP
aiti-345	141	15	features	feature	NOUN
aiti-345	141	16	and	and	CCONJ
aiti-345	141	17	to	to	PART
aiti-345	141	18	avoid	avoid	VERB
aiti-345	141	19	attributes	attribute	NOUN
aiti-345	141	20	in	in	ADP
aiti-345	141	21	greater	great	ADJ
aiti-345	141	22	numeric	numeric	ADJ
aiti-345	141	23	ranges	range	NOUN
aiti-345	141	24	dominating	dominate	VERB
aiti-345	141	25	those	those	PRON
aiti-345	141	26	in	in	ADP
aiti-345	141	27	smaller	small	ADJ
aiti-345	141	28	numeric	numeric	ADJ
aiti-345	141	29	ranges	range	NOUN
aiti-345	141	30	.	.	PUNCT
aiti-345	142	1	normalization	normalization	NOUN
aiti-345	142	2	by	by	ADP
aiti-345	142	3	eq	eq	PROPN
aiti-345	142	4	.	.	PUNCT
aiti-345	143	1	(	(	PUNCT
aiti-345	143	2	8)	8)	NUM
aiti-345	143	3	can	can	AUX
aiti-345	143	4	be	be	AUX
aiti-345	143	5	linearly	linearly	ADV
aiti-345	143	6	scaled	scale	VERB
aiti-345	143	7	to	to	AUX
aiti-345	143	8	range	range	VERB
aiti-345	143	9	[	[	X
aiti-345	143	10	-1	-1	X
aiti-345	143	11	,	,	PUNCT
aiti-345	143	12	+1	+1	X
aiti-345	143	13	]	]	PUNCT
aiti-345	143	14	or	or	CCONJ
aiti-345	143	15	[	[	X
aiti-345	143	16	0	0	NUM
aiti-345	143	17	,	,	PUNCT
aiti-345	143	18	1	1	NUM
aiti-345	143	19	]	]	PUNCT
aiti-345	143	20	,	,	PUNCT
aiti-345	143	21	where	where	SCONJ
aiti-345	143	22	(	(	PUNCT
aiti-345	143	23	)	)	PUNCT
aiti-345	143	24	j	j	PROPN
aiti-345	143	25	ia	ia	PROPN
aiti-345	143	26	x	x	PROPN
aiti-345	143	27	is	be	AUX
aiti-345	143	28	the	the	DET
aiti-345	143	29	original	original	ADJ
aiti-345	143	30	attribute	attribute	NOUN
aiti-345	143	31	value	value	NOUN
aiti-345	143	32	of	of	ADP
aiti-345	143	33	feature	feature	NOUN
aiti-345	143	34	ix	ix	ADV
aiti-345	143	35	,	,	PUNCT
aiti-345	143	36	'	'	PUNCT
aiti-345	143	37	(	(	PUNCT
aiti-345	143	38	)	)	PUNCT
aiti-345	143	39	j	j	PROPN
aiti-345	143	40	ia	ia	PROPN
aiti-345	143	41	x	x	PRON
aiti-345	143	42	is	be	AUX
aiti-345	143	43	scaled	scale	VERB
aiti-345	143	44	value	value	NOUN
aiti-345	143	45	,	,	PUNCT
aiti-345	143	46	max	max	PROPN
aiti-345	143	47	j	j	PROPN
aiti-345	143	48	and	and	CCONJ
aiti-345	143	49	min	min	PROPN
aiti-345	143	50	j	j	PROPN
aiti-345	143	51	correspond	correspond	VERB
aiti-345	143	52	to	to	ADP
aiti-345	143	53	the	the	DET
aiti-345	143	54	maximum	maximum	ADJ
aiti-345	143	55	and	and	CCONJ
aiti-345	143	56	minimum	minimum	ADJ
aiti-345	143	57	values	value	NOUN
aiti-345	143	58	for	for	ADP
aiti-345	143	59	ja	ja	PROPN
aiti-345	143	60	over	over	ADP
aiti-345	143	61	all	all	DET
aiti-345	143	62	samples	sample	NOUN
aiti-345	143	63	.	.	PUNCT
aiti-345	143	64	'	'	PUNCT
aiti-345	144	1	(	(	PUNCT
aiti-345	144	2	)	)	PUNCT
aiti-345	144	3	min	min	NOUN
aiti-345	144	4	(	(	PUNCT
aiti-345	144	5	)	)	PUNCT
aiti-345	144	6	,	,	PUNCT
aiti-345	144	7	max	max	PROPN
aiti-345	144	8	min	min	PROPN
aiti-345	144	9	j	j	PROPN
aiti-345	145	1	i	i	PRON
aiti-345	145	2	j	j	PROPN
aiti-345	146	1	j	j	INTJ
aiti-345	146	2	i	i	PRON
aiti-345	146	3	j	j	PROPN
aiti-345	147	1	j	j	PROPN
aiti-345	147	2	a	a	X
aiti-345	147	3	x	x	X
aiti-345	147	4	a	a	X
aiti-345	147	5	x	x	X
aiti-345	147	6	i	i	PRON
aiti-345	147	7			VERB
aiti-345	147	8			NUM
aiti-345	147	9			ADJ
aiti-345	147	10			NOUN
aiti-345	147	11	(	(	PUNCT
aiti-345	147	12	8)	8)	NUM
aiti-345	147	13	step	step	NOUN
aiti-345	147	14	5	5	NUM
aiti-345	147	15	:	:	PUNCT
aiti-345	147	16	feature	feature	NOUN
aiti-345	147	17	selection	selection	NOUN
aiti-345	147	18	according	accord	VERB
aiti-345	147	19	to	to	ADP
aiti-345	147	20	the	the	DET
aiti-345	147	21	feature	feature	NOUN
aiti-345	147	22	mask	mask	NOUN
aiti-345	147	23	,	,	PUNCT
aiti-345	147	24	which	which	PRON
aiti-345	147	25	is	be	AUX
aiti-345	147	26	represented	represent	VERB
aiti-345	147	27	in	in	ADP
aiti-345	147	28	the	the	DET
aiti-345	147	29	particle	particle	NOUN
aiti-345	147	30	from	from	ADP
aiti-345	147	31	step	step	NOUN
aiti-345	147	32	2	2	NUM
aiti-345	147	33	,	,	PUNCT
aiti-345	147	34	is	be	AUX
aiti-345	147	35	to	to	PART
aiti-345	147	36	select	select	VERB
aiti-345	147	37	input	input	NOUN
aiti-345	147	38	features	feature	NOUN
aiti-345	147	39	for	for	ADP
aiti-345	147	40	training	training	NOUN
aiti-345	147	41	set	set	NOUN
aiti-345	147	42	traind	traind	NOUN
aiti-345	147	43	and	and	CCONJ
aiti-345	147	44	testing	testing	NOUN
aiti-345	147	45	set	set	VERB
aiti-345	147	46	testd	testd	NOUN
aiti-345	147	47	.	.	PUNCT
aiti-345	148	1	the	the	DET
aiti-345	148	2	selected	select	VERB
aiti-345	148	3	features	feature	NOUN
aiti-345	148	4	subset	subset	VERB
aiti-345	148	5	can	can	AUX
aiti-345	148	6	be	be	AUX
aiti-345	148	7	denoted	denote	VERB
aiti-345	148	8	as	as	ADP
aiti-345	148	9	_	_	PROPN
aiti-345	148	10	f	f	PROPN
aiti-345	148	11	traind	traind	NOUN
aiti-345	148	12	and	and	CCONJ
aiti-345	148	13	_	_	NOUN
aiti-345	148	14	f	f	NOUN
aiti-345	148	15	testd	testd	NOUN
aiti-345	148	16	,	,	PUNCT
aiti-345	148	17	respectively	respectively	ADV
aiti-345	148	18	.	.	PUNCT
aiti-345	149	1	step	step	NOUN
aiti-345	149	2	6	6	NUM
aiti-345	149	3	:	:	PUNCT
aiti-345	149	4	to	to	PART
aiti-345	149	5	train	train	VERB
aiti-345	149	6	and	and	CCONJ
aiti-345	149	7	test	test	VERB
aiti-345	149	8	for	for	ADP
aiti-345	149	9	svm	svm	ADJ
aiti-345	149	10	classifier	classifier	NOUN
aiti-345	149	11	for	for	ADP
aiti-345	149	12	the	the	DET
aiti-345	149	13	parameters	parameter	NOUN
aiti-345	149	14	cond	cond	PROPN
aiti-345	149	15	,	,	PUNCT
aiti-345	149	16	c	c	PROPN
aiti-345	149	17	and	and	CCONJ
aiti-345	149	18			NUM
aiti-345	149	19	which	which	PRON
aiti-345	149	20	are	be	AUX
aiti-345	149	21	represented	represent	VERB
aiti-345	149	22	in	in	ADP
aiti-345	149	23	the	the	DET
aiti-345	149	24	particle	particle	NOUN
aiti-345	149	25	,	,	PUNCT
aiti-345	149	26	to	to	PART
aiti-345	149	27	train	train	VERB
aiti-345	149	28	the	the	DET
aiti-345	149	29	svm	svm	ADJ
aiti-345	149	30	classifier	classifier	NOUN
aiti-345	149	31	on	on	ADP
aiti-345	149	32	the	the	DET
aiti-345	149	33	training	training	NOUN
aiti-345	149	34	dataset	dataset	VERB
aiti-345	149	35	_	_	PROPN
aiti-345	149	36	f	f	PROPN
aiti-345	149	37	traind	traind	NOUN
aiti-345	149	38	,	,	PUNCT
aiti-345	149	39	then	then	ADV
aiti-345	149	40	the	the	DET
aiti-345	149	41	classification	classification	NOUN
aiti-345	149	42	accuracy	accuracy	NOUN
aiti-345	149	43	cca	cca	PROPN
aiti-345	149	44	for	for	ADP
aiti-345	149	45	svm	svm	PROPN
aiti-345	149	46	on	on	ADP
aiti-345	149	47	the	the	DET
aiti-345	149	48	testing	testing	NOUN
aiti-345	149	49	dataset	dataset	VERB
aiti-345	149	50	_	_	PRON
aiti-345	149	51	f	f	NOUN
aiti-345	149	52	testd	testd	NOUN
aiti-345	149	53	can	can	AUX
aiti-345	149	54	be	be	AUX
aiti-345	149	55	evaluated	evaluate	VERB
aiti-345	149	56	.	.	PUNCT
aiti-345	150	1	step	step	NOUN
aiti-345	150	2	7	7	NUM
aiti-345	150	3	:	:	PUNCT
aiti-345	150	4	fitness	fitness	NOUN
aiti-345	150	5	evaluation	evaluation	NOUN
aiti-345	150	6	for	for	ADP
aiti-345	150	7	each	each	DET
aiti-345	150	8	particle	particle	NOUN
aiti-345	150	9	,	,	PUNCT
aiti-345	150	10	the	the	DET
aiti-345	150	11	fitness	fitness	NOUN
aiti-345	150	12	value	value	NOUN
aiti-345	150	13	is	be	AUX
aiti-345	150	14	to	to	PART
aiti-345	150	15	be	be	AUX
aiti-345	150	16	calculated	calculate	VERB
aiti-345	150	17	by	by	ADP
aiti-345	150	18	the	the	DET
aiti-345	150	19	eq	eq	NOUN
aiti-345	150	20	.	.	PUNCT
aiti-345	151	1	(	(	PUNCT
aiti-345	151	2	6	6	NUM
aiti-345	151	3	)	)	PUNCT
aiti-345	151	4	.	.	PUNCT
aiti-345	152	1	the	the	DET
aiti-345	152	2	optimal	optimal	ADJ
aiti-345	152	3	fitness	fitness	NOUN
aiti-345	152	4	value	value	NOUN
aiti-345	152	5	can	can	AUX
aiti-345	152	6	be	be	AUX
aiti-345	152	7	stored	store	VERB
aiti-345	152	8	on	on	ADP
aiti-345	152	9	the	the	DET
aiti-345	152	10	evolution	evolution	NOUN
aiti-345	152	11	process	process	NOUN
aiti-345	152	12	of	of	ADP
aiti-345	152	13	pso	pso	NOUN
aiti-345	152	14	to	to	PART
aiti-345	152	15	search	search	VERB
aiti-345	152	16	for	for	ADP
aiti-345	152	17	the	the	DET
aiti-345	152	18	better	well	ADJ
aiti-345	152	19	fitness	fitness	NOUN
aiti-345	152	20	of	of	ADP
aiti-345	152	21	particle	particle	NOUN
aiti-345	152	22	in	in	ADP
aiti-345	152	23	the	the	DET
aiti-345	152	24	next	next	ADJ
aiti-345	152	25	particles	particle	NOUN
aiti-345	152	26	evolution	evolution	NOUN
aiti-345	152	27	procedure	procedure	NOUN
aiti-345	152	28	.	.	PUNCT
aiti-345	153	1	step	step	NOUN
aiti-345	153	2	8	8	NUM
aiti-345	153	3	:	:	PUNCT
aiti-345	153	4	termination	termination	NOUN
aiti-345	153	5	criteria	criterion	NOUN
aiti-345	153	6	when	when	SCONJ
aiti-345	153	7	the	the	DET
aiti-345	153	8	number	number	NOUN
aiti-345	153	9	of	of	ADP
aiti-345	153	10	iteration	iteration	NOUN
aiti-345	153	11	reaches	reach	VERB
aiti-345	153	12	the	the	DET
aiti-345	153	13	pre	pre	ADJ
aiti-345	153	14	-	-	VERB
aiti-345	153	15	defined	define	VERB
aiti-345	153	16	maximum	maximum	ADJ
aiti-345	153	17	number	number	NOUN
aiti-345	153	18	or	or	CCONJ
aiti-345	153	19	a	a	DET
aiti-345	153	20	termination	termination	NOUN
aiti-345	153	21	criterion	criterion	NOUN
aiti-345	153	22	is	be	AUX
aiti-345	153	23	satisfied	satisfied	ADJ
aiti-345	153	24	,	,	PUNCT
aiti-345	153	25	the	the	DET
aiti-345	153	26	best	good	ADJ
aiti-345	153	27	solution	solution	NOUN
aiti-345	153	28	bestg	bestg	NOUN
aiti-345	153	29	is	be	AUX
aiti-345	153	30	obtained	obtain	VERB
aiti-345	153	31	,	,	PUNCT
aiti-345	153	32	and	and	CCONJ
aiti-345	153	33	the	the	DET
aiti-345	153	34	program	program	NOUN
aiti-345	153	35	ends	end	VERB
aiti-345	153	36	;	;	PUNCT
aiti-345	153	37	otherwise	otherwise	ADV
aiti-345	153	38	,	,	PUNCT
aiti-345	153	39	go	go	VERB
aiti-345	153	40	to	to	ADP
aiti-345	153	41	the	the	DET
aiti-345	153	42	next	next	ADJ
aiti-345	153	43	step	step	NOUN
aiti-345	153	44	.	.	PUNCT
aiti-345	154	1	advances	advance	NOUN
aiti-345	154	2	in	in	ADP
aiti-345	154	3	technology	technology	NOUN
aiti-345	154	4	innovation	innovation	NOUN
aiti-345	154	5	,	,	PUNCT
aiti-345	154	6	vol	vol	NOUN
aiti-345	154	7	.	.	PROPN
aiti-345	154	8	1	1	NUM
aiti-345	154	9	,	,	PUNCT
aiti-345	154	10	no	no	INTJ
aiti-345	154	11	.	.	NOUN
aiti-345	154	12	2	2	NUM
aiti-345	154	13	,	,	PUNCT
aiti-345	154	14	2016	2016	NUM
aiti-345	154	15	,	,	PUNCT
aiti-345	154	16	pp	pp	ADJ
aiti-345	154	17	.	.	PUNCT
aiti-345	155	1	53	53	NUM
aiti-345	155	2	57	57	NUM
aiti-345	155	3	56	56	NUM
aiti-345	155	4	copyright	copyright	NOUN
aiti-345	155	5	©	©	PROPN
aiti-345	155	6	taeti	taeti	PROPN
aiti-345	155	7	copyright	copyright	NOUN
aiti-345	155	8	©	©	PROPN
aiti-345	155	9	taeti	taeti	PROPN
aiti-345	156	1	copyright	copyright	NOUN
aiti-345	156	2	©	©	PROPN
aiti-345	156	3	taeti	taeti	PROPN
aiti-345	157	1	copyright	copyright	NOUN
aiti-345	157	2	©	©	PROPN
aiti-345	157	3	taeti	taeti	PROPN
aiti-345	158	1	copyright	copyright	NOUN
aiti-345	158	2	©	©	PROPN
aiti-345	158	3	taeti	taeti	PROPN
aiti-345	158	4	step	step	VERB
aiti-345	158	5	9	9	NUM
aiti-345	158	6	:	:	PUNCT
aiti-345	158	7	hybrid	hybrid	ADJ
aiti-345	158	8	pso	pso	NOUN
aiti-345	158	9	operation	operation	NOUN
aiti-345	158	10	in	in	ADP
aiti-345	158	11	the	the	DET
aiti-345	158	12	evolution	evolution	NOUN
aiti-345	158	13	process	process	NOUN
aiti-345	158	14	,	,	PUNCT
aiti-345	158	15	discrete	discrete	NOUN
aiti-345	158	16	-	-	PUNCT
aiti-345	158	17	valued	value	VERB
aiti-345	158	18	and	and	CCONJ
aiti-345	158	19	continuous	continuous	ADJ
aiti-345	158	20	-	-	PUNCT
aiti-345	158	21	valued	value	VERB
aiti-345	158	22	dimension	dimension	NOUN
aiti-345	158	23	of	of	ADP
aiti-345	158	24	hpso	hpso	NOUN
aiti-345	158	25	with	with	ADP
aiti-345	158	26	the	the	DET
aiti-345	158	27	disturbance	disturbance	NOUN
aiti-345	158	28	operator	operator	NOUN
aiti-345	158	29	continued	continue	VERB
aiti-345	158	30	to	to	PART
aiti-345	158	31	be	be	AUX
aiti-345	158	32	applied	apply	VERB
aiti-345	158	33	for	for	ADP
aiti-345	158	34	searching	search	VERB
aiti-345	158	35	better	well	ADJ
aiti-345	158	36	particle	particle	NOUN
aiti-345	158	37	solutions	solution	NOUN
aiti-345	158	38	.	.	PUNCT
aiti-345	159	1	4	4	X
aiti-345	159	2	.	.	X
aiti-345	159	3	experiment	experiment	NOUN
aiti-345	159	4	results	result	VERB
aiti-345	159	5	4.1	4.1	NUM
aiti-345	159	6	.	.	PUNCT
aiti-345	160	1	dataset	dataset	NOUN
aiti-345	160	2	and	and	CCONJ
aiti-345	160	3	system	system	NOUN
aiti-345	160	4	description	description	NOUN
aiti-345	160	5	to	to	PART
aiti-345	160	6	measure	measure	VERB
aiti-345	160	7	the	the	DET
aiti-345	160	8	performance	performance	NOUN
aiti-345	160	9	of	of	ADP
aiti-345	160	10	the	the	DET
aiti-345	160	11	developed	develop	VERB
aiti-345	160	12	hybrid	hybrid	ADJ
aiti-345	160	13	framework	framework	NOUN
aiti-345	160	14	,	,	PUNCT
aiti-345	160	15	several	several	ADJ
aiti-345	160	16	real	real	ADJ
aiti-345	160	17	benchmark	benchmark	NOUN
aiti-345	160	18	datasets	dataset	NOUN
aiti-345	160	19	in	in	ADP
aiti-345	160	20	table	table	NOUN
aiti-345	160	21	2	2	NUM
aiti-345	160	22	are	be	AUX
aiti-345	160	23	conducted	conduct	VERB
aiti-345	160	24	to	to	PART
aiti-345	160	25	verify	verify	VERB
aiti-345	160	26	the	the	DET
aiti-345	160	27	effectiveness	effectiveness	NOUN
aiti-345	160	28	of	of	ADP
aiti-345	160	29	performance	performance	NOUN
aiti-345	160	30	.	.	PUNCT
aiti-345	161	1	these	these	DET
aiti-345	161	2	table	table	NOUN
aiti-345	161	3	2	2	NUM
aiti-345	161	4	the	the	DET
aiti-345	161	5	uci	uci	PROPN
aiti-345	161	6	benchmark	benchmark	NOUN
aiti-345	161	7	datasets	dataset	NOUN
aiti-345	161	8	[	[	X
aiti-345	161	9	7	7	X
aiti-345	161	10	]	]	X
aiti-345	161	11	datasets	dataset	NOUN
aiti-345	161	12	are	be	AUX
aiti-345	161	13	partitioned	partition	VERB
aiti-345	161	14	using	use	VERB
aiti-345	161	15	the	the	DET
aiti-345	161	16	10	10	NUM
aiti-345	161	17	-	-	ADJ
aiti-345	161	18	fold	fold	ADJ
aiti-345	161	19	cross	cross	NOUN
aiti-345	161	20	validation	validation	NOUN
aiti-345	161	21	.	.	PUNCT
aiti-345	162	1	our	our	PRON
aiti-345	162	2	implementation	implementation	NOUN
aiti-345	162	3	platform	platform	NOUN
aiti-345	162	4	was	be	AUX
aiti-345	162	5	implemented	implement	VERB
aiti-345	162	6	on	on	ADP
aiti-345	162	7	matlab	matlab	PROPN
aiti-345	162	8	2013	2013	NUM
aiti-345	162	9	,	,	PUNCT
aiti-345	162	10	by	by	ADP
aiti-345	162	11	extending	extend	VERB
aiti-345	162	12	the	the	DET
aiti-345	162	13	libsvm	libsvm	NOUN
aiti-345	162	14	which	which	PRON
aiti-345	162	15	is	be	AUX
aiti-345	162	16	originally	originally	ADV
aiti-345	162	17	designed	design	VERB
aiti-345	162	18	by	by	ADP
aiti-345	162	19	chang	chang	PROPN
aiti-345	162	20	and	and	CCONJ
aiti-345	162	21	lin	lin	PROPN
aiti-345	163	1	[	[	X
aiti-345	163	2	12	12	NUM
aiti-345	163	3	]	]	PUNCT
aiti-345	163	4	.	.	PUNCT
aiti-345	164	1	through	through	ADP
aiti-345	164	2	initial	initial	ADJ
aiti-345	164	3	experiment	experiment	NOUN
aiti-345	164	4	,	,	PUNCT
aiti-345	164	5	the	the	DET
aiti-345	164	6	parameter	parameter	NOUN
aiti-345	164	7	values	value	NOUN
aiti-345	164	8	of	of	ADP
aiti-345	164	9	the	the	DET
aiti-345	164	10	pso	pso	NOUN
aiti-345	164	11	were	be	AUX
aiti-345	164	12	set	set	VERB
aiti-345	164	13	as	as	SCONJ
aiti-345	164	14	follows	follow	VERB
aiti-345	164	15	.	.	PUNCT
aiti-345	165	1	the	the	DET
aiti-345	165	2	swarm	swarm	NOUN
aiti-345	165	3	size	size	NOUN
aiti-345	165	4	is	be	AUX
aiti-345	165	5	set	set	VERB
aiti-345	165	6	to	to	ADP
aiti-345	165	7	100	100	NUM
aiti-345	165	8	particles	particle	NOUN
aiti-345	165	9	.	.	PUNCT
aiti-345	166	1	the	the	DET
aiti-345	166	2	searching	searching	NOUN
aiti-345	166	3	ranges	range	NOUN
aiti-345	166	4	of	of	ADP
aiti-345	166	5	continuous	continuous	ADJ
aiti-345	166	6	type	type	NOUN
aiti-345	166	7	dimension	dimension	NOUN
aiti-345	166	8	parameters	parameter	NOUN
aiti-345	166	9	are	be	AUX
aiti-345	166	10	:	:	PUNCT
aiti-345	167	1	[	[	X
aiti-345	167	2	0,1]cond	0,1]cond	NUM
aiti-345	167	3			NOUN
aiti-345	167	4	,	,	PUNCT
aiti-345	167	5	2	2	NUM
aiti-345	167	6	4[10	4[10	NUM
aiti-345	167	7	,	,	PUNCT
aiti-345	167	8	10	10	NUM
aiti-345	167	9	]	]	SYM
aiti-345	167	10	c	c	X
aiti-345	167	11			PROPN
aiti-345	167	12	and	and	CCONJ
aiti-345	167	13	4	4	NUM
aiti-345	167	14	4[10	4[10	NUM
aiti-345	167	15	,	,	PUNCT
aiti-345	167	16	10	10	NUM
aiti-345	167	17	]	]	PUNCT
aiti-345	167	18			NUM
aiti-345	167	19			NOUN
aiti-345	167	20	.	.	PUNCT
aiti-345	168	1	the	the	DET
aiti-345	168	2	discrete	discrete	ADJ
aiti-345	168	3	type	type	NOUN
aiti-345	168	4	particle	particle	NOUN
aiti-345	168	5	for	for	ADP
aiti-345	168	6	features	feature	NOUN
aiti-345	168	7	mask	mask	NOUN
aiti-345	168	8	,	,	PUNCT
aiti-345	168	9	we	we	PRON
aiti-345	168	10	set	set	VERB
aiti-345	168	11	min	min	PROPN
aiti-345	168	12	max	max	PROPN
aiti-345	168	13	[	[	X
aiti-345	168	14	,	,	PUNCT
aiti-345	168	15	]	]	X
aiti-345	168	16	[	[	PUNCT
aiti-345	168	17	6,6]v	6,6]v	NUM
aiti-345	168	18	v	v	ADP
aiti-345	168	19			PROPN
aiti-345	168	20			PROPN
aiti-345	168	21	,	,	PUNCT
aiti-345	168	22	which	which	PRON
aiti-345	168	23	yields	yield	VERB
aiti-345	168	24	a	a	DET
aiti-345	168	25	range	range	NOUN
aiti-345	168	26	of	of	ADP
aiti-345	168	27	[	[	X
aiti-345	168	28	0.9975,0.0025	0.9975,0.0025	X
aiti-345	168	29	]	]	PUNCT
aiti-345	168	30	using	use	VERB
aiti-345	168	31	the	the	DET
aiti-345	168	32	sigmoid	sigmoid	NOUN
aiti-345	168	33	limiting	limit	VERB
aiti-345	168	34	transformation	transformation	NOUN
aiti-345	168	35	by	by	ADP
aiti-345	168	36	eq	eq	PROPN
aiti-345	168	37	.	.	PUNCT
aiti-345	169	1	(	(	PUNCT
aiti-345	169	2	7	7	NUM
aiti-345	169	3	)	)	PUNCT
aiti-345	169	4	.	.	PUNCT
aiti-345	170	1	both	both	CCONJ
aiti-345	170	2	the	the	DET
aiti-345	170	3	cognition	cognition	NOUN
aiti-345	170	4	learning	learn	VERB
aiti-345	170	5	factor	factor	NOUN
aiti-345	170	6	1c	1c	NOUN
aiti-345	170	7	and	and	CCONJ
aiti-345	170	8	the	the	DET
aiti-345	170	9	social	social	ADJ
aiti-345	170	10	learning	learning	NOUN
aiti-345	170	11	factor	factor	NOUN
aiti-345	170	12	2c	2c	NOUN
aiti-345	170	13	are	be	AUX
aiti-345	170	14	set	set	VERB
aiti-345	170	15	to	to	ADP
aiti-345	170	16	2	2	NUM
aiti-345	170	17	.	.	PUNCT
aiti-345	171	1	the	the	DET
aiti-345	171	2	disturbance	disturbance	NOUN
aiti-345	171	3	rate	rate	NOUN
aiti-345	171	4	is	be	AUX
aiti-345	171	5	0.05	0.05	NUM
aiti-345	171	6	,	,	PUNCT
aiti-345	171	7	and	and	CCONJ
aiti-345	171	8	the	the	DET
aiti-345	171	9	number	number	NOUN
aiti-345	171	10	of	of	ADP
aiti-345	171	11	generation	generation	NOUN
aiti-345	171	12	is	be	AUX
aiti-345	171	13	500	500	NUM
aiti-345	171	14	.	.	PUNCT
aiti-345	172	1	the	the	DET
aiti-345	172	2	inertia	inertia	NOUN
aiti-345	172	3	weight	weight	NOUN
aiti-345	172	4	factor	factor	NOUN
aiti-345	172	5	min	min	PROPN
aiti-345	172	6	0.4w	0.4w	PROPN
aiti-345	172	7			PROPN
aiti-345	172	8	and	and	CCONJ
aiti-345	172	9	max	max	PROPN
aiti-345	172	10	0.9w	0.9w	PROPN
aiti-345	172	11			PROPN
aiti-345	172	12	.	.	PUNCT
aiti-345	173	1	the	the	DET
aiti-345	173	2	linearly	linearly	ADV
aiti-345	173	3	decreasing	decrease	VERB
aiti-345	173	4	inertia	inertia	NOUN
aiti-345	173	5	weight	weight	NOUN
aiti-345	173	6	is	be	AUX
aiti-345	173	7	set	set	VERB
aiti-345	173	8	as	as	ADP
aiti-345	173	9	eq	eq	ADP
aiti-345	173	10	.	.	PUNCT
aiti-345	174	1	(	(	PUNCT
aiti-345	174	2	9	9	NUM
aiti-345	174	3	)	)	PUNCT
aiti-345	174	4	,	,	PUNCT
aiti-345	174	5	where	where	SCONJ
aiti-345	174	6	nowi	nowi	PROPN
aiti-345	174	7	is	be	AUX
aiti-345	174	8	the	the	DET
aiti-345	174	9	current	current	ADJ
aiti-345	174	10	iteration	iteration	NOUN
aiti-345	174	11	and	and	CCONJ
aiti-345	174	12	maxi	maxi	NOUN
aiti-345	174	13	is	be	AUX
aiti-345	174	14	the	the	DET
aiti-345	174	15	pre	pre	ADJ
aiti-345	174	16	-	-	ADJ
aiti-345	174	17	defined	define	VERB
aiti-345	174	18	maximum	maximum	ADJ
aiti-345	174	19	iteration	iteration	NOUN
aiti-345	174	20	.	.	PUNCT
aiti-345	175	1	according	accord	VERB
aiti-345	175	2	to	to	ADP
aiti-345	175	3	the	the	DET
aiti-345	175	4	fitness	fitness	NOUN
aiti-345	175	5	function	function	NOUN
aiti-345	175	6	defined	define	VERB
aiti-345	175	7	by	by	ADP
aiti-345	175	8	eq	eq	PROPN
aiti-345	175	9	.	.	PUNCT
aiti-345	176	1	(	(	PUNCT
aiti-345	176	2	6	6	NUM
aiti-345	176	3	)	)	PUNCT
aiti-345	176	4	,	,	PUNCT
aiti-345	176	5	set	set	VERB
aiti-345	176	6	the	the	DET
aiti-345	176	7	accuracy	accuracy	NOUN
aiti-345	176	8	’s	’s	PART
aiti-345	176	9	weight	weight	NOUN
aiti-345	176	10	1	1	NUM
aiti-345	176	11	0.8w	0.8w	NUM
aiti-345	176	12			NOUN
aiti-345	176	13	and	and	CCONJ
aiti-345	176	14	the	the	DET
aiti-345	176	15	feature	feature	NOUN
aiti-345	176	16	’s	’s	PART
aiti-345	176	17	weight	weight	NOUN
aiti-345	176	18	2	2	NUM
aiti-345	176	19	0.2w	0.2w	NOUN
aiti-345	176	20			NOUN
aiti-345	176	21	.	.	PUNCT
aiti-345	177	1	max	max	PROPN
aiti-345	177	2	max	max	PROPN
aiti-345	177	3	min	min	PROPN
aiti-345	177	4	max	max	PROPN
aiti-345	177	5	(	(	PUNCT
aiti-345	177	6	)	)	PUNCT
aiti-345	177	7	nowi	nowi	PROPN
aiti-345	177	8	w	w	PROPN
aiti-345	177	9	w	w	PROPN
aiti-345	177	10	w	w	PROPN
aiti-345	177	11	w	w	PROPN
aiti-345	177	12	i	i	PROPN
aiti-345	177	13			PROPN
aiti-345	177	14			PROPN
aiti-345	177	15			NOUN
aiti-345	177	16	(	(	PUNCT
aiti-345	177	17	9	9	NUM
aiti-345	177	18	)	)	PUNCT
aiti-345	177	19	table	table	NOUN
aiti-345	177	20	3	3	NUM
aiti-345	177	21	the	the	DET
aiti-345	177	22	parameters	parameter	NOUN
aiti-345	177	23	setting	set	VERB
aiti-345	177	24	for	for	ADP
aiti-345	177	25	pso	pso	NOUN
aiti-345	177	26	and	and	CCONJ
aiti-345	177	27	ga	ga	PROPN
aiti-345	177	28	in	in	ADP
aiti-345	177	29	[	[	X
aiti-345	177	30	9	9	NUM
aiti-345	177	31	]	]	PUNCT
aiti-345	177	32	,	,	PUNCT
aiti-345	177	33	the	the	DET
aiti-345	177	34	authors	author	NOUN
aiti-345	177	35	presented	present	VERB
aiti-345	177	36	the	the	DET
aiti-345	177	37	ga	ga	NOUN
aiti-345	177	38	-	-	PUNCT
aiti-345	177	39	based	base	VERB
aiti-345	177	40	method	method	NOUN
aiti-345	177	41	without	without	ADP
aiti-345	177	42	aci	aci	PROPN
aiti-345	177	43	mechanism	mechanism	NOUN
aiti-345	177	44	for	for	ADP
aiti-345	177	45	searching	search	VERB
aiti-345	177	46	the	the	DET
aiti-345	177	47	bestc	bestc	NOUN
aiti-345	177	48	,	,	PUNCT
aiti-345	177	49			PROPN
aiti-345	177	50	,	,	PUNCT
aiti-345	177	51	and	and	CCONJ
aiti-345	177	52	features	feature	NOUN
aiti-345	177	53	subset	subset	VERB
aiti-345	177	54	.	.	PUNCT
aiti-345	178	1	the	the	DET
aiti-345	178	2	existing	exist	VERB
aiti-345	178	3	ga	ga	NOUN
aiti-345	178	4	-	-	PUNCT
aiti-345	178	5	svm	svm	PROPN
aiti-345	178	6	method	method	NOUN
aiti-345	178	7	without	without	ADP
aiti-345	178	8	aci	aci	PROPN
aiti-345	178	9	scheme	scheme	NOUN
aiti-345	178	10	deals	deal	NOUN
aiti-345	178	11	solely	solely	ADV
aiti-345	178	12	with	with	ADP
aiti-345	178	13	feature	feature	NOUN
aiti-345	178	14	selection	selection	NOUN
aiti-345	178	15	and	and	CCONJ
aiti-345	178	16	parameters	parameter	NOUN
aiti-345	178	17	optimization	optimization	NOUN
aiti-345	178	18	by	by	ADP
aiti-345	178	19	means	mean	NOUN
aiti-345	178	20	of	of	ADP
aiti-345	178	21	genetic	genetic	ADJ
aiti-345	178	22	algorithm	algorithm	NOUN
aiti-345	178	23	,	,	PUNCT
aiti-345	178	24	and	and	CCONJ
aiti-345	178	25	the	the	DET
aiti-345	178	26	treatment	treatment	NOUN
aiti-345	178	27	of	of	ADP
aiti-345	178	28	these	these	DET
aiti-345	178	29	redundant	redundant	ADJ
aiti-345	178	30	or	or	CCONJ
aiti-345	178	31	noisy	noisy	ADJ
aiti-345	178	32	instances	instance	NOUN
aiti-345	178	33	in	in	ADP
aiti-345	178	34	a	a	DET
aiti-345	178	35	classification	classification	NOUN
aiti-345	178	36	process	process	NOUN
aiti-345	178	37	did	do	AUX
aiti-345	178	38	not	not	PART
aiti-345	178	39	be	be	AUX
aiti-345	178	40	taken	take	VERB
aiti-345	178	41	into	into	ADP
aiti-345	178	42	account	account	NOUN
aiti-345	178	43	.	.	PUNCT
aiti-345	179	1	our	our	PRON
aiti-345	179	2	proposed	propose	VERB
aiti-345	179	3	hybrid	hybrid	NOUN
aiti-345	179	4	framework	framework	NOUN
aiti-345	179	5	has	have	AUX
aiti-345	179	6	been	be	AUX
aiti-345	179	7	tested	test	VERB
aiti-345	179	8	fairly	fairly	ADV
aiti-345	179	9	extensively	extensively	ADV
aiti-345	179	10	and	and	CCONJ
aiti-345	179	11	compared	compare	VERB
aiti-345	179	12	with	with	ADP
aiti-345	179	13	these	these	DET
aiti-345	179	14	approaches	approach	NOUN
aiti-345	179	15	including	include	VERB
aiti-345	179	16	both	both	DET
aiti-345	179	17	ga	ga	NOUN
aiti-345	179	18	-	-	PUNCT
aiti-345	179	19	svm	svm	PROPN
aiti-345	179	20	and	and	CCONJ
aiti-345	179	21	pso	pso	NOUN
aiti-345	179	22	-	-	PUNCT
aiti-345	179	23	svm	svm	NOUN
aiti-345	179	24	approaches	approach	NOUN
aiti-345	179	25	without	without	ADP
aiti-345	179	26	aci	aci	PROPN
aiti-345	179	27	mechanism	mechanism	NOUN
aiti-345	179	28	.	.	PUNCT
aiti-345	180	1	furthermore	furthermore	ADV
aiti-345	180	2	,	,	PUNCT
aiti-345	180	3	the	the	DET
aiti-345	180	4	comparison	comparison	NOUN
aiti-345	180	5	of	of	ADP
aiti-345	180	6	pso	pso	NOUN
aiti-345	180	7	and	and	CCONJ
aiti-345	180	8	ga	ga	NOUN
aiti-345	180	9	technique	technique	NOUN
aiti-345	180	10	with	with	ADP
aiti-345	180	11	the	the	DET
aiti-345	180	12	aci	aci	PROPN
aiti-345	180	13	scheme	scheme	NOUN
aiti-345	180	14	for	for	ADP
aiti-345	180	15	svm	svm	NOUN
aiti-345	180	16	is	be	AUX
aiti-345	180	17	also	also	ADV
aiti-345	180	18	presented	present	VERB
aiti-345	180	19	.	.	PUNCT
aiti-345	181	1	while	while	SCONJ
aiti-345	181	2	applying	apply	VERB
aiti-345	181	3	ga	ga	NOUN
aiti-345	181	4	algorithm	algorithm	NOUN
aiti-345	181	5	,	,	PUNCT
aiti-345	181	6	a	a	DET
aiti-345	181	7	number	number	NOUN
aiti-345	181	8	of	of	ADP
aiti-345	181	9	parameters	parameter	NOUN
aiti-345	181	10	are	be	AUX
aiti-345	181	11	required	require	VERB
aiti-345	181	12	to	to	PART
aiti-345	181	13	be	be	AUX
aiti-345	181	14	specified	specify	VERB
aiti-345	181	15	.	.	PUNCT
aiti-345	182	1	the	the	DET
aiti-345	182	2	two	two	NUM
aiti-345	182	3	algorithms	algorithm	NOUN
aiti-345	182	4	(	(	PUNCT
aiti-345	182	5	both	both	CCONJ
aiti-345	182	6	pso	pso	NOUN
aiti-345	182	7	and	and	CCONJ
aiti-345	182	8	ga	ga	PROPN
aiti-345	182	9	)	)	PUNCT
aiti-345	182	10	are	be	AUX
aiti-345	182	11	run	run	VERB
aiti-345	182	12	for	for	ADP
aiti-345	182	13	the	the	DET
aiti-345	182	14	same	same	ADJ
aiti-345	182	15	number	number	NOUN
aiti-345	182	16	of	of	ADP
aiti-345	182	17	fitness	fitness	NOUN
aiti-345	182	18	function	function	NOUN
aiti-345	182	19	evaluations	evaluation	NOUN
aiti-345	182	20	.	.	PUNCT
aiti-345	183	1	table	table	NOUN
aiti-345	183	2	3	3	NUM
aiti-345	183	3	summarized	summarize	VERB
aiti-345	183	4	the	the	DET
aiti-345	183	5	parameters	parameter	NOUN
aiti-345	183	6	used	use	VERB
aiti-345	183	7	for	for	ADP
aiti-345	183	8	pso	pso	NOUN
aiti-345	183	9	and	and	CCONJ
aiti-345	183	10	ga	ga	PROPN
aiti-345	183	11	technique	technique	NOUN
aiti-345	183	12	.	.	PUNCT
aiti-345	184	1	the	the	DET
aiti-345	184	2	empirical	empirical	ADJ
aiti-345	184	3	results	result	NOUN
aiti-345	184	4	are	be	AUX
aiti-345	184	5	reported	report	VERB
aiti-345	184	6	in	in	ADP
aiti-345	184	7	section	section	NOUN
aiti-345	184	8	4.2	4.2	NUM
aiti-345	184	9	.	.	PUNCT
aiti-345	185	1	4.2	4.2	NUM
aiti-345	185	2	.	.	PUNCT
aiti-345	185	3	result	result	VERB
aiti-345	185	4	and	and	CCONJ
aiti-345	185	5	comparison	comparison	NOUN
aiti-345	185	6	the	the	DET
aiti-345	185	7	results	result	NOUN
aiti-345	185	8	obtained	obtain	VERB
aiti-345	185	9	by	by	ADP
aiti-345	185	10	the	the	DET
aiti-345	185	11	developed	develop	VERB
aiti-345	185	12	hpso	hpso	NOUN
aiti-345	185	13	-	-	PUNCT
aiti-345	185	14	aci	aci	NOUN
aiti-345	185	15	-	-	PUNCT
aiti-345	185	16	svm	svm	ADJ
aiti-345	185	17	approach	approach	NOUN
aiti-345	185	18	are	be	AUX
aiti-345	185	19	compared	compare	VERB
aiti-345	185	20	with	with	ADP
aiti-345	185	21	those	those	PRON
aiti-345	185	22	of	of	ADP
aiti-345	185	23	ga	ga	NOUN
aiti-345	185	24	-	-	PUNCT
aiti-345	185	25	svm	svm	PROPN
aiti-345	185	26	proposed	propose	VERB
aiti-345	185	27	by	by	ADP
aiti-345	185	28	huang	huang	PROPN
aiti-345	185	29	et	et	PROPN
aiti-345	185	30	al	al	PROPN
aiti-345	185	31	.	.	PUNCT
aiti-345	186	1	[	[	X
aiti-345	186	2	9	9	NUM
aiti-345	186	3	]	]	PUNCT
aiti-345	186	4	without	without	ADP
aiti-345	186	5	aci	aci	PROPN
aiti-345	186	6	scheme	scheme	PROPN
aiti-345	186	7	.	.	PUNCT
aiti-345	187	1	taking	take	VERB
aiti-345	187	2	the	the	DET
aiti-345	187	3	heart	heart	NOUN
aiti-345	187	4	disease	disease	NOUN
aiti-345	187	5	dataset	dataset	VERB
aiti-345	187	6	,	,	PUNCT
aiti-345	187	7	for	for	ADP
aiti-345	187	8	example	example	NOUN
aiti-345	187	9	,	,	PUNCT
aiti-345	187	10	the	the	DET
aiti-345	187	11	classification	classification	NOUN
aiti-345	187	12	accuracy	accuracy	NOUN
aiti-345	187	13	cca	cca	NOUN
aiti-345	187	14	,	,	PUNCT
aiti-345	187	15	number	number	NOUN
aiti-345	187	16	of	of	ADP
aiti-345	187	17	selected	select	VERB
aiti-345	187	18	features	feature	NOUN
aiti-345	187	19	fn	fn	ADV
aiti-345	187	20	,	,	PUNCT
aiti-345	187	21	and	and	CCONJ
aiti-345	187	22	the	the	DET
aiti-345	187	23	best	good	ADJ
aiti-345	187	24	parameters	parameter	NOUN
aiti-345	187	25	cond	cond	PROPN
aiti-345	187	26	,	,	PUNCT
aiti-345	187	27	c	c	AUX
aiti-345	187	28	,	,	PUNCT
aiti-345	187	29			NUM
aiti-345	187	30	for	for	ADP
aiti-345	187	31	each	each	DET
aiti-345	187	32	fold	fold	NOUN
aiti-345	187	33	are	be	AUX
aiti-345	187	34	shown	show	VERB
aiti-345	187	35	in	in	ADP
aiti-345	187	36	table	table	NOUN
aiti-345	187	37	4	4	NUM
aiti-345	187	38	.	.	PUNCT
aiti-345	188	1	for	for	ADP
aiti-345	188	2	the	the	DET
aiti-345	188	3	hpso	hpso	NOUN
aiti-345	188	4	-	-	PUNCT
aiti-345	188	5	aci	aci	NOUN
aiti-345	188	6	-	-	PUNCT
aiti-345	188	7	svm	svm	PROPN
aiti-345	188	8	method	method	NOUN
aiti-345	188	9	,	,	PUNCT
aiti-345	188	10	average	average	ADJ
aiti-345	188	11	classification	classification	NOUN
aiti-345	188	12	accuracy	accuracy	NOUN
aiti-345	188	13	rate	rate	NOUN
aiti-345	188	14	is	be	AUX
aiti-345	188	15	96.87	96.87	NUM
aiti-345	188	16	%	%	NOUN
aiti-345	188	17	,	,	PUNCT
aiti-345	188	18	and	and	CCONJ
aiti-345	188	19	average	average	ADJ
aiti-345	188	20	number	number	NOUN
aiti-345	188	21	of	of	ADP
aiti-345	188	22	features	feature	NOUN
aiti-345	188	23	is	be	AUX
aiti-345	188	24	4.9	4.9	NUM
aiti-345	188	25	.	.	PUNCT
aiti-345	189	1	for	for	ADP
aiti-345	189	2	the	the	DET
aiti-345	189	3	ga	ga	NOUN
aiti-345	189	4	-	-	PUNCT
aiti-345	189	5	svm	svm	PROPN
aiti-345	189	6	without	without	ADP
aiti-345	189	7	aci	aci	PROPN
aiti-345	189	8	approach	approach	NOUN
aiti-345	189	9	,	,	PUNCT
aiti-345	189	10	its	its	PRON
aiti-345	189	11	average	average	ADJ
aiti-345	189	12	classification	classification	NOUN
aiti-345	189	13	accuracy	accuracy	NOUN
aiti-345	189	14	rate	rate	NOUN
aiti-345	189	15	is	be	AUX
aiti-345	189	16	only	only	ADV
aiti-345	189	17	94.81	94.81	NUM
aiti-345	189	18	%	%	NOUN
aiti-345	189	19	,	,	PUNCT
aiti-345	189	20	and	and	CCONJ
aiti-345	189	21	average	average	ADJ
aiti-345	189	22	number	number	NOUN
aiti-345	189	23	of	of	ADP
aiti-345	189	24	features	feature	NOUN
aiti-345	189	25	is	be	AUX
aiti-345	189	26	5.6	5.6	NUM
aiti-345	189	27	.	.	PUNCT
aiti-345	189	28	table	table	NOUN
aiti-345	189	29	4	4	NUM
aiti-345	189	30	comparison	comparison	NOUN
aiti-345	189	31	for	for	ADP
aiti-345	189	32	hpso	hpso	NOUN
aiti-345	189	33	-	-	PUNCT
aiti-345	189	34	aci	aci	NOUN
aiti-345	189	35	-	-	PUNCT
aiti-345	189	36	svm	svm	PROPN
aiti-345	189	37	and	and	CCONJ
aiti-345	189	38	ga	ga	NOUN
aiti-345	189	39	-	-	PUNCT
aiti-345	189	40	svm	svm	PROPN
aiti-345	189	41	table	table	NOUN
aiti-345	189	42	5	5	NUM
aiti-345	189	43	comparison	comparison	NOUN
aiti-345	189	44	for	for	ADP
aiti-345	189	45	hpso	hpso	NOUN
aiti-345	189	46	-	-	PUNCT
aiti-345	189	47	aci	aci	NOUN
aiti-345	189	48	-	-	PUNCT
aiti-345	189	49	svm	svm	PROPN
aiti-345	189	50	,	,	PUNCT
aiti-345	189	51	pso	pso	NOUN
aiti-345	189	52	-	-	PUNCT
aiti-345	189	53	svm	svm	PROPN
aiti-345	189	54	and	and	CCONJ
aiti-345	189	55	ga	ga	NOUN
aiti-345	189	56	-	-	PUNCT
aiti-345	189	57	svm	svm	PROPN
aiti-345	189	58	advances	advance	NOUN
aiti-345	189	59	in	in	ADP
aiti-345	189	60	technology	technology	NOUN
aiti-345	189	61	innovation	innovation	NOUN
aiti-345	189	62	,	,	PUNCT
aiti-345	189	63	vol	vol	NOUN
aiti-345	189	64	.	.	PROPN
aiti-345	189	65	1	1	NUM
aiti-345	189	66	,	,	PUNCT
aiti-345	189	67	no	no	INTJ
aiti-345	189	68	.	.	NOUN
aiti-345	189	69	2	2	NUM
aiti-345	189	70	,	,	PUNCT
aiti-345	189	71	2016	2016	NUM
aiti-345	189	72	,	,	PUNCT
aiti-345	189	73	pp	pp	ADJ
aiti-345	189	74	.	.	PUNCT
aiti-345	190	1	53	53	NUM
aiti-345	190	2	57	57	NUM
aiti-345	190	3	57	57	NUM
aiti-345	190	4	copyright	copyright	NOUN
aiti-345	190	5	©	©	PROPN
aiti-345	190	6	taeti	taeti	PROPN
aiti-345	190	7	table	table	NOUN
aiti-345	190	8	5	5	NUM
aiti-345	190	9	shows	show	VERB
aiti-345	190	10	the	the	DET
aiti-345	190	11	summary	summary	NOUN
aiti-345	190	12	results	result	VERB
aiti-345	190	13	for	for	ADP
aiti-345	190	14	the	the	DET
aiti-345	190	15	average	average	ADJ
aiti-345	190	16	class	class	NOUN
aiti-345	190	17	ification	ification	NOUN
aiti-345	190	18	accuracy	accuracy	NOUN
aiti-345	190	19	rate	rate	NOUN
aiti-345	190	20	of	of	ADP
aiti-345	190	21	the	the	DET
aiti-345	190	22	hpso	hpso	NOUN
aiti-345	190	23	-	-	PUNCT
aiti-345	190	24	aci	aci	NOUN
aiti-345	190	25	-	-	PUNCT
aiti-345	190	26	svm	svm	ADJ
aiti-345	190	27	hybrid	hybrid	NOUN
aiti-345	190	28	framework	framework	NOUN
aiti-345	190	29	and	and	CCONJ
aiti-345	190	30	pso	pso	NOUN
aiti-345	190	31	-	-	PUNCT
aiti-345	190	32	svm	svm	PROPN
aiti-345	190	33	,	,	PUNCT
aiti-345	190	34	ga	ga	NOUN
aiti-345	190	35	-	-	PUNCT
aiti-345	190	36	svm	svm	PROPN
aiti-345	190	37	without	without	ADP
aiti-345	190	38	aci	aci	PROPN
aiti-345	190	39	method	method	NOUN
aiti-345	190	40	on	on	ADP
aiti-345	190	41	six	six	NUM
aiti-345	190	42	uci	uci	PROPN
aiti-345	190	43	datasets	dataset	NOUN
aiti-345	190	44	.	.	PUNCT
aiti-345	191	1	in	in	ADP
aiti-345	191	2	table	table	NOUN
aiti-345	191	3	5	5	NUM
aiti-345	191	4	,	,	PUNCT
aiti-345	191	5	the	the	DET
aiti-345	191	6	classification	classification	NOUN
aiti-345	191	7	accuracy	accuracy	NOUN
aiti-345	191	8	rate	rate	NOUN
aiti-345	191	9	is	be	AUX
aiti-345	191	10	represented	represent	VERB
aiti-345	191	11	as	as	ADP
aiti-345	191	12	the	the	DET
aiti-345	191	13	form	form	NOUN
aiti-345	191	14	of	of	ADP
aiti-345	191	15	‘	'	PUNCT
aiti-345	191	16	average	average	PROPN
aiti-345	191	17	standard	standard	ADJ
aiti-345	191	18	deviation	deviation	NOUN
aiti-345	191	19	’	'	PUNCT
aiti-345	191	20	.	.	PUNCT
aiti-345	192	1	to	to	PART
aiti-345	192	2	highlight	highlight	VERB
aiti-345	192	3	the	the	DET
aiti-345	192	4	advantage	advantage	NOUN
aiti-345	192	5	,	,	PUNCT
aiti-345	192	6	we	we	PRON
aiti-345	192	7	used	use	VERB
aiti-345	192	8	the	the	DET
aiti-345	192	9	non	non	ADJ
aiti-345	192	10	-	-	ADJ
aiti-345	192	11	parametric	parametric	ADJ
aiti-345	192	12	wilcoxon	wilcoxon	PROPN
aiti-345	192	13	signed	sign	VERB
aiti-345	192	14	rank	rank	NOUN
aiti-345	192	15	test	test	NOUN
aiti-345	192	16	for	for	ADP
aiti-345	192	17	all	all	PRON
aiti-345	192	18	of	of	ADP
aiti-345	192	19	the	the	DET
aiti-345	192	20	datasets	dataset	NOUN
aiti-345	192	21	.	.	PUNCT
aiti-345	193	1	in	in	ADP
aiti-345	193	2	table	table	NOUN
aiti-345	193	3	5	5	NUM
aiti-345	193	4	,	,	PUNCT
aiti-345	193	5	the	the	DET
aiti-345	193	6	p	p	NOUN
aiti-345	193	7	-	-	PUNCT
aiti-345	193	8	values	value	NOUN
aiti-345	193	9	of	of	ADP
aiti-345	193	10	hpso	hpso	NOUN
aiti-345	193	11	-	-	PUNCT
aiti-345	193	12	aci	aci	NOUN
aiti-345	193	13	-	-	PUNCT
aiti-345	193	14	svm	svm	PROPN
aiti-345	193	15	versus	versus	ADP
aiti-345	193	16	pso	pso	NOUN
aiti-345	193	17	-	-	PUNCT
aiti-345	193	18	svm	svm	PROPN
aiti-345	193	19	and	and	CCONJ
aiti-345	193	20	ga	ga	PROPN
aiti-345	193	21	-	-	PUNCT
aiti-345	193	22	svm	svm	PROPN
aiti-345	193	23	are	be	AUX
aiti-345	193	24	smaller	small	ADJ
aiti-345	193	25	than	than	ADP
aiti-345	193	26	the	the	DET
aiti-345	193	27	statistical	statistical	ADJ
aiti-345	193	28	significance	significance	NOUN
aiti-345	193	29	level	level	NOUN
aiti-345	193	30	of	of	ADP
aiti-345	193	31	0.05	0.05	NUM
aiti-345	193	32	except	except	SCONJ
aiti-345	193	33	iris	iris	NOUN
aiti-345	193	34	dataset	dataset	VERB
aiti-345	193	35	.	.	PUNCT
aiti-345	194	1	that	that	PRON
aiti-345	194	2	is	be	AUX
aiti-345	194	3	to	to	PART
aiti-345	194	4	say	say	VERB
aiti-345	194	5	,	,	PUNCT
aiti-345	194	6	the	the	DET
aiti-345	194	7	developed	develop	VERB
aiti-345	194	8	hpso	hpso	NOUN
aiti-345	194	9	-	-	PUNCT
aiti-345	194	10	aci	aci	NOUN
aiti-345	194	11	-	-	PUNCT
aiti-345	194	12	svm	svm	PROPN
aiti-345	194	13	yields	yield	NOUN
aiti-345	194	14	higher	high	ADJ
aiti-345	194	15	classification	classification	NOUN
aiti-345	194	16	accuracy	accuracy	NOUN
aiti-345	194	17	rate	rate	NOUN
aiti-345	194	18	across	across	ADP
aiti-345	194	19	different	different	ADJ
aiti-345	194	20	datasets	dataset	NOUN
aiti-345	194	21	to	to	PART
aiti-345	194	22	enhance	enhance	VERB
aiti-345	194	23	the	the	DET
aiti-345	194	24	performance	performance	NOUN
aiti-345	194	25	for	for	ADP
aiti-345	194	26	the	the	DET
aiti-345	194	27	svm	svm	PROPN
aiti-345	194	28	.	.	PROPN
aiti-345	194	29	further	far	ADV
aiti-345	194	30	,	,	PUNCT
aiti-345	194	31	under	under	ADP
aiti-345	194	32	our	our	PRON
aiti-345	194	33	proposed	propose	VERB
aiti-345	194	34	hybrid	hybrid	NOUN
aiti-345	194	35	framework	framework	NOUN
aiti-345	194	36	with	with	ADP
aiti-345	194	37	aci	aci	PROPN
aiti-345	194	38	mechanism	mechanism	NOUN
aiti-345	194	39	,	,	PUNCT
aiti-345	194	40	the	the	DET
aiti-345	194	41	performance	performance	NOUN
aiti-345	194	42	of	of	ADP
aiti-345	194	43	both	both	CCONJ
aiti-345	194	44	pso	pso	NOUN
aiti-345	194	45	and	and	CCONJ
aiti-345	194	46	ga	ga	NOUN
aiti-345	194	47	optimization	optimization	NOUN
aiti-345	194	48	techniques	technique	NOUN
aiti-345	194	49	in	in	ADP
aiti-345	194	50	terms	term	NOUN
aiti-345	194	51	of	of	ADP
aiti-345	194	52	the	the	DET
aiti-345	194	53	average	average	ADJ
aiti-345	194	54	classification	classification	NOUN
aiti-345	194	55	accuracy	accuracy	NOUN
aiti-345	194	56	rate	rate	NOUN
aiti-345	194	57	_	_	PUNCT
aiti-345	194	58	ccavg	ccavg	NOUN
aiti-345	194	59	a	a	PRON
aiti-345	194	60	and	and	CCONJ
aiti-345	194	61	the	the	DET
aiti-345	194	62	average	average	ADJ
aiti-345	194	63	number	number	NOUN
aiti-345	194	64	of	of	ADP
aiti-345	194	65	selected	select	VERB
aiti-345	194	66	features	feature	NOUN
aiti-345	194	67	_	_	PUNCT
aiti-345	194	68	favg	favg	NOUN
aiti-345	194	69	n	n	VERB
aiti-345	194	70	is	be	AUX
aiti-345	194	71	compared	compare	VERB
aiti-345	194	72	.	.	PUNCT
aiti-345	195	1	in	in	ADP
aiti-345	195	2	table	table	NOUN
aiti-345	195	3	6	6	NUM
aiti-345	195	4	,	,	PUNCT
aiti-345	195	5	the	the	DET
aiti-345	195	6	hpso	hpso	NOUN
aiti-345	195	7	exhibits	exhibit	VERB
aiti-345	195	8	slightly	slightly	ADV
aiti-345	195	9	higher	high	ADJ
aiti-345	195	10	classification	classification	NOUN
aiti-345	195	11	accuracy	accuracy	NOUN
aiti-345	195	12	and	and	CCONJ
aiti-345	195	13	fewer	few	ADJ
aiti-345	195	14	selected	select	VERB
aiti-345	195	15	features	feature	NOUN
aiti-345	195	16	than	than	ADP
aiti-345	195	17	ga	ga	PROPN
aiti-345	195	18	.	.	PUNCT
aiti-345	196	1	it	it	PRON
aiti-345	196	2	is	be	AUX
aiti-345	196	3	observed	observe	VERB
aiti-345	196	4	that	that	SCONJ
aiti-345	196	5	,	,	PUNCT
aiti-345	196	6	from	from	ADP
aiti-345	196	7	an	an	DET
aiti-345	196	8	evolutionary	evolutionary	ADJ
aiti-345	196	9	point	point	NOUN
aiti-345	196	10	of	of	ADP
aiti-345	196	11	view	view	NOUN
aiti-345	196	12	,	,	PUNCT
aiti-345	196	13	the	the	DET
aiti-345	196	14	performance	performance	NOUN
aiti-345	196	15	of	of	ADP
aiti-345	196	16	the	the	DET
aiti-345	196	17	hpso	hpso	NOUN
aiti-345	196	18	is	be	AUX
aiti-345	196	19	better	well	ADJ
aiti-345	196	20	than	than	ADP
aiti-345	196	21	ga	ga	PROPN
aiti-345	196	22	.	.	PUNCT
aiti-345	197	1	however	however	ADV
aiti-345	197	2	,	,	PUNCT
aiti-345	197	3	the	the	DET
aiti-345	197	4	results	result	NOUN
aiti-345	197	5	indicate	indicate	VERB
aiti-345	197	6	that	that	SCONJ
aiti-345	197	7	both	both	CCONJ
aiti-345	197	8	pso	pso	NOUN
aiti-345	197	9	and	and	CCONJ
aiti-345	197	10	ga	ga	PROPN
aiti-345	197	11	algorithms	algorithm	NOUN
aiti-345	197	12	can	can	AUX
aiti-345	197	13	be	be	AUX
aiti-345	197	14	used	use	VERB
aiti-345	197	15	in	in	ADP
aiti-345	197	16	optimizing	optimize	VERB
aiti-345	197	17	the	the	DET
aiti-345	197	18	parameters	parameter	NOUN
aiti-345	197	19	under	under	ADP
aiti-345	197	20	our	our	PRON
aiti-345	197	21	developed	develop	VERB
aiti-345	197	22	hybrid	hybrid	ADJ
aiti-345	197	23	framework	framework	NOUN
aiti-345	197	24	to	to	PART
aiti-345	197	25	effectively	effectively	ADV
aiti-345	197	26	improve	improve	VERB
aiti-345	197	27	the	the	DET
aiti-345	197	28	classification	classification	NOUN
aiti-345	197	29	accuracy	accuracy	NOUN
aiti-345	197	30	for	for	ADP
aiti-345	197	31	svm	svm	ADJ
aiti-345	197	32	classifier	classifier	NOUN
aiti-345	197	33	design	design	PROPN
aiti-345	197	34	.	.	PUNCT
aiti-345	198	1	table	table	NOUN
aiti-345	198	2	6	6	NUM
aiti-345	198	3	comparison	comparison	NOUN
aiti-345	198	4	for	for	ADP
aiti-345	198	5	hpso	hpso	NOUN
aiti-345	198	6	-	-	PUNCT
aiti-345	198	7	aci	aci	NOUN
aiti-345	198	8	-	-	PUNCT
aiti-345	198	9	svm	svm	PROPN
aiti-345	198	10	and	and	CCONJ
aiti-345	198	11	ga	ga	PROPN
aiti-345	198	12	-	-	PUNCT
aiti-345	198	13	aci	aci	PROPN
aiti-345	198	14	-	-	PUNCT
aiti-345	198	15	svm	svm	PROPN
aiti-345	198	16	5	5	NUM
aiti-345	198	17	.	.	PUNCT
aiti-345	198	18	conclusion	conclusion	NOUN
aiti-345	198	19	for	for	ADP
aiti-345	198	20	svm	svm	ADJ
aiti-345	198	21	classifier	classifier	NOUN
aiti-345	198	22	,	,	PUNCT
aiti-345	198	23	it	it	PRON
aiti-345	198	24	is	be	AUX
aiti-345	198	25	imperative	imperative	ADJ
aiti-345	198	26	to	to	PART
aiti-345	198	27	perform	perform	VERB
aiti-345	198	28	feature	feature	NOUN
aiti-345	198	29	selection	selection	NOUN
aiti-345	198	30	to	to	PART
aiti-345	198	31	detect	detect	VERB
aiti-345	198	32	which	which	DET
aiti-345	198	33	features	feature	NOUN
aiti-345	198	34	are	be	AUX
aiti-345	198	35	actually	actually	ADV
aiti-345	198	36	relevant	relevant	ADJ
aiti-345	198	37	.	.	PUNCT
aiti-345	199	1	in	in	ADP
aiti-345	199	2	this	this	DET
aiti-345	199	3	study	study	NOUN
aiti-345	199	4	,	,	PUNCT
aiti-345	199	5	the	the	DET
aiti-345	199	6	effectively	effectively	ADV
aiti-345	199	7	adaptive	adaptive	ADJ
aiti-345	199	8	condensed	condense	VERB
aiti-345	199	9	instances	instance	NOUN
aiti-345	199	10	(	(	PUNCT
aiti-345	199	11	aci	aci	NOUN
aiti-345	199	12	)	)	PUNCT
aiti-345	199	13	strategy	strategy	NOUN
aiti-345	199	14	is	be	AUX
aiti-345	199	15	applied	apply	VERB
aiti-345	199	16	to	to	PART
aiti-345	199	17	decide	decide	VERB
aiti-345	199	18	which	which	DET
aiti-345	199	19	instances	instance	NOUN
aiti-345	199	20	of	of	ADP
aiti-345	199	21	the	the	DET
aiti-345	199	22	training	training	NOUN
aiti-345	199	23	data	datum	NOUN
aiti-345	199	24	set	set	VERB
aiti-345	199	25	are	be	AUX
aiti-345	199	26	support	support	NOUN
aiti-345	199	27	vectors	vector	NOUN
aiti-345	199	28	.	.	PUNCT
aiti-345	200	1	furthermore	furthermore	ADV
aiti-345	200	2	,	,	PUNCT
aiti-345	200	3	we	we	PRON
aiti-345	200	4	propose	propose	VERB
aiti-345	200	5	the	the	DET
aiti-345	200	6	aci	aci	PROPN
aiti-345	200	7	scheme	scheme	NOUN
aiti-345	200	8	based	base	VERB
aiti-345	200	9	on	on	ADP
aiti-345	200	10	the	the	DET
aiti-345	200	11	hybrid	hybrid	ADJ
aiti-345	200	12	particle	particle	NOUN
aiti-345	200	13	swarm	swarm	NOUN
aiti-345	200	14	optimization	optimization	NOUN
aiti-345	200	15	(	(	PUNCT
aiti-345	200	16	hpso	hpso	NOUN
aiti-345	200	17	)	)	PUNCT
aiti-345	200	18	algorithm	algorithm	NOUN
aiti-345	200	19	to	to	PART
aiti-345	200	20	simultaneously	simultaneously	ADV
aiti-345	200	21	optimize	optimize	VERB
aiti-345	200	22	the	the	DET
aiti-345	200	23	condensed	condense	VERB
aiti-345	200	24	instances	instance	NOUN
aiti-345	200	25	and	and	CCONJ
aiti-345	200	26	svm	svm	ADJ
aiti-345	200	27	kernel	kernel	PROPN
aiti-345	200	28	parameters	parameter	NOUN
aiti-345	200	29	for	for	ADP
aiti-345	200	30	the	the	DET
aiti-345	200	31	classification	classification	NOUN
aiti-345	200	32	accuracy	accuracy	NOUN
aiti-345	200	33	enhancement	enhancement	NOUN
aiti-345	200	34	.	.	PUNCT
aiti-345	201	1	several	several	ADJ
aiti-345	201	2	uci	uci	PROPN
aiti-345	201	3	benchmark	benchmark	NOUN
aiti-345	201	4	datasets	dataset	NOUN
aiti-345	201	5	are	be	AUX
aiti-345	201	6	conducted	conduct	VERB
aiti-345	201	7	to	to	PART
aiti-345	201	8	validate	validate	VERB
aiti-345	201	9	the	the	DET
aiti-345	201	10	effectiveness	effectiveness	NOUN
aiti-345	201	11	of	of	ADP
aiti-345	201	12	the	the	DET
aiti-345	201	13	proposed	propose	VERB
aiti-345	201	14	method	method	NOUN
aiti-345	201	15	,	,	PUNCT
aiti-345	201	16	and	and	CCONJ
aiti-345	201	17	the	the	DET
aiti-345	201	18	experiment	experiment	NOUN
aiti-345	201	19	results	result	NOUN
aiti-345	201	20	show	show	VERB
aiti-345	201	21	that	that	SCONJ
aiti-345	201	22	the	the	DET
aiti-345	201	23	proposed	propose	VERB
aiti-345	201	24	hybrid	hybrid	NOUN
aiti-345	201	25	framework	framework	NOUN
aiti-345	201	26	can	can	AUX
aiti-345	201	27	achieve	achieve	VERB
aiti-345	201	28	better	well	ADJ
aiti-345	201	29	performance	performance	NOUN
aiti-345	201	30	than	than	ADP
aiti-345	201	31	other	other	ADJ
aiti-345	201	32	existing	exist	VERB
aiti-345	201	33	methods	method	NOUN
aiti-345	201	34	in	in	ADP
aiti-345	201	35	literature	literature	NOUN
aiti-345	201	36	.	.	PUNCT
aiti-345	202	1	investigating	investigate	VERB
aiti-345	202	2	more	more	ADV
aiti-345	202	3	large	large	ADJ
aiti-345	202	4	scale	scale	NOUN
aiti-345	202	5	dataset	dataset	NOUN
aiti-345	202	6	as	as	ADV
aiti-345	202	7	well	well	ADV
aiti-345	202	8	as	as	ADP
aiti-345	202	9	combining	combine	VERB
aiti-345	202	10	other	other	ADJ
aiti-345	202	11	heuristic	heuristic	ADJ
aiti-345	202	12	algorithm	algorithm	NOUN
aiti-345	202	13	for	for	ADP
aiti-345	202	14	hybrid	hybrid	ADJ
aiti-345	202	15	system	system	NOUN
aiti-345	202	16	may	may	AUX
aiti-345	202	17	be	be	AUX
aiti-345	202	18	interesting	interesting	ADJ
aiti-345	202	19	future	future	ADJ
aiti-345	202	20	work	work	NOUN
aiti-345	202	21	.	.	PUNCT
aiti-345	203	1	references	reference	NOUN
aiti-345	203	2	[	[	X
aiti-345	203	3	1	1	X
aiti-345	203	4	]	]	PUNCT
aiti-345	203	5	v.	v.	ADP
aiti-345	203	6	n.	n.	PROPN
aiti-345	203	7	vapnik	vapnik	NOUN
aiti-345	203	8	,	,	PUNCT
aiti-345	203	9	"	"	PUNCT
aiti-345	203	10	an	an	DET
aiti-345	203	11	overview	overview	NOUN
aiti-345	203	12	of	of	ADP
aiti-345	203	13	statistical	statistical	ADJ
aiti-345	203	14	learning	learning	NOUN
aiti-345	203	15	theory	theory	NOUN
aiti-345	203	16	,	,	PUNCT
aiti-345	203	17	"	"	PUNCT
aiti-345	203	18	ieee	ieee	NOUN
aiti-345	203	19	transactions	transaction	NOUN
aiti-345	203	20	on	on	ADP
aiti-345	203	21	neural	neural	ADJ
aiti-345	203	22	networks	network	NOUN
aiti-345	203	23	,	,	PUNCT
aiti-345	203	24	vol	vol	NOUN
aiti-345	203	25	.	.	PROPN
aiti-345	203	26	10	10	NUM
aiti-345	203	27	,	,	PUNCT
aiti-345	203	28	pp	pp	ADJ
aiti-345	203	29	.	.	PUNCT
aiti-345	204	1	988	988	NUM
aiti-345	204	2	-	-	SYM
aiti-345	204	3	999	999	NUM
aiti-345	204	4	,	,	PUNCT
aiti-345	204	5	1999	1999	NUM
aiti-345	204	6	.	.	PUNCT
aiti-345	205	1	[	[	X
aiti-345	205	2	2	2	NUM
aiti-345	205	3	]	]	PUNCT
aiti-345	205	4	i.	i.	PROPN
aiti-345	205	5	j.	j.	PROPN
aiti-345	205	6	ding	ding	PROPN
aiti-345	205	7	,	,	PUNCT
aiti-345	205	8	c.	c.	PROPN
aiti-345	205	9	t.	t.	NOUN
aiti-345	205	10	yen	yen	PROPN
aiti-345	205	11	,	,	PUNCT
aiti-345	205	12	and	and	CCONJ
aiti-345	205	13	d.	d.	PROPN
aiti-345	205	14	c.	c.	PROPN
aiti-345	205	15	ou	ou	PROPN
aiti-345	205	16	"	"	PUNCT
aiti-345	205	17	a	a	DET
aiti-345	205	18	method	method	NOUN
aiti-345	205	19	to	to	PART
aiti-345	205	20	integrate	integrate	VERB
aiti-345	205	21	gmm	gmm	NOUN
aiti-345	205	22	,	,	PUNCT
aiti-345	205	23	svm	svm	VERB
aiti-345	205	24	and	and	CCONJ
aiti-345	205	25	dtw	dtw	PROPN
aiti-345	205	26	for	for	ADP
aiti-345	205	27	speaker	speaker	NOUN
aiti-345	205	28	recognition	recognition	NOUN
aiti-345	205	29	,	,	PUNCT
aiti-345	205	30	"	"	PUNCT
aiti-345	205	31	international	international	ADJ
aiti-345	205	32	journal	journal	NOUN
aiti-345	205	33	of	of	ADP
aiti-345	205	34	engineering	engineering	NOUN
aiti-345	205	35	and	and	CCONJ
aiti-345	205	36	technology	technology	NOUN
aiti-345	205	37	innovation	innovation	NOUN
aiti-345	205	38	,	,	PUNCT
aiti-345	205	39	vol	vol	NOUN
aiti-345	205	40	.	.	PROPN
aiti-345	205	41	4	4	NUM
aiti-345	205	42	,	,	PUNCT
aiti-345	205	43	pp	pp	ADJ
aiti-345	205	44	.	.	PUNCT
aiti-345	206	1	38	38	NUM
aiti-345	206	2	-	-	SYM
aiti-345	206	3	47	47	NUM
aiti-345	206	4	,	,	PUNCT
aiti-345	206	5	2014	2014	NUM
aiti-345	206	6	.	.	PUNCT
aiti-345	207	1	[	[	X
aiti-345	207	2	3	3	X
aiti-345	207	3	]	]	X
aiti-345	207	4	r.	r.	PROPN
aiti-345	207	5	kothandan	kothandan	PROPN
aiti-345	207	6	and	and	CCONJ
aiti-345	207	7	s.	s.	PROPN
aiti-345	207	8	biswas	biswas	PROPN
aiti-345	207	9	,	,	PUNCT
aiti-345	207	10	"	"	PUNCT
aiti-345	207	11	identifying	identify	VERB
aiti-345	207	12	micrornas	microrna	NOUN
aiti-345	207	13	involved	involve	VERB
aiti-345	207	14	in	in	ADP
aiti-345	207	15	cancer	cancer	NOUN
aiti-345	207	16	pathway	pathway	NOUN
aiti-345	207	17	using	use	VERB
aiti-345	207	18	support	support	NOUN
aiti-345	207	19	vector	vector	NOUN
aiti-345	207	20	machines	machine	NOUN
aiti-345	207	21	,	,	PUNCT
aiti-345	207	22	"	"	PUNCT
aiti-345	207	23	computational	computational	ADJ
aiti-345	207	24	biology	biology	NOUN
aiti-345	207	25	and	and	CCONJ
aiti-345	207	26	chemistry	chemistry	NOUN
aiti-345	207	27	,	,	PUNCT
aiti-345	207	28	vol	vol	NOUN
aiti-345	207	29	.	.	PROPN
aiti-345	207	30	55	55	NUM
aiti-345	207	31	,	,	PUNCT
aiti-345	207	32	pp	pp	ADJ
aiti-345	207	33	.	.	PUNCT
aiti-345	208	1	31	31	NUM
aiti-345	208	2	-	-	SYM
aiti-345	208	3	36	36	NUM
aiti-345	208	4	,	,	PUNCT
aiti-345	208	5	apr	apr	NOUN
aiti-345	208	6	.	.	PROPN
aiti-345	208	7	2015	2015	NUM
aiti-345	208	8	.	.	PUNCT
aiti-345	209	1	[	[	X
aiti-345	209	2	4	4	X
aiti-345	209	3	]	]	X
aiti-345	209	4	b.	b.	PROPN
aiti-345	209	5	ramesh	ramesh	PROPN
aiti-345	209	6	and	and	CCONJ
aiti-345	209	7	j.	j.	PROPN
aiti-345	209	8	g.	g.	PROPN
aiti-345	209	9	r.	r.	PROPN
aiti-345	209	10	sathiaseelan	sathiaseelan	PROPN
aiti-345	209	11	,	,	PUNCT
aiti-345	209	12	"	"	PUNCT
aiti-345	209	13	an	an	DET
aiti-345	209	14	advanced	advanced	ADJ
aiti-345	209	15	multi	multi	ADJ
aiti-345	209	16	class	class	NOUN
aiti-345	209	17	instance	instance	NOUN
aiti-345	209	18	selection	selection	NOUN
aiti-345	209	19	based	base	VERB
aiti-345	209	20	support	support	NOUN
aiti-345	209	21	vector	vector	NOUN
aiti-345	209	22	machine	machine	NOUN
aiti-345	209	23	for	for	ADP
aiti-345	209	24	text	text	NOUN
aiti-345	209	25	classification	classification	NOUN
aiti-345	209	26	,	,	PUNCT
aiti-345	209	27	"	"	PUNCT
aiti-345	209	28	procedia	procedia	NOUN
aiti-345	209	29	computer	computer	NOUN
aiti-345	209	30	science	science	NOUN
aiti-345	209	31	,	,	PUNCT
aiti-345	209	32	vol	vol	NOUN
aiti-345	209	33	.	.	PROPN
aiti-345	210	1	57	57	NUM
aiti-345	210	2	,	,	PUNCT
aiti-345	210	3	pp	pp	ADJ
aiti-345	210	4	.	.	PUNCT
aiti-345	211	1	1124	1124	NUM
aiti-345	211	2	-	-	SYM
aiti-345	211	3	1130	1130	NUM
aiti-345	211	4	,	,	PUNCT
aiti-345	211	5	2015	2015	NUM
aiti-345	211	6	.	.	PUNCT
aiti-345	212	1	[	[	X
aiti-345	212	2	5	5	NUM
aiti-345	212	3	]	]	PUNCT
aiti-345	212	4	x.	x.	NOUN
aiti-345	212	5	zhang	zhang	PROPN
aiti-345	212	6	,	,	PUNCT
aiti-345	212	7	g.	g.	PROPN
aiti-345	212	8	wu	wu	PROPN
aiti-345	212	9	,	,	PUNCT
aiti-345	212	10	z.	z.	PROPN
aiti-345	212	11	dong	dong	PROPN
aiti-345	212	12	and	and	CCONJ
aiti-345	212	13	c.	c.	PROPN
aiti-345	212	14	crawford	crawford	PROPN
aiti-345	212	15	,	,	PUNCT
aiti-345	212	16	"	"	PUNCT
aiti-345	212	17	embedded	embed	VERB
aiti-345	212	18	feature	feature	NOUN
aiti-345	212	19	-	-	PUNCT
aiti-345	212	20	selection	selection	NOUN
aiti-345	212	21	support	support	NOUN
aiti-345	212	22	vector	vector	NOUN
aiti-345	212	23	machine	machine	NOUN
aiti-345	212	24	for	for	ADP
aiti-345	212	25	driving	drive	VERB
aiti-345	212	26	pattern	pattern	NOUN
aiti-345	212	27	recognition	recognition	NOUN
aiti-345	212	28	,	,	PUNCT
aiti-345	212	29	"	"	PUNCT
aiti-345	212	30	journal	journal	NOUN
aiti-345	212	31	of	of	ADP
aiti-345	212	32	the	the	DET
aiti-345	212	33	franklin	franklin	PROPN
aiti-345	212	34	institute	institute	PROPN
aiti-345	212	35	,	,	PUNCT
aiti-345	212	36	vol	vol	NOUN
aiti-345	212	37	.	.	PROPN
aiti-345	212	38	352	352	NUM
aiti-345	212	39	,	,	PUNCT
aiti-345	212	40	pp	pp	ADJ
aiti-345	212	41	.	.	PUNCT
aiti-345	213	1	669	669	NUM
aiti-345	213	2	-	-	SYM
aiti-345	213	3	685	685	NUM
aiti-345	213	4	,	,	PUNCT
aiti-345	213	5	2015	2015	NUM
aiti-345	213	6	.	.	PUNCT
aiti-345	214	1	[	[	X
aiti-345	214	2	6	6	NUM
aiti-345	214	3	]	]	X
aiti-345	214	4	s.w	s.w	PROPN
aiti-345	214	5	.	.	PROPN
aiti-345	214	6	lin	lin	PROPN
aiti-345	214	7	,	,	PUNCT
aiti-345	214	8	z.	z.	PROPN
aiti-345	214	9	j.	j.	PROPN
aiti-345	214	10	lee	lee	PROPN
aiti-345	214	11	,	,	PUNCT
aiti-345	214	12	s.	s.	PROPN
aiti-345	214	13	c.	c.	PROPN
aiti-345	214	14	chen	chen	PROPN
aiti-345	214	15	and	and	CCONJ
aiti-345	214	16	t.	t.	PROPN
aiti-345	214	17	y.	y.	PROPN
aiti-345	214	18	tseng	tseng	PROPN
aiti-345	214	19	,	,	PUNCT
aiti-345	214	20	"	"	PUNCT
aiti-345	214	21	parameter	parameter	NOUN
aiti-345	214	22	determination	determination	NOUN
aiti-345	214	23	of	of	ADP
aiti-345	214	24	support	support	NOUN
aiti-345	214	25	vector	vector	NOUN
aiti-345	214	26	machine	machine	NOUN
aiti-345	214	27	and	and	CCONJ
aiti-345	214	28	feature	feature	NOUN
aiti-345	214	29	selection	selection	NOUN
aiti-345	214	30	using	use	VERB
aiti-345	214	31	simulated	simulate	VERB
aiti-345	214	32	annealing	annealing	NOUN
aiti-345	214	33	approach	approach	NOUN
aiti-345	214	34	,	,	PUNCT
aiti-345	214	35	"	"	PUNCT
aiti-345	214	36	applied	apply	VERB
aiti-345	214	37	soft	soft	ADJ
aiti-345	214	38	computing	computing	NOUN
aiti-345	214	39	,	,	PUNCT
aiti-345	214	40	vol	vol	NOUN
aiti-345	214	41	.	.	PROPN
aiti-345	214	42	8	8	NUM
aiti-345	214	43	,	,	PUNCT
aiti-345	214	44	pp	pp	ADJ
aiti-345	214	45	.	.	PUNCT
aiti-345	215	1	1505	1505	NUM
aiti-345	215	2	-	-	SYM
aiti-345	215	3	1512	1512	NUM
aiti-345	215	4	,	,	PUNCT
aiti-345	215	5	2008	2008	NUM
aiti-345	215	6	.	.	PUNCT
aiti-345	216	1	[	[	X
aiti-345	216	2	7	7	X
aiti-345	216	3	]	]	X
aiti-345	216	4	c.	c.	PROPN
aiti-345	216	5	l.	l.	PROPN
aiti-345	216	6	lu	lu	PROPN
aiti-345	216	7	,	,	PUNCT
aiti-345	216	8	i.	i.	PROPN
aiti-345	216	9	f.	f.	PROPN
aiti-345	216	10	chung	chung	PROPN
aiti-345	216	11	and	and	CCONJ
aiti-345	216	12	t.	t.	PROPN
aiti-345	216	13	c.	c.	PROPN
aiti-345	216	14	lin	lin	PROPN
aiti-345	216	15	,	,	PUNCT
aiti-345	216	16	"	"	PUNCT
aiti-345	216	17	the	the	DET
aiti-345	216	18	hybrid	hybrid	ADJ
aiti-345	216	19	dynamic	dynamic	ADJ
aiti-345	216	20	prototype	prototype	NOUN
aiti-345	216	21	construction	construction	NOUN
aiti-345	216	22	and	and	CCONJ
aiti-345	216	23	parameter	parameter	NOUN
aiti-345	216	24	optimization	optimization	NOUN
aiti-345	216	25	with	with	ADP
aiti-345	216	26	genetic	genetic	ADJ
aiti-345	216	27	algorithm	algorithm	NOUN
aiti-345	216	28	for	for	ADP
aiti-345	216	29	support	support	NOUN
aiti-345	216	30	vector	vector	NOUN
aiti-345	216	31	machine	machine	NOUN
aiti-345	216	32	,	,	PUNCT
aiti-345	216	33	"	"	PUNCT
aiti-345	216	34	international	international	ADJ
aiti-345	216	35	journal	journal	NOUN
aiti-345	216	36	of	of	ADP
aiti-345	216	37	engineering	engineering	NOUN
aiti-345	216	38	and	and	CCONJ
aiti-345	216	39	technology	technology	NOUN
aiti-345	216	40	innovation	innovation	NOUN
aiti-345	216	41	,	,	PUNCT
aiti-345	216	42	vol	vol	NOUN
aiti-345	216	43	.	.	PROPN
aiti-345	216	44	5	5	NUM
aiti-345	216	45	,	,	PUNCT
aiti-345	216	46	pp	pp	ADJ
aiti-345	216	47	.	.	PUNCT
aiti-345	216	48	220	220	NUM
aiti-345	216	49	-	-	SYM
aiti-345	216	50	232	232	NUM
aiti-345	216	51	,	,	PUNCT
aiti-345	216	52	2015	2015	NUM
aiti-345	216	53	.	.	PUNCT
aiti-345	217	1	[	[	X
aiti-345	217	2	8	8	X
aiti-345	217	3	]	]	X
aiti-345	217	4	b.	b.	PROPN
aiti-345	217	5	sahu	sahu	PROPN
aiti-345	217	6	and	and	CCONJ
aiti-345	217	7	d.	d.	PROPN
aiti-345	217	8	mishra	mishra	PROPN
aiti-345	217	9	,	,	PUNCT
aiti-345	217	10	"	"	PUNCT
aiti-345	217	11	a	a	DET
aiti-345	217	12	novel	novel	ADJ
aiti-345	217	13	feature	feature	NOUN
aiti-345	217	14	selection	selection	NOUN
aiti-345	217	15	algorithm	algorithm	NOUN
aiti-345	217	16	using	use	VERB
aiti-345	217	17	particle	particle	NOUN
aiti-345	217	18	swarm	swarm	NOUN
aiti-345	217	19	optimization	optimization	NOUN
aiti-345	217	20	for	for	ADP
aiti-345	217	21	cancer	cancer	NOUN
aiti-345	217	22	microarray	microarray	NOUN
aiti-345	217	23	data	datum	NOUN
aiti-345	217	24	,	,	PUNCT
aiti-345	217	25	"	"	PUNCT
aiti-345	217	26	procedia	procedia	NOUN
aiti-345	217	27	engineering	engineering	NOUN
aiti-345	217	28	,	,	PUNCT
aiti-345	217	29	vol	vol	NOUN
aiti-345	217	30	.	.	PROPN
aiti-345	217	31	38	38	NUM
aiti-345	217	32	,	,	PUNCT
aiti-345	217	33	pp	pp	ADJ
aiti-345	217	34	.	.	PUNCT
aiti-345	218	1	27	27	NUM
aiti-345	218	2	-	-	SYM
aiti-345	218	3	31	31	NUM
aiti-345	218	4	,	,	PUNCT
aiti-345	218	5	2012	2012	NUM
aiti-345	218	6	.	.	PUNCT
aiti-345	219	1	[	[	X
aiti-345	219	2	9	9	NUM
aiti-345	219	3	]	]	PUNCT
aiti-345	219	4	c.	c.	PROPN
aiti-345	219	5	l.	l.	PROPN
aiti-345	219	6	huang	huang	PROPN
aiti-345	219	7	and	and	CCONJ
aiti-345	219	8	c.	c.	PROPN
aiti-345	219	9	j.	j.	PROPN
aiti-345	219	10	wang	wang	PROPN
aiti-345	219	11	,	,	PUNCT
aiti-345	219	12	"	"	PUNCT
aiti-345	219	13	a	a	DET
aiti-345	219	14	ga	ga	NOUN
aiti-345	219	15	-	-	PUNCT
aiti-345	219	16	based	base	VERB
aiti-345	219	17	feature	feature	NOUN
aiti-345	219	18	selection	selection	NOUN
aiti-345	219	19	and	and	CCONJ
aiti-345	219	20	parameters	parameter	NOUN
aiti-345	219	21	optimization	optimization	NOUN
aiti-345	219	22	for	for	ADP
aiti-345	219	23	support	support	NOUN
aiti-345	219	24	vector	vector	NOUN
aiti-345	219	25	machines	machine	NOUN
aiti-345	219	26	,	,	PUNCT
aiti-345	219	27	"	"	PUNCT
aiti-345	219	28	expert	expert	NOUN
aiti-345	219	29	systems	system	NOUN
aiti-345	219	30	with	with	ADP
aiti-345	219	31	applications	application	NOUN
aiti-345	219	32	,	,	PUNCT
aiti-345	219	33	vol	vol	NOUN
aiti-345	219	34	.	.	PROPN
aiti-345	219	35	31	31	NUM
aiti-345	219	36	,	,	PUNCT
aiti-345	219	37	pp	pp	ADJ
aiti-345	219	38	.	.	PUNCT
aiti-345	219	39	231	231	NUM
aiti-345	219	40	-	-	SYM
aiti-345	219	41	240	240	NUM
aiti-345	219	42	,	,	PUNCT
aiti-345	219	43	2006	2006	NUM
aiti-345	219	44	[	[	X
aiti-345	219	45	10	10	NUM
aiti-345	219	46	]	]	PUNCT
aiti-345	219	47	j.	j.	PROPN
aiti-345	219	48	kennedy	kennedy	PROPN
aiti-345	219	49	and	and	CCONJ
aiti-345	219	50	r.	r.	PROPN
aiti-345	219	51	c.	c.	PROPN
aiti-345	219	52	eberhart	eberhart	PROPN
aiti-345	219	53	,	,	PUNCT
aiti-345	219	54	"	"	PUNCT
aiti-345	219	55	a	a	DET
aiti-345	219	56	discrete	discrete	ADJ
aiti-345	219	57	binary	binary	ADJ
aiti-345	219	58	version	version	NOUN
aiti-345	219	59	of	of	ADP
aiti-345	219	60	the	the	DET
aiti-345	219	61	particle	particle	NOUN
aiti-345	219	62	swarm	swarm	NOUN
aiti-345	219	63	algorithm	algorithm	NOUN
aiti-345	219	64	,	,	PUNCT
aiti-345	219	65	"	"	PUNCT
aiti-345	219	66	in	in	ADP
aiti-345	219	67	proceedings	proceeding	NOUN
aiti-345	219	68	of	of	ADP
aiti-345	219	69	the	the	DET
aiti-345	219	70	world	world	NOUN
aiti-345	219	71	on	on	ADP
aiti-345	219	72	systematics	systematic	NOUN
aiti-345	219	73	,	,	PUNCT
aiti-345	219	74	cybernetics	cybernetic	NOUN
aiti-345	219	75	and	and	CCONJ
aiti-345	219	76	informatics	informatic	NOUN
aiti-345	219	77	,	,	PUNCT
aiti-345	219	78	vol	vol	NOUN
aiti-345	219	79	.	.	PROPN
aiti-345	219	80	8	8	NUM
aiti-345	219	81	,	,	PUNCT
aiti-345	219	82	pp	pp	ADJ
aiti-345	219	83	.	.	PUNCT
aiti-345	219	84	4104	4104	NUM
aiti-345	219	85	-	-	PUNCT
aiti-345	219	86	4109	4109	NUM
aiti-345	219	87	,	,	PUNCT
aiti-345	219	88	1997	1997	NUM
aiti-345	219	89	.	.	PUNCT
aiti-345	220	1	[	[	X
aiti-345	220	2	11	11	NUM
aiti-345	220	3	]	]	PUNCT
aiti-345	220	4	v.	v.	ADP
aiti-345	220	5	vapnik	vapnik	X
aiti-345	220	6	,	,	PUNCT
aiti-345	220	7	the	the	DET
aiti-345	220	8	nature	nature	NOUN
aiti-345	220	9	of	of	ADP
aiti-345	220	10	statistical	statistical	ADJ
aiti-345	220	11	learning	learning	NOUN
aiti-345	220	12	theory	theory	NOUN
aiti-345	220	13	,	,	PUNCT
aiti-345	220	14	new	new	PROPN
aiti-345	220	15	york	york	PROPN
aiti-345	220	16	:	:	PUNCT
aiti-345	220	17	springer	springer	NOUN
aiti-345	220	18	-	-	PUNCT
aiti-345	220	19	verlag	verlag	PROPN
aiti-345	220	20	,	,	PUNCT
aiti-345	220	21	1995	1995	NUM
aiti-345	220	22	.	.	PUNCT
aiti-345	221	1	[	[	X
aiti-345	221	2	12	12	NUM
aiti-345	221	3	]	]	X
aiti-345	221	4	c.	c.	PROPN
aiti-345	221	5	c.	c.	PROPN
aiti-345	221	6	chang	chang	PROPN
aiti-345	221	7	,	,	PUNCT
aiti-345	221	8	and	and	CCONJ
aiti-345	221	9	c.	c.	PROPN
aiti-345	221	10	j.	j.	PROPN
aiti-345	221	11	lin	lin	PROPN
aiti-345	221	12	,	,	PUNCT
aiti-345	221	13	"	"	PUNCT
aiti-345	221	14	training	train	VERB
aiti-345	221	15	nu	nu	NOUN
aiti-345	221	16	-	-	PUNCT
aiti-345	221	17	support	support	NOUN
aiti-345	221	18	vector	vector	NOUN
aiti-345	221	19	regression	regression	NOUN
aiti-345	221	20	:	:	PUNCT
aiti-345	221	21	theory	theory	NOUN
aiti-345	221	22	and	and	CCONJ
aiti-345	221	23	algorithms	algorithm	NOUN
aiti-345	221	24	,	,	PUNCT
aiti-345	221	25	"	"	PUNCT
aiti-345	221	26	neural	neural	ADJ
aiti-345	221	27	computation	computation	NOUN
aiti-345	221	28	,	,	PUNCT
aiti-345	221	29	vol	vol	NOUN
aiti-345	221	30	.	.	PROPN
aiti-345	221	31	14	14	NUM
aiti-345	221	32	,	,	PUNCT
aiti-345	221	33	pp	pp	ADJ
aiti-345	221	34	.	.	PUNCT
aiti-345	221	35	1959	1959	NUM
aiti-345	221	36	-	-	SYM
aiti-345	221	37	1977	1977	NUM
aiti-345	221	38	,	,	PUNCT
aiti-345	221	39	2002	2002	NUM
aiti-345	221	40	.	.	PUNCT