id	sid	tid	token	lemma	pos
ajst-29869	1	1	academic	academic	ADJ
ajst-29869	1	2	journal	journal	NOUN
ajst-29869	1	3	of	of	ADP
ajst-29869	1	4	science	science	NOUN
ajst-29869	1	5	and	and	CCONJ
ajst-29869	1	6	technology	technology	NOUN
ajst-29869	1	7	issn	issn	NOUN
ajst-29869	1	8	:	:	PUNCT
ajst-29869	1	9	2771	2771	NUM
ajst-29869	1	10	-	-	SYM
ajst-29869	1	11	3032	3032	NUM
ajst-29869	1	12	|	|	NOUN
ajst-29869	1	13	vol	vol	NOUN
ajst-29869	1	14	.	.	PUNCT
ajst-29869	2	1	14	14	NUM
ajst-29869	2	2	,	,	PUNCT
ajst-29869	2	3	no	no	INTJ
ajst-29869	2	4	.	.	NOUN
ajst-29869	2	5	2	2	NUM
ajst-29869	2	6	,	,	PUNCT
ajst-29869	2	7	2025	2025	NUM
ajst-29869	2	8	145	145	NUM
ajst-29869	2	9	research	research	NOUN
ajst-29869	2	10	on	on	ADP
ajst-29869	2	11	feature	feature	NOUN
ajst-29869	2	12	optimization	optimization	NOUN
ajst-29869	2	13	algorithm	algorithm	NOUN
ajst-29869	2	14	for	for	ADP
ajst-29869	2	15	rice	rice	NOUN
ajst-29869	2	16	crops	crop	NOUN
ajst-29869	2	17	under	under	ADP
ajst-29869	2	18	mountainous	mountainous	ADJ
ajst-29869	2	19	conditions	condition	NOUN
ajst-29869	2	20	yuyao	yuyao	NOUN
ajst-29869	2	21	wang1	wang1	PROPN
ajst-29869	2	22	,	,	PUNCT
ajst-29869	2	23	*	*	SYM
ajst-29869	2	24	1	1	NUM
ajst-29869	2	25	school	school	NOUN
ajst-29869	2	26	of	of	ADP
ajst-29869	2	27	surveying	surveying	NOUN
ajst-29869	2	28	&	&	CCONJ
ajst-29869	2	29	land	land	PROPN
ajst-29869	2	30	information	information	PROPN
ajst-29869	2	31	engineering	engineering	PROPN
ajst-29869	2	32	,	,	PUNCT
ajst-29869	2	33	henan	henan	PROPN
ajst-29869	2	34	polytechnic	polytechnic	PROPN
ajst-29869	2	35	university	university	PROPN
ajst-29869	2	36	,	,	PUNCT
ajst-29869	2	37	jiaozuo	jiaozuo	PROPN
ajst-29869	2	38	454000	454000	NUM
ajst-29869	2	39	,	,	PUNCT
ajst-29869	2	40	china	china	PROPN
ajst-29869	2	41	*	*	PUNCT
ajst-29869	2	42	corresponding	correspond	VERB
ajst-29869	2	43	author	author	NOUN
ajst-29869	2	44	:	:	PUNCT
ajst-29869	2	45	yuyao	yuyao	PROPN
ajst-29869	2	46	wang	wang	PROPN
ajst-29869	2	47	(	(	PUNCT
ajst-29869	2	48	email	email	NOUN
ajst-29869	2	49	:	:	PUNCT
ajst-29869	2	50	212204010001@home.hpu.edu.cn	212204010001@home.hpu.edu.cn	NUM
ajst-29869	2	51	)	)	PUNCT
ajst-29869	2	52	abstract	abstract	NOUN
ajst-29869	2	53	:	:	PUNCT
ajst-29869	2	54	to	to	PART
ajst-29869	2	55	improve	improve	VERB
ajst-29869	2	56	the	the	DET
ajst-29869	2	57	accuracy	accuracy	NOUN
ajst-29869	2	58	and	and	CCONJ
ajst-29869	2	59	efficiency	efficiency	NOUN
ajst-29869	2	60	of	of	ADP
ajst-29869	2	61	rice	rice	NOUN
ajst-29869	2	62	recognition	recognition	NOUN
ajst-29869	2	63	in	in	ADP
ajst-29869	2	64	mountainous	mountainous	ADJ
ajst-29869	2	65	areas	area	NOUN
ajst-29869	2	66	,	,	PUNCT
ajst-29869	2	67	this	this	DET
ajst-29869	2	68	paper	paper	NOUN
ajst-29869	2	69	proposes	propose	VERB
ajst-29869	2	70	a	a	DET
ajst-29869	2	71	feature	feature	NOUN
ajst-29869	2	72	selection	selection	NOUN
ajst-29869	2	73	method	method	NOUN
ajst-29869	2	74	combining	combine	VERB
ajst-29869	2	75	the	the	DET
ajst-29869	2	76	gray	gray	ADJ
ajst-29869	2	77	wolf	wolf	NOUN
ajst-29869	2	78	optimization	optimization	NOUN
ajst-29869	2	79	(	(	PUNCT
ajst-29869	2	80	gwo	gwo	NOUN
ajst-29869	2	81	)	)	PUNCT
ajst-29869	2	82	algorithm	algorithm	NOUN
ajst-29869	2	83	and	and	CCONJ
ajst-29869	2	84	the	the	DET
ajst-29869	2	85	random	random	ADJ
ajst-29869	2	86	forest	forest	NOUN
ajst-29869	2	87	classifier	classifier	NOUN
ajst-29869	2	88	.	.	PUNCT
ajst-29869	3	1	first	first	ADV
ajst-29869	3	2	,	,	PUNCT
ajst-29869	3	3	the	the	DET
ajst-29869	3	4	relief	relief	NOUN
ajst-29869	3	5	algorithm	algorithm	NOUN
ajst-29869	3	6	is	be	AUX
ajst-29869	3	7	applied	apply	VERB
ajst-29869	3	8	to	to	AUX
ajst-29869	3	9	further	far	ADV
ajst-29869	3	10	filter	filter	VERB
ajst-29869	3	11	the	the	DET
ajst-29869	3	12	initial	initial	ADJ
ajst-29869	3	13	feature	feature	NOUN
ajst-29869	3	14	set	set	VERB
ajst-29869	3	15	by	by	ADP
ajst-29869	3	16	calculating	calculate	VERB
ajst-29869	3	17	feature	feature	NOUN
ajst-29869	3	18	weights	weight	NOUN
ajst-29869	3	19	and	and	CCONJ
ajst-29869	3	20	setting	set	VERB
ajst-29869	3	21	appropriate	appropriate	ADJ
ajst-29869	3	22	thresholds	threshold	NOUN
ajst-29869	3	23	,	,	PUNCT
ajst-29869	3	24	optimizing	optimize	VERB
ajst-29869	3	25	the	the	DET
ajst-29869	3	26	feature	feature	NOUN
ajst-29869	3	27	subset	subset	NOUN
ajst-29869	3	28	's	's	PART
ajst-29869	3	29	dimensionality	dimensionality	NOUN
ajst-29869	3	30	and	and	CCONJ
ajst-29869	3	31	classification	classification	NOUN
ajst-29869	3	32	accuracy	accuracy	NOUN
ajst-29869	3	33	.	.	PUNCT
ajst-29869	4	1	then	then	ADV
ajst-29869	4	2	,	,	PUNCT
ajst-29869	4	3	the	the	DET
ajst-29869	4	4	gwo	gwo	PROPN
ajst-29869	4	5	algorithm	algorithm	PROPN
ajst-29869	4	6	is	be	AUX
ajst-29869	4	7	used	use	VERB
ajst-29869	4	8	to	to	PART
ajst-29869	4	9	further	far	ADV
ajst-29869	4	10	optimize	optimize	VERB
ajst-29869	4	11	the	the	DET
ajst-29869	4	12	feature	feature	NOUN
ajst-29869	4	13	subset	subset	NOUN
ajst-29869	4	14	,	,	PUNCT
ajst-29869	4	15	reducing	reduce	VERB
ajst-29869	4	16	feature	feature	NOUN
ajst-29869	4	17	redundancy	redundancy	NOUN
ajst-29869	4	18	and	and	CCONJ
ajst-29869	4	19	correlation	correlation	NOUN
ajst-29869	4	20	,	,	PUNCT
ajst-29869	4	21	and	and	CCONJ
ajst-29869	4	22	improving	improve	VERB
ajst-29869	4	23	the	the	DET
ajst-29869	4	24	quality	quality	NOUN
ajst-29869	4	25	of	of	ADP
ajst-29869	4	26	the	the	DET
ajst-29869	4	27	final	final	ADJ
ajst-29869	4	28	feature	feature	NOUN
ajst-29869	4	29	set	set	NOUN
ajst-29869	4	30	.	.	PUNCT
ajst-29869	5	1	the	the	DET
ajst-29869	5	2	gwo	gwo	PROPN
ajst-29869	5	3	algorithm	algorithm	PROPN
ajst-29869	5	4	simulates	simulate	VERB
ajst-29869	5	5	the	the	DET
ajst-29869	5	6	hunting	hunt	VERB
ajst-29869	5	7	behavior	behavior	NOUN
ajst-29869	5	8	of	of	ADP
ajst-29869	5	9	gray	gray	ADJ
ajst-29869	5	10	wolf	wolf	NOUN
ajst-29869	5	11	packs	pack	NOUN
ajst-29869	5	12	,	,	PUNCT
ajst-29869	5	13	dynamically	dynamically	ADV
ajst-29869	5	14	adjusting	adjust	VERB
ajst-29869	5	15	the	the	DET
ajst-29869	5	16	positions	position	NOUN
ajst-29869	5	17	of	of	ADP
ajst-29869	5	18	the	the	DET
ajst-29869	5	19	wolves	wolf	NOUN
ajst-29869	5	20	to	to	PART
ajst-29869	5	21	gradually	gradually	ADV
ajst-29869	5	22	approach	approach	VERB
ajst-29869	5	23	the	the	DET
ajst-29869	5	24	optimal	optimal	ADJ
ajst-29869	5	25	solution	solution	NOUN
ajst-29869	5	26	.	.	PUNCT
ajst-29869	6	1	during	during	ADP
ajst-29869	6	2	the	the	DET
ajst-29869	6	3	optimization	optimization	NOUN
ajst-29869	6	4	process	process	NOUN
ajst-29869	6	5	,	,	PUNCT
ajst-29869	6	6	the	the	DET
ajst-29869	6	7	random	random	ADJ
ajst-29869	6	8	forest	forest	NOUN
ajst-29869	6	9	classifier	classifier	NOUN
ajst-29869	6	10	serves	serve	VERB
ajst-29869	6	11	as	as	ADP
ajst-29869	6	12	the	the	DET
ajst-29869	6	13	fitness	fitness	NOUN
ajst-29869	6	14	function	function	NOUN
ajst-29869	6	15	to	to	PART
ajst-29869	6	16	evaluate	evaluate	VERB
ajst-29869	6	17	the	the	DET
ajst-29869	6	18	classification	classification	NOUN
ajst-29869	6	19	accuracy	accuracy	NOUN
ajst-29869	6	20	of	of	ADP
ajst-29869	6	21	each	each	DET
ajst-29869	6	22	feature	feature	NOUN
ajst-29869	6	23	subset	subset	VERB
ajst-29869	6	24	.	.	PUNCT
ajst-29869	7	1	through	through	ADP
ajst-29869	7	2	an	an	DET
ajst-29869	7	3	iterative	iterative	NOUN
ajst-29869	7	4	update	update	NOUN
ajst-29869	7	5	process	process	NOUN
ajst-29869	7	6	,	,	PUNCT
ajst-29869	7	7	the	the	DET
ajst-29869	7	8	gwo_rf	gwo_rf	PROPN
ajst-29869	7	9	algorithm	algorithm	PROPN
ajst-29869	7	10	successfully	successfully	ADV
ajst-29869	7	11	selects	select	VERB
ajst-29869	7	12	the	the	DET
ajst-29869	7	13	optimal	optimal	ADJ
ajst-29869	7	14	feature	feature	NOUN
ajst-29869	7	15	subset	subset	NOUN
ajst-29869	7	16	,	,	PUNCT
ajst-29869	7	17	achieving	achieve	VERB
ajst-29869	7	18	high	high	ADJ
ajst-29869	7	19	recognition	recognition	NOUN
ajst-29869	7	20	accuracy	accuracy	NOUN
ajst-29869	7	21	with	with	ADP
ajst-29869	7	22	reduced	reduced	ADJ
ajst-29869	7	23	computational	computational	ADJ
ajst-29869	7	24	complexity	complexity	NOUN
ajst-29869	7	25	.	.	PUNCT
ajst-29869	8	1	experimental	experimental	ADJ
ajst-29869	8	2	results	result	NOUN
ajst-29869	8	3	show	show	VERB
ajst-29869	8	4	that	that	SCONJ
ajst-29869	8	5	the	the	DET
ajst-29869	8	6	proposed	propose	VERB
ajst-29869	8	7	method	method	NOUN
ajst-29869	8	8	significantly	significantly	ADV
ajst-29869	8	9	improves	improve	VERB
ajst-29869	8	10	recognition	recognition	NOUN
ajst-29869	8	11	accuracy	accuracy	NOUN
ajst-29869	8	12	and	and	CCONJ
ajst-29869	8	13	effectively	effectively	ADV
ajst-29869	8	14	reduces	reduce	VERB
ajst-29869	8	15	computation	computation	NOUN
ajst-29869	8	16	time	time	NOUN
ajst-29869	8	17	,	,	PUNCT
ajst-29869	8	18	providing	provide	VERB
ajst-29869	8	19	a	a	DET
ajst-29869	8	20	new	new	ADJ
ajst-29869	8	21	approach	approach	NOUN
ajst-29869	8	22	for	for	ADP
ajst-29869	8	23	the	the	DET
ajst-29869	8	24	efficient	efficient	ADJ
ajst-29869	8	25	analysis	analysis	NOUN
ajst-29869	8	26	of	of	ADP
ajst-29869	8	27	large	large	ADJ
ajst-29869	8	28	-	-	PUNCT
ajst-29869	8	29	scale	scale	NOUN
ajst-29869	8	30	remote	remote	ADJ
ajst-29869	8	31	sensing	sensing	NOUN
ajst-29869	8	32	images	image	NOUN
ajst-29869	8	33	.	.	PUNCT
ajst-29869	9	1	keywords	keyword	NOUN
ajst-29869	9	2	:	:	PUNCT
ajst-29869	9	3	gray	gray	ADJ
ajst-29869	9	4	wolf	wolf	PROPN
ajst-29869	9	5	optimization	optimization	NOUN
ajst-29869	9	6	,	,	PUNCT
ajst-29869	9	7	feature	feature	NOUN
ajst-29869	9	8	selection	selection	NOUN
ajst-29869	9	9	,	,	PUNCT
ajst-29869	9	10	random	random	ADJ
ajst-29869	9	11	forest	forest	NOUN
ajst-29869	9	12	,	,	PUNCT
ajst-29869	9	13	relief	relief	NOUN
ajst-29869	9	14	algorithm	algorithm	NOUN
ajst-29869	9	15	,	,	PUNCT
ajst-29869	9	16	rice	rice	NOUN
ajst-29869	9	17	recognition	recognition	NOUN
ajst-29869	9	18	.	.	PUNCT
ajst-29869	10	1	1	1	X
ajst-29869	10	2	.	.	X
ajst-29869	10	3	introduction	introduction	NOUN
ajst-29869	10	4	feature	feature	NOUN
ajst-29869	10	5	selection	selection	NOUN
ajst-29869	10	6	is	be	AUX
ajst-29869	10	7	a	a	DET
ajst-29869	10	8	critical	critical	ADJ
ajst-29869	10	9	step	step	NOUN
ajst-29869	10	10	in	in	ADP
ajst-29869	10	11	data	datum	NOUN
ajst-29869	10	12	mining	mining	NOUN
ajst-29869	10	13	,	,	PUNCT
ajst-29869	10	14	aiming	aim	VERB
ajst-29869	10	15	to	to	PART
ajst-29869	10	16	identify	identify	VERB
ajst-29869	10	17	the	the	DET
ajst-29869	10	18	most	most	ADV
ajst-29869	10	19	relevant	relevant	ADJ
ajst-29869	10	20	features	feature	NOUN
ajst-29869	10	21	from	from	ADP
ajst-29869	10	22	a	a	DET
ajst-29869	10	23	large	large	ADJ
ajst-29869	10	24	set	set	NOUN
ajst-29869	10	25	of	of	ADP
ajst-29869	10	26	raw	raw	ADJ
ajst-29869	10	27	features	feature	NOUN
ajst-29869	10	28	while	while	SCONJ
ajst-29869	10	29	eliminating	eliminate	VERB
ajst-29869	10	30	redundant	redundant	ADJ
ajst-29869	10	31	and	and	CCONJ
ajst-29869	10	32	irrelevant	irrelevant	ADJ
ajst-29869	10	33	ones	one	NOUN
ajst-29869	10	34	.	.	PUNCT
ajst-29869	11	1	this	this	DET
ajst-29869	11	2	process	process	NOUN
ajst-29869	11	3	reduces	reduce	VERB
ajst-29869	11	4	computational	computational	ADJ
ajst-29869	11	5	costs	cost	NOUN
ajst-29869	11	6	,	,	PUNCT
ajst-29869	11	7	enhances	enhance	VERB
ajst-29869	11	8	model	model	NOUN
ajst-29869	11	9	performance	performance	NOUN
ajst-29869	11	10	,	,	PUNCT
ajst-29869	11	11	and	and	CCONJ
ajst-29869	11	12	improves	improve	VERB
ajst-29869	11	13	data	datum	NOUN
ajst-29869	11	14	interpretability	interpretability	NOUN
ajst-29869	11	15	[	[	X
ajst-29869	11	16	1	1	NUM
ajst-29869	11	17	]	]	PUNCT
ajst-29869	11	18	.	.	PUNCT
ajst-29869	12	1	current	current	ADJ
ajst-29869	12	2	research	research	NOUN
ajst-29869	12	3	on	on	ADP
ajst-29869	12	4	feature	feature	NOUN
ajst-29869	12	5	selection	selection	NOUN
ajst-29869	12	6	primarily	primarily	ADV
ajst-29869	12	7	falls	fall	VERB
ajst-29869	12	8	into	into	ADP
ajst-29869	12	9	two	two	NUM
ajst-29869	12	10	categories	category	NOUN
ajst-29869	12	11	:	:	PUNCT
ajst-29869	12	12	one	one	NUM
ajst-29869	12	13	evaluates	evaluate	VERB
ajst-29869	12	14	the	the	DET
ajst-29869	12	15	importance	importance	NOUN
ajst-29869	12	16	of	of	ADP
ajst-29869	12	17	each	each	DET
ajst-29869	12	18	feature	feature	NOUN
ajst-29869	12	19	to	to	ADP
ajst-29869	12	20	the	the	DET
ajst-29869	12	21	target	target	NOUN
ajst-29869	12	22	task	task	NOUN
ajst-29869	12	23	,	,	PUNCT
ajst-29869	12	24	selecting	select	VERB
ajst-29869	12	25	essential	essential	ADJ
ajst-29869	12	26	features	feature	NOUN
ajst-29869	12	27	and	and	CCONJ
ajst-29869	12	28	discarding	discard	VERB
ajst-29869	12	29	unimportant	unimportant	ADJ
ajst-29869	12	30	ones	one	NOUN
ajst-29869	12	31	;	;	PUNCT
ajst-29869	12	32	the	the	DET
ajst-29869	12	33	other	other	ADJ
ajst-29869	12	34	integrates	integrate	NOUN
ajst-29869	12	35	multidimensional	multidimensional	ADJ
ajst-29869	12	36	features	feature	NOUN
ajst-29869	12	37	through	through	ADP
ajst-29869	12	38	algorithms	algorithm	NOUN
ajst-29869	12	39	to	to	PART
ajst-29869	12	40	achieve	achieve	VERB
ajst-29869	12	41	dimensionality	dimensionality	NOUN
ajst-29869	12	42	reduction	reduction	NOUN
ajst-29869	12	43	while	while	SCONJ
ajst-29869	12	44	preserving	preserve	VERB
ajst-29869	12	45	as	as	ADV
ajst-29869	12	46	much	much	ADJ
ajst-29869	12	47	feature	feature	NOUN
ajst-29869	12	48	information	information	NOUN
ajst-29869	12	49	as	as	ADP
ajst-29869	12	50	possible	possible	ADJ
ajst-29869	12	51	.	.	PUNCT
ajst-29869	13	1	however	however	ADV
ajst-29869	13	2	,	,	PUNCT
ajst-29869	13	3	the	the	DET
ajst-29869	13	4	features	feature	NOUN
ajst-29869	13	5	obtained	obtain	VERB
ajst-29869	13	6	through	through	ADP
ajst-29869	13	7	the	the	DET
ajst-29869	13	8	latter	latter	ADJ
ajst-29869	13	9	approach	approach	NOUN
ajst-29869	13	10	often	often	ADV
ajst-29869	13	11	lack	lack	VERB
ajst-29869	13	12	interpretability	interpretability	NOUN
ajst-29869	13	13	[	[	X
ajst-29869	13	14	2	2	NUM
ajst-29869	13	15	-	-	SYM
ajst-29869	13	16	5	5	NUM
ajst-29869	13	17	]	]	PUNCT
ajst-29869	13	18	.	.	PUNCT
ajst-29869	14	1	this	this	DET
ajst-29869	14	2	issue	issue	NOUN
ajst-29869	14	3	is	be	AUX
ajst-29869	14	4	particularly	particularly	ADV
ajst-29869	14	5	pronounced	pronounce	VERB
ajst-29869	14	6	in	in	ADP
ajst-29869	14	7	land	land	NOUN
ajst-29869	14	8	cover	cover	NOUN
ajst-29869	14	9	classification	classification	NOUN
ajst-29869	14	10	tasks	task	NOUN
ajst-29869	14	11	,	,	PUNCT
ajst-29869	14	12	where	where	SCONJ
ajst-29869	14	13	reducing	reduce	VERB
ajst-29869	14	14	high	high	ADJ
ajst-29869	14	15	-	-	PUNCT
ajst-29869	14	16	dimensional	dimensional	ADJ
ajst-29869	14	17	features	feature	NOUN
ajst-29869	14	18	via	via	ADP
ajst-29869	14	19	algorithmic	algorithmic	ADJ
ajst-29869	14	20	methods	method	NOUN
ajst-29869	14	21	hinders	hinder	VERB
ajst-29869	14	22	the	the	DET
ajst-29869	14	23	understanding	understanding	NOUN
ajst-29869	14	24	of	of	ADP
ajst-29869	14	25	their	their	PRON
ajst-29869	14	26	real	real	ADJ
ajst-29869	14	27	-	-	PUNCT
ajst-29869	14	28	world	world	NOUN
ajst-29869	14	29	significance	significance	NOUN
ajst-29869	14	30	and	and	CCONJ
ajst-29869	14	31	the	the	DET
ajst-29869	14	32	characteristics	characteristic	NOUN
ajst-29869	14	33	they	they	PRON
ajst-29869	14	34	represent	represent	VERB
ajst-29869	14	35	across	across	ADP
ajst-29869	14	36	different	different	ADJ
ajst-29869	14	37	categories	category	NOUN
ajst-29869	14	38	.	.	PUNCT
ajst-29869	15	1	therefore	therefore	ADV
ajst-29869	15	2	,	,	PUNCT
ajst-29869	15	3	this	this	DET
ajst-29869	15	4	study	study	NOUN
ajst-29869	15	5	adopts	adopt	VERB
ajst-29869	15	6	a	a	DET
ajst-29869	15	7	feature	feature	NOUN
ajst-29869	15	8	selection	selection	NOUN
ajst-29869	15	9	approach	approach	NOUN
ajst-29869	15	10	based	base	VERB
ajst-29869	15	11	on	on	ADP
ajst-29869	15	12	quantifying	quantify	VERB
ajst-29869	15	13	feature	feature	NOUN
ajst-29869	15	14	importance	importance	NOUN
ajst-29869	15	15	,	,	PUNCT
ajst-29869	15	16	identifying	identify	VERB
ajst-29869	15	17	the	the	DET
ajst-29869	15	18	optimal	optimal	ADJ
ajst-29869	15	19	feature	feature	NOUN
ajst-29869	15	20	subset	subset	VERB
ajst-29869	15	21	from	from	ADP
ajst-29869	15	22	the	the	DET
ajst-29869	15	23	original	original	ADJ
ajst-29869	15	24	feature	feature	NOUN
ajst-29869	15	25	set	set	VERB
ajst-29869	15	26	while	while	SCONJ
ajst-29869	15	27	preserving	preserve	VERB
ajst-29869	15	28	the	the	DET
ajst-29869	15	29	interpretability	interpretability	NOUN
ajst-29869	15	30	of	of	ADP
ajst-29869	15	31	each	each	DET
ajst-29869	15	32	feature	feature	NOUN
ajst-29869	15	33	.	.	PUNCT
ajst-29869	16	1	in	in	ADP
ajst-29869	16	2	mountainous	mountainous	ADJ
ajst-29869	16	3	rice	rice	NOUN
ajst-29869	16	4	feature	feature	NOUN
ajst-29869	16	5	extraction	extraction	NOUN
ajst-29869	16	6	,	,	PUNCT
ajst-29869	16	7	this	this	DET
ajst-29869	16	8	study	study	NOUN
ajst-29869	16	9	extracted	extract	VERB
ajst-29869	16	10	a	a	DET
ajst-29869	16	11	total	total	NOUN
ajst-29869	16	12	of	of	ADP
ajst-29869	16	13	55	55	NUM
ajst-29869	16	14	spectral	spectral	ADJ
ajst-29869	16	15	,	,	PUNCT
ajst-29869	16	16	textural	textural	ADJ
ajst-29869	16	17	,	,	PUNCT
ajst-29869	16	18	polarimetric	polarimetric	ADJ
ajst-29869	16	19	,	,	PUNCT
ajst-29869	16	20	and	and	CCONJ
ajst-29869	16	21	topographic	topographic	NOUN
ajst-29869	16	22	features	feature	VERB
ajst-29869	16	23	[	[	X
ajst-29869	16	24	6	6	NUM
ajst-29869	16	25	-	-	SYM
ajst-29869	16	26	7	7	NUM
ajst-29869	16	27	]	]	PUNCT
ajst-29869	16	28	.	.	PUNCT
ajst-29869	17	1	due	due	ADP
ajst-29869	17	2	to	to	ADP
ajst-29869	17	3	potential	potential	ADJ
ajst-29869	17	4	coupling	coupling	NOUN
ajst-29869	17	5	among	among	ADP
ajst-29869	17	6	these	these	DET
ajst-29869	17	7	features	feature	NOUN
ajst-29869	17	8	,	,	PUNCT
ajst-29869	17	9	a	a	DET
ajst-29869	17	10	high	high	ADJ
ajst-29869	17	11	level	level	NOUN
ajst-29869	17	12	of	of	ADP
ajst-29869	17	13	redundancy	redundancy	NOUN
ajst-29869	17	14	may	may	AUX
ajst-29869	17	15	exist	exist	VERB
ajst-29869	17	16	,	,	PUNCT
ajst-29869	17	17	making	make	VERB
ajst-29869	17	18	it	it	PRON
ajst-29869	17	19	challenging	challenge	VERB
ajst-29869	17	20	for	for	ADP
ajst-29869	17	21	subsequent	subsequent	ADJ
ajst-29869	17	22	classification	classification	NOUN
ajst-29869	17	23	models	model	NOUN
ajst-29869	17	24	to	to	PART
ajst-29869	17	25	effectively	effectively	ADV
ajst-29869	17	26	capture	capture	VERB
ajst-29869	17	27	key	key	ADJ
ajst-29869	17	28	features	feature	NOUN
ajst-29869	17	29	.	.	PUNCT
ajst-29869	18	1	traditional	traditional	ADJ
ajst-29869	18	2	feature	feature	NOUN
ajst-29869	18	3	selection	selection	NOUN
ajst-29869	18	4	methods	method	NOUN
ajst-29869	18	5	often	often	ADV
ajst-29869	18	6	focus	focus	VERB
ajst-29869	18	7	on	on	ADP
ajst-29869	18	8	a	a	DET
ajst-29869	18	9	specific	specific	ADJ
ajst-29869	18	10	type	type	NOUN
ajst-29869	18	11	of	of	ADP
ajst-29869	18	12	feature	feature	NOUN
ajst-29869	18	13	and	and	CCONJ
ajst-29869	18	14	fail	fail	VERB
ajst-29869	18	15	to	to	PART
ajst-29869	18	16	consider	consider	VERB
ajst-29869	18	17	the	the	DET
ajst-29869	18	18	complementarity	complementarity	NOUN
ajst-29869	18	19	between	between	ADP
ajst-29869	18	20	multiple	multiple	ADJ
ajst-29869	18	21	features	feature	NOUN
ajst-29869	18	22	.	.	PUNCT
ajst-29869	19	1	this	this	DET
ajst-29869	19	2	limitation	limitation	NOUN
ajst-29869	19	3	is	be	AUX
ajst-29869	19	4	particularly	particularly	ADV
ajst-29869	19	5	problematic	problematic	ADJ
ajst-29869	19	6	in	in	ADP
ajst-29869	19	7	complex	complex	ADJ
ajst-29869	19	8	mountainous	mountainous	ADJ
ajst-29869	19	9	environments	environment	NOUN
ajst-29869	19	10	,	,	PUNCT
ajst-29869	19	11	where	where	SCONJ
ajst-29869	19	12	a	a	DET
ajst-29869	19	13	single	single	ADJ
ajst-29869	19	14	feature	feature	NOUN
ajst-29869	19	15	selection	selection	NOUN
ajst-29869	19	16	strategy	strategy	NOUN
ajst-29869	19	17	may	may	AUX
ajst-29869	19	18	overlook	overlook	VERB
ajst-29869	19	19	critical	critical	ADJ
ajst-29869	19	20	features	feature	NOUN
ajst-29869	19	21	or	or	CCONJ
ajst-29869	19	22	introduce	introduce	VERB
ajst-29869	19	23	irrelevant	irrelevant	ADJ
ajst-29869	19	24	ones	one	NOUN
ajst-29869	19	25	,	,	PUNCT
ajst-29869	19	26	ultimately	ultimately	ADV
ajst-29869	19	27	reducing	reduce	VERB
ajst-29869	19	28	the	the	DET
ajst-29869	19	29	accuracy	accuracy	NOUN
ajst-29869	19	30	of	of	ADP
ajst-29869	19	31	rice	rice	NOUN
ajst-29869	19	32	identification[8	identification[8	NOUN
ajst-29869	19	33	]	]	PUNCT
ajst-29869	19	34	.	.	PUNCT
ajst-29869	20	1	therefore	therefore	ADV
ajst-29869	20	2	,	,	PUNCT
ajst-29869	20	3	a	a	DET
ajst-29869	20	4	feature	feature	NOUN
ajst-29869	20	5	selection	selection	NOUN
ajst-29869	20	6	method	method	NOUN
ajst-29869	20	7	that	that	PRON
ajst-29869	20	8	can	can	AUX
ajst-29869	20	9	fully	fully	ADV
ajst-29869	20	10	exploit	exploit	VERB
ajst-29869	20	11	multi	multi	ADJ
ajst-29869	20	12	-	-	ADJ
ajst-29869	20	13	source	source	ADJ
ajst-29869	20	14	feature	feature	NOUN
ajst-29869	20	15	information	information	NOUN
ajst-29869	20	16	and	and	CCONJ
ajst-29869	20	17	enhance	enhance	VERB
ajst-29869	20	18	the	the	DET
ajst-29869	20	19	robustness	robustness	NOUN
ajst-29869	20	20	of	of	ADP
ajst-29869	20	21	mountainous	mountainous	ADJ
ajst-29869	20	22	rice	rice	NOUN
ajst-29869	20	23	identification	identification	NOUN
ajst-29869	20	24	is	be	AUX
ajst-29869	20	25	urgently	urgently	ADV
ajst-29869	20	26	needed	need	VERB
ajst-29869	20	27	.	.	PUNCT
ajst-29869	21	1	to	to	PART
ajst-29869	21	2	address	address	VERB
ajst-29869	21	3	this	this	DET
ajst-29869	21	4	issue	issue	NOUN
ajst-29869	21	5	,	,	PUNCT
ajst-29869	21	6	this	this	DET
ajst-29869	21	7	study	study	NOUN
ajst-29869	21	8	proposes	propose	VERB
ajst-29869	21	9	a	a	DET
ajst-29869	21	10	feature	feature	NOUN
ajst-29869	21	11	selection	selection	NOUN
ajst-29869	21	12	model	model	NOUN
ajst-29869	21	13	that	that	PRON
ajst-29869	21	14	integrates	integrate	VERB
ajst-29869	21	15	the	the	DET
ajst-29869	21	16	mutual	mutual	ADJ
ajst-29869	21	17	information	information	NOUN
ajst-29869	21	18	(	(	PUNCT
ajst-29869	21	19	mi	mi	NOUN
ajst-29869	21	20	)	)	PUNCT
ajst-29869	21	21	algorithm	algorithm	NOUN
ajst-29869	21	22	,	,	PUNCT
ajst-29869	21	23	relief	relief	NOUN
ajst-29869	21	24	algorithm	algorithm	NOUN
ajst-29869	21	25	,	,	PUNCT
ajst-29869	21	26	grey	grey	ADJ
ajst-29869	21	27	wolf	wolf	PROPN
ajst-29869	21	28	optimization	optimization	NOUN
ajst-29869	21	29	(	(	PUNCT
ajst-29869	21	30	gwo	gwo	NOUN
ajst-29869	21	31	)	)	PUNCT
ajst-29869	21	32	algorithm	algorithm	NOUN
ajst-29869	21	33	,	,	PUNCT
ajst-29869	21	34	and	and	CCONJ
ajst-29869	21	35	random	random	ADJ
ajst-29869	21	36	forest	forest	NOUN
ajst-29869	21	37	(	(	PUNCT
ajst-29869	21	38	rf	rf	NOUN
ajst-29869	21	39	)	)	PUNCT
ajst-29869	21	40	algorithm	algorithm	NOUN
ajst-29869	21	41	,	,	PUNCT
ajst-29869	21	42	forming	form	VERB
ajst-29869	21	43	the	the	DET
ajst-29869	21	44	mi	mi	ADJ
ajst-29869	21	45	-	-	ADJ
ajst-29869	21	46	reliefgwo_rf	reliefgwo_rf	ADJ
ajst-29869	21	47	model	model	NOUN
ajst-29869	21	48	,	,	PUNCT
ajst-29869	21	49	abbreviated	abbreviate	VERB
ajst-29869	21	50	as	as	ADP
ajst-29869	21	51	mrg_rf	mrg_rf	PROPN
ajst-29869	21	52	.	.	PUNCT
ajst-29869	22	1	this	this	DET
ajst-29869	22	2	model	model	NOUN
ajst-29869	22	3	is	be	AUX
ajst-29869	22	4	used	use	VERB
ajst-29869	22	5	to	to	PART
ajst-29869	22	6	determine	determine	VERB
ajst-29869	22	7	the	the	DET
ajst-29869	22	8	optimal	optimal	ADJ
ajst-29869	22	9	feature	feature	NOUN
ajst-29869	22	10	subset	subset	VERB
ajst-29869	22	11	for	for	ADP
ajst-29869	22	12	rice	rice	NOUN
ajst-29869	22	13	identification	identification	NOUN
ajst-29869	22	14	under	under	ADP
ajst-29869	22	15	mountainous	mountainous	ADJ
ajst-29869	22	16	conditions	condition	NOUN
ajst-29869	22	17	.	.	PUNCT
ajst-29869	23	1	2	2	X
ajst-29869	23	2	.	.	X
ajst-29869	23	3	methodology	methodology	NOUN
ajst-29869	23	4	the	the	DET
ajst-29869	23	5	mrg_rf	mrg_rf	PROPN
ajst-29869	23	6	model	model	PROPN
ajst-29869	23	7	first	first	ADV
ajst-29869	23	8	employs	employ	VERB
ajst-29869	23	9	the	the	DET
ajst-29869	23	10	mutual	mutual	ADJ
ajst-29869	23	11	information	information	NOUN
ajst-29869	23	12	(	(	PUNCT
ajst-29869	23	13	mi	mi	NOUN
ajst-29869	23	14	)	)	PUNCT
ajst-29869	23	15	algorithm	algorithm	NOUN
ajst-29869	23	16	to	to	PART
ajst-29869	23	17	identify	identify	VERB
ajst-29869	23	18	a	a	DET
ajst-29869	23	19	subset	subset	NOUN
ajst-29869	23	20	of	of	ADP
ajst-29869	23	21	features	feature	NOUN
ajst-29869	23	22	with	with	ADP
ajst-29869	23	23	weak	weak	ADJ
ajst-29869	23	24	relevance	relevance	NOUN
ajst-29869	23	25	to	to	ADP
ajst-29869	23	26	the	the	DET
ajst-29869	23	27	target	target	NOUN
ajst-29869	23	28	task	task	NOUN
ajst-29869	23	29	.	.	PUNCT
ajst-29869	24	1	then	then	ADV
ajst-29869	24	2	,	,	PUNCT
ajst-29869	24	3	the	the	DET
ajst-29869	24	4	relief	relief	NOUN
ajst-29869	24	5	algorithm	algorithm	NOUN
ajst-29869	24	6	ranks	rank	VERB
ajst-29869	24	7	the	the	DET
ajst-29869	24	8	initially	initially	ADV
ajst-29869	24	9	selected	select	VERB
ajst-29869	24	10	features	feature	NOUN
ajst-29869	24	11	based	base	VERB
ajst-29869	24	12	on	on	ADP
ajst-29869	24	13	their	their	PRON
ajst-29869	24	14	importance	importance	NOUN
ajst-29869	24	15	weights	weight	NOUN
ajst-29869	24	16	and	and	CCONJ
ajst-29869	24	17	filters	filter	VERB
ajst-29869	24	18	out	out	ADP
ajst-29869	24	19	features	feature	NOUN
ajst-29869	24	20	exceeding	exceed	VERB
ajst-29869	24	21	a	a	DET
ajst-29869	24	22	predefined	predefine	VERB
ajst-29869	24	23	threshold	threshold	NOUN
ajst-29869	24	24	.	.	PUNCT
ajst-29869	25	1	the	the	DET
ajst-29869	25	2	remaining	remain	VERB
ajst-29869	25	3	features	feature	NOUN
ajst-29869	25	4	are	be	AUX
ajst-29869	25	5	retained	retain	VERB
ajst-29869	25	6	for	for	ADP
ajst-29869	25	7	the	the	DET
ajst-29869	25	8	next	next	ADJ
ajst-29869	25	9	stage	stage	NOUN
ajst-29869	25	10	of	of	ADP
ajst-29869	25	11	feature	feature	NOUN
ajst-29869	25	12	selection	selection	NOUN
ajst-29869	25	13	.	.	PUNCT
ajst-29869	26	1	subsequently	subsequently	ADV
ajst-29869	26	2	,	,	PUNCT
ajst-29869	26	3	the	the	DET
ajst-29869	26	4	grey	grey	ADJ
ajst-29869	26	5	wolf	wolf	PROPN
ajst-29869	26	6	optimization	optimization	NOUN
ajst-29869	26	7	(	(	PUNCT
ajst-29869	26	8	gwo	gwo	NOUN
ajst-29869	26	9	)	)	PUNCT
ajst-29869	26	10	algorithm	algorithm	NOUN
ajst-29869	26	11	is	be	AUX
ajst-29869	26	12	combined	combine	VERB
ajst-29869	26	13	with	with	ADP
ajst-29869	26	14	the	the	DET
ajst-29869	26	15	random	random	ADJ
ajst-29869	26	16	forest	forest	NOUN
ajst-29869	26	17	(	(	PUNCT
ajst-29869	26	18	rf	rf	NOUN
ajst-29869	26	19	)	)	PUNCT
ajst-29869	26	20	algorithm	algorithm	NOUN
ajst-29869	26	21	to	to	PART
ajst-29869	26	22	form	form	VERB
ajst-29869	26	23	a	a	DET
ajst-29869	26	24	wrapper	wrapper	NOUN
ajst-29869	26	25	-	-	PUNCT
ajst-29869	26	26	based	base	VERB
ajst-29869	26	27	feature	feature	NOUN
ajst-29869	26	28	selection	selection	NOUN
ajst-29869	26	29	method	method	NOUN
ajst-29869	26	30	(	(	PUNCT
ajst-29869	26	31	gwo_rf	gwo_rf	PROPN
ajst-29869	26	32	)	)	PUNCT
ajst-29869	26	33	.	.	PUNCT
ajst-29869	27	1	in	in	ADP
ajst-29869	27	2	this	this	DET
ajst-29869	27	3	process	process	NOUN
ajst-29869	27	4	,	,	PUNCT
ajst-29869	27	5	the	the	DET
ajst-29869	27	6	gwo	gwo	PROPN
ajst-29869	27	7	algorithm	algorithm	NOUN
ajst-29869	27	8	searches	search	NOUN
ajst-29869	27	9	for	for	ADP
ajst-29869	27	10	the	the	DET
ajst-29869	27	11	optimal	optimal	ADJ
ajst-29869	27	12	feature	feature	NOUN
ajst-29869	27	13	subset	subset	VERB
ajst-29869	27	14	within	within	ADP
ajst-29869	27	15	the	the	DET
ajst-29869	27	16	feature	feature	NOUN
ajst-29869	27	17	space	space	NOUN
ajst-29869	27	18	,	,	PUNCT
ajst-29869	27	19	while	while	SCONJ
ajst-29869	27	20	the	the	DET
ajst-29869	27	21	rf	rf	NOUN
ajst-29869	27	22	algorithm	algorithm	NOUN
ajst-29869	27	23	serves	serve	VERB
ajst-29869	27	24	as	as	ADP
ajst-29869	27	25	the	the	DET
ajst-29869	27	26	fitness	fitness	NOUN
ajst-29869	27	27	function	function	NOUN
ajst-29869	27	28	to	to	PART
ajst-29869	27	29	evaluate	evaluate	VERB
ajst-29869	27	30	the	the	DET
ajst-29869	27	31	performance	performance	NOUN
ajst-29869	27	32	of	of	ADP
ajst-29869	27	33	the	the	DET
ajst-29869	27	34	feature	feature	NOUN
ajst-29869	27	35	subsets	subset	NOUN
ajst-29869	27	36	.	.	PUNCT
ajst-29869	28	1	finally	finally	ADV
ajst-29869	28	2	,	,	PUNCT
ajst-29869	28	3	the	the	DET
ajst-29869	28	4	gwo_rf	gwo_rf	PROPN
ajst-29869	28	5	method	method	NOUN
ajst-29869	28	6	is	be	AUX
ajst-29869	28	7	applied	apply	VERB
ajst-29869	28	8	to	to	PART
ajst-29869	28	9	refine	refine	VERB
ajst-29869	28	10	the	the	DET
ajst-29869	28	11	preselected	preselecte	VERB
ajst-29869	28	12	feature	feature	NOUN
ajst-29869	28	13	set	set	NOUN
ajst-29869	28	14	,	,	PUNCT
ajst-29869	28	15	resulting	result	VERB
ajst-29869	28	16	in	in	ADP
ajst-29869	28	17	the	the	DET
ajst-29869	28	18	optimal	optimal	ADJ
ajst-29869	28	19	feature	feature	NOUN
ajst-29869	28	20	subset	subset	VERB
ajst-29869	28	21	.	.	PUNCT
ajst-29869	29	1	2.1	2.1	NUM
ajst-29869	29	2	.	.	PUNCT
ajst-29869	29	3	random	random	ADJ
ajst-29869	29	4	forest	forest	NOUN
ajst-29869	29	5	2.1.1	2.1.1	NUM
ajst-29869	29	6	.	.	PUNCT
ajst-29869	30	1	decision	decision	NOUN
ajst-29869	30	2	tree	tree	NOUN
ajst-29869	30	3	a	a	DET
ajst-29869	30	4	decision	decision	NOUN
ajst-29869	30	5	tree	tree	NOUN
ajst-29869	30	6	is	be	AUX
ajst-29869	30	7	a	a	DET
ajst-29869	30	8	recursive	recursive	ADJ
ajst-29869	30	9	partitioning	partitioning	NOUN
ajst-29869	30	10	model	model	NOUN
ajst-29869	30	11	used	use	VERB
ajst-29869	30	12	for	for	ADP
ajst-29869	30	13	classification	classification	NOUN
ajst-29869	30	14	and	and	CCONJ
ajst-29869	30	15	regression	regression	NOUN
ajst-29869	30	16	tasks	task	NOUN
ajst-29869	30	17	.	.	PUNCT
ajst-29869	31	1	its	its	PRON
ajst-29869	31	2	core	core	ADJ
ajst-29869	31	3	idea	idea	NOUN
ajst-29869	31	4	is	be	AUX
ajst-29869	31	5	to	to	PART
ajst-29869	31	6	iteratively	iteratively	ADV
ajst-29869	31	7	select	select	VERB
ajst-29869	31	8	the	the	DET
ajst-29869	31	9	most	most	ADV
ajst-29869	31	10	informative	informative	ADJ
ajst-29869	31	11	features	feature	NOUN
ajst-29869	31	12	to	to	PART
ajst-29869	31	13	split	split	VERB
ajst-29869	31	14	the	the	DET
ajst-29869	31	15	dataset	dataset	NOUN
ajst-29869	31	16	into	into	ADP
ajst-29869	31	17	increasingly	increasingly	ADV
ajst-29869	31	18	homogeneous	homogeneous	ADJ
ajst-29869	31	19	subsets	subset	NOUN
ajst-29869	31	20	,	,	PUNCT
ajst-29869	31	21	ultimately	ultimately	ADV
ajst-29869	31	22	improving	improve	VERB
ajst-29869	31	23	prediction	prediction	NOUN
ajst-29869	31	24	accuracy	accuracy	NOUN
ajst-29869	31	25	.	.	PUNCT
ajst-29869	32	1	at	at	ADP
ajst-29869	32	2	each	each	DET
ajst-29869	32	3	split	split	NOUN
ajst-29869	32	4	,	,	PUNCT
ajst-29869	32	5	the	the	DET
ajst-29869	32	6	algorithm	algorithm	NOUN
ajst-29869	32	7	determines	determine	VERB
ajst-29869	32	8	the	the	DET
ajst-29869	32	9	optimal	optimal	ADJ
ajst-29869	32	10	feature	feature	NOUN
ajst-29869	32	11	and	and	CCONJ
ajst-29869	32	12	threshold	threshold	NOUN
ajst-29869	32	13	using	use	VERB
ajst-29869	32	14	selection	selection	NOUN
ajst-29869	32	15	criteria	criterion	NOUN
ajst-29869	32	16	such	such	ADJ
ajst-29869	32	17	as	as	ADP
ajst-29869	32	18	information	information	NOUN
ajst-29869	32	19	gain	gain	NOUN
ajst-29869	32	20	,	,	PUNCT
ajst-29869	32	21	gain	gain	VERB
ajst-29869	32	22	ratio	ratio	NOUN
ajst-29869	32	23	,	,	PUNCT
ajst-29869	32	24	or	or	CCONJ
ajst-29869	32	25	the	the	DET
ajst-29869	32	26	gini	gini	PROPN
ajst-29869	32	27	index	index	PROPN
ajst-29869	32	28	.	.	PUNCT
ajst-29869	33	1	the	the	DET
ajst-29869	33	2	decision	decision	NOUN
ajst-29869	33	3	tree	tree	NOUN
ajst-29869	33	4	construction	construction	NOUN
ajst-29869	33	5	consists	consist	VERB
ajst-29869	33	6	of	of	ADP
ajst-29869	33	7	two	two	NUM
ajst-29869	33	8	main	main	ADJ
ajst-29869	33	9	phases	phase	NOUN
ajst-29869	33	10	:	:	PUNCT
ajst-29869	33	11	tree	tree	NOUN
ajst-29869	33	12	growth	growth	NOUN
ajst-29869	33	13	and	and	CCONJ
ajst-29869	33	14	pruning	pruning	NOUN
ajst-29869	33	15	.	.	PUNCT
ajst-29869	34	1	the	the	DET
ajst-29869	34	2	tree	tree	NOUN
ajst-29869	34	3	growth	growth	NOUN
ajst-29869	34	4	phase	phase	NOUN
ajst-29869	34	5	recursively	recursively	ADV
ajst-29869	34	6	146	146	NUM
ajst-29869	34	7	partitions	partition	NOUN
ajst-29869	34	8	the	the	DET
ajst-29869	34	9	data	datum	NOUN
ajst-29869	34	10	until	until	SCONJ
ajst-29869	34	11	all	all	DET
ajst-29869	34	12	samples	sample	NOUN
ajst-29869	34	13	are	be	AUX
ajst-29869	34	14	classified	classify	VERB
ajst-29869	34	15	or	or	CCONJ
ajst-29869	34	16	further	further	ADJ
ajst-29869	34	17	division	division	NOUN
ajst-29869	34	18	is	be	AUX
ajst-29869	34	19	not	not	PART
ajst-29869	34	20	possible	possible	ADJ
ajst-29869	34	21	.	.	PUNCT
ajst-29869	35	1	the	the	DET
ajst-29869	35	2	pruning	prune	VERB
ajst-29869	35	3	phase	phase	NOUN
ajst-29869	35	4	prevents	prevent	VERB
ajst-29869	35	5	overfitting	overfitte	VERB
ajst-29869	35	6	by	by	ADP
ajst-29869	35	7	simplifying	simplify	VERB
ajst-29869	35	8	the	the	DET
ajst-29869	35	9	tree	tree	NOUN
ajst-29869	35	10	structure	structure	NOUN
ajst-29869	35	11	to	to	PART
ajst-29869	35	12	enhance	enhance	VERB
ajst-29869	35	13	generalization	generalization	NOUN
ajst-29869	35	14	performance	performance	NOUN
ajst-29869	35	15	[	[	X
ajst-29869	35	16	9	9	NUM
ajst-29869	35	17	]	]	PUNCT
ajst-29869	35	18	.	.	PUNCT
ajst-29869	36	1	(	(	PUNCT
ajst-29869	36	2	1	1	X
ajst-29869	36	3	)	)	PUNCT
ajst-29869	36	4	tree	tree	NOUN
ajst-29869	36	5	growth	growth	NOUN
ajst-29869	36	6	the	the	DET
ajst-29869	36	7	tree	tree	NOUN
ajst-29869	36	8	growth	growth	NOUN
ajst-29869	36	9	process	process	NOUN
ajst-29869	36	10	recursively	recursively	ADV
ajst-29869	36	11	partitions	partition	VERB
ajst-29869	36	12	the	the	DET
ajst-29869	36	13	dataset	dataset	NOUN
ajst-29869	36	14	until	until	SCONJ
ajst-29869	36	15	either	either	CCONJ
ajst-29869	36	16	all	all	DET
ajst-29869	36	17	samples	sample	NOUN
ajst-29869	36	18	belong	belong	VERB
ajst-29869	36	19	to	to	ADP
ajst-29869	36	20	a	a	DET
ajst-29869	36	21	single	single	ADJ
ajst-29869	36	22	category	category	NOUN
ajst-29869	36	23	or	or	CCONJ
ajst-29869	36	24	a	a	DET
ajst-29869	36	25	predefined	predefine	VERB
ajst-29869	36	26	stopping	stopping	NOUN
ajst-29869	36	27	criterion	criterion	NOUN
ajst-29869	36	28	is	be	AUX
ajst-29869	36	29	met	meet	VERB
ajst-29869	36	30	.	.	PUNCT
ajst-29869	37	1	the	the	DET
ajst-29869	37	2	steps	step	NOUN
ajst-29869	37	3	include	include	VERB
ajst-29869	37	4	:	:	PUNCT
ajst-29869	37	5	1.selecting	1.selecting	NUM
ajst-29869	37	6	the	the	DET
ajst-29869	37	7	optimal	optimal	ADJ
ajst-29869	37	8	feature	feature	NOUN
ajst-29869	37	9	for	for	ADP
ajst-29869	37	10	splitting	splitting	NOUN
ajst-29869	37	11	,	,	PUNCT
ajst-29869	37	12	based	base	VERB
ajst-29869	37	13	on	on	ADP
ajst-29869	37	14	information	information	NOUN
ajst-29869	37	15	gain	gain	NOUN
ajst-29869	37	16	,	,	PUNCT
ajst-29869	37	17	gain	gain	VERB
ajst-29869	37	18	ratio	ratio	NOUN
ajst-29869	37	19	,	,	PUNCT
ajst-29869	37	20	or	or	CCONJ
ajst-29869	37	21	gini	gini	PROPN
ajst-29869	37	22	index	index	NOUN
ajst-29869	37	23	,	,	PUNCT
ajst-29869	37	24	to	to	PART
ajst-29869	37	25	maximize	maximize	VERB
ajst-29869	37	26	subset	subset	ADJ
ajst-29869	37	27	purity	purity	NOUN
ajst-29869	37	28	.	.	PUNCT
ajst-29869	38	1	2.partitioning	2.partitione	VERB
ajst-29869	38	2	the	the	DET
ajst-29869	38	3	dataset	dataset	NOUN
ajst-29869	38	4	into	into	ADP
ajst-29869	38	5	subsets	subset	NOUN
ajst-29869	38	6	according	accord	VERB
ajst-29869	38	7	to	to	ADP
ajst-29869	38	8	the	the	DET
ajst-29869	38	9	chosen	choose	VERB
ajst-29869	38	10	feature	feature	NOUN
ajst-29869	38	11	values	value	NOUN
ajst-29869	38	12	and	and	CCONJ
ajst-29869	38	13	generating	generate	VERB
ajst-29869	38	14	child	child	NOUN
ajst-29869	38	15	nodes	node	NOUN
ajst-29869	38	16	.	.	PUNCT
ajst-29869	39	1	3.recursively	3.recursively	ADV
ajst-29869	39	2	repeating	repeat	VERB
ajst-29869	39	3	the	the	DET
ajst-29869	39	4	feature	feature	NOUN
ajst-29869	39	5	selection	selection	NOUN
ajst-29869	39	6	and	and	CCONJ
ajst-29869	39	7	splitting	splitting	NOUN
ajst-29869	39	8	process	process	NOUN
ajst-29869	39	9	for	for	ADP
ajst-29869	39	10	each	each	DET
ajst-29869	39	11	child	child	NOUN
ajst-29869	39	12	node	node	NOUN
ajst-29869	39	13	until	until	SCONJ
ajst-29869	39	14	a	a	DET
ajst-29869	39	15	stopping	stop	VERB
ajst-29869	39	16	condition	condition	NOUN
ajst-29869	39	17	is	be	AUX
ajst-29869	39	18	reached	reach	VERB
ajst-29869	39	19	,	,	PUNCT
ajst-29869	39	20	such	such	ADJ
ajst-29869	39	21	as	as	ADP
ajst-29869	39	22	a	a	DET
ajst-29869	39	23	homogeneous	homogeneous	ADJ
ajst-29869	39	24	class	class	NOUN
ajst-29869	39	25	distribution	distribution	NOUN
ajst-29869	39	26	or	or	CCONJ
ajst-29869	39	27	a	a	DET
ajst-29869	39	28	minimum	minimum	ADJ
ajst-29869	39	29	number	number	NOUN
ajst-29869	39	30	of	of	ADP
ajst-29869	39	31	samples	sample	NOUN
ajst-29869	39	32	per	per	ADP
ajst-29869	39	33	node	node	NOUN
ajst-29869	39	34	.	.	PUNCT
ajst-29869	40	1	(	(	PUNCT
ajst-29869	40	2	2	2	X
ajst-29869	40	3	)	)	PUNCT
ajst-29869	40	4	tree	tree	NOUN
ajst-29869	40	5	pruning	prune	VERB
ajst-29869	40	6	to	to	PART
ajst-29869	40	7	prevent	prevent	VERB
ajst-29869	40	8	overfitting	overfitting	NOUN
ajst-29869	40	9	,	,	PUNCT
ajst-29869	40	10	pruning	prune	VERB
ajst-29869	40	11	simplifies	simplifie	NOUN
ajst-29869	40	12	the	the	DET
ajst-29869	40	13	tree	tree	NOUN
ajst-29869	40	14	while	while	SCONJ
ajst-29869	40	15	maintaining	maintain	VERB
ajst-29869	40	16	predictive	predictive	ADJ
ajst-29869	40	17	accuracy	accuracy	NOUN
ajst-29869	40	18	.	.	PUNCT
ajst-29869	41	1	the	the	DET
ajst-29869	41	2	steps	step	NOUN
ajst-29869	41	3	include	include	VERB
ajst-29869	41	4	:	:	PUNCT
ajst-29869	41	5	1.setting	1.setting	NUM
ajst-29869	41	6	a	a	DET
ajst-29869	41	7	threshold	threshold	NOUN
ajst-29869	41	8	to	to	PART
ajst-29869	41	9	determine	determine	VERB
ajst-29869	41	10	pruning	pruning	NOUN
ajst-29869	41	11	,	,	PUNCT
ajst-29869	41	12	such	such	ADJ
ajst-29869	41	13	as	as	ADP
ajst-29869	41	14	a	a	DET
ajst-29869	41	15	change	change	NOUN
ajst-29869	41	16	in	in	ADP
ajst-29869	41	17	loss	loss	NOUN
ajst-29869	41	18	function	function	NOUN
ajst-29869	41	19	or	or	CCONJ
ajst-29869	41	20	validation	validation	NOUN
ajst-29869	41	21	performance	performance	NOUN
ajst-29869	41	22	.	.	PUNCT
ajst-29869	42	1	2.starting	2.starte	VERB
ajst-29869	42	2	from	from	ADP
ajst-29869	42	3	leaf	leaf	NOUN
ajst-29869	42	4	nodes	node	NOUN
ajst-29869	42	5	and	and	CCONJ
ajst-29869	42	6	evaluating	evaluate	VERB
ajst-29869	42	7	whether	whether	SCONJ
ajst-29869	42	8	pruning	prune	VERB
ajst-29869	42	9	a	a	DET
ajst-29869	42	10	node	node	NOUN
ajst-29869	42	11	improves	improve	VERB
ajst-29869	42	12	overall	overall	ADJ
ajst-29869	42	13	model	model	NOUN
ajst-29869	42	14	performance	performance	NOUN
ajst-29869	42	15	.	.	PUNCT
ajst-29869	43	1	if	if	SCONJ
ajst-29869	43	2	merging	merge	VERB
ajst-29869	43	3	the	the	DET
ajst-29869	43	4	node	node	NOUN
ajst-29869	43	5	and	and	CCONJ
ajst-29869	43	6	its	its	PRON
ajst-29869	43	7	children	child	NOUN
ajst-29869	43	8	reduces	reduce	VERB
ajst-29869	43	9	loss	loss	NOUN
ajst-29869	43	10	or	or	CCONJ
ajst-29869	43	11	improves	improve	VERB
ajst-29869	43	12	performance	performance	NOUN
ajst-29869	43	13	,	,	PUNCT
ajst-29869	43	14	pruning	prune	VERB
ajst-29869	43	15	is	be	AUX
ajst-29869	43	16	applied	apply	VERB
ajst-29869	43	17	.	.	PUNCT
ajst-29869	44	1	3.repeating	3.repeating	NUM
ajst-29869	44	2	the	the	DET
ajst-29869	44	3	process	process	NOUN
ajst-29869	44	4	until	until	SCONJ
ajst-29869	44	5	no	no	PRON
ajst-29869	44	6	further	far	ADV
ajst-29869	44	7	pruning	pruning	NOUN
ajst-29869	44	8	improves	improve	VERB
ajst-29869	44	9	performance	performance	NOUN
ajst-29869	44	10	,	,	PUNCT
ajst-29869	44	11	resulting	result	VERB
ajst-29869	44	12	in	in	ADP
ajst-29869	44	13	a	a	DET
ajst-29869	44	14	more	more	ADV
ajst-29869	44	15	generalizable	generalizable	ADJ
ajst-29869	44	16	tree	tree	NOUN
ajst-29869	44	17	.	.	PUNCT
ajst-29869	45	1	2.1.2	2.1.2	X
ajst-29869	45	2	.	.	PUNCT
ajst-29869	45	3	random	random	ADJ
ajst-29869	45	4	forest	forest	NOUN
ajst-29869	45	5	random	random	ADJ
ajst-29869	45	6	forest	forest	NOUN
ajst-29869	45	7	is	be	AUX
ajst-29869	45	8	an	an	DET
ajst-29869	45	9	ensemble	ensemble	ADJ
ajst-29869	45	10	learning	learning	NOUN
ajst-29869	45	11	algorithm	algorithm	NOUN
ajst-29869	45	12	composed	compose	VERB
ajst-29869	45	13	of	of	ADP
ajst-29869	45	14	multiple	multiple	ADJ
ajst-29869	45	15	decision	decision	NOUN
ajst-29869	45	16	trees	tree	NOUN
ajst-29869	45	17	.	.	PUNCT
ajst-29869	46	1	by	by	ADP
ajst-29869	46	2	introducing	introduce	VERB
ajst-29869	46	3	randomness	randomness	NOUN
ajst-29869	46	4	in	in	ADP
ajst-29869	46	5	both	both	PRON
ajst-29869	46	6	data	datum	NOUN
ajst-29869	46	7	sampling	sampling	NOUN
ajst-29869	46	8	and	and	CCONJ
ajst-29869	46	9	feature	feature	NOUN
ajst-29869	46	10	selection	selection	NOUN
ajst-29869	46	11	,	,	PUNCT
ajst-29869	46	12	it	it	PRON
ajst-29869	46	13	reduces	reduce	VERB
ajst-29869	46	14	overfitting	overfitting	NOUN
ajst-29869	46	15	and	and	CCONJ
ajst-29869	46	16	variance	variance	NOUN
ajst-29869	46	17	,	,	PUNCT
ajst-29869	46	18	enhancing	enhance	VERB
ajst-29869	46	19	model	model	NOUN
ajst-29869	46	20	robustness	robustness	NOUN
ajst-29869	46	21	.	.	PUNCT
ajst-29869	47	1	the	the	DET
ajst-29869	47	2	key	key	ADJ
ajst-29869	47	3	idea	idea	NOUN
ajst-29869	47	4	is	be	AUX
ajst-29869	47	5	to	to	PART
ajst-29869	47	6	train	train	VERB
ajst-29869	47	7	multiple	multiple	ADJ
ajst-29869	47	8	trees	tree	NOUN
ajst-29869	47	9	on	on	ADP
ajst-29869	47	10	different	different	ADJ
ajst-29869	47	11	data	datum	NOUN
ajst-29869	47	12	subsets	subset	NOUN
ajst-29869	47	13	and	and	CCONJ
ajst-29869	47	14	aggregate	aggregate	VERB
ajst-29869	47	15	their	their	PRON
ajst-29869	47	16	predictions	prediction	NOUN
ajst-29869	47	17	through	through	ADP
ajst-29869	47	18	majority	majority	NOUN
ajst-29869	47	19	voting	voting	NOUN
ajst-29869	47	20	(	(	PUNCT
ajst-29869	47	21	classification	classification	NOUN
ajst-29869	47	22	)	)	PUNCT
ajst-29869	47	23	or	or	CCONJ
ajst-29869	47	24	averaging	average	VERB
ajst-29869	47	25	(	(	PUNCT
ajst-29869	47	26	regression	regression	NOUN
ajst-29869	47	27	)	)	PUNCT
ajst-29869	48	1	[	[	X
ajst-29869	48	2	10	10	NUM
ajst-29869	48	3	-	-	SYM
ajst-29869	48	4	11	11	NUM
ajst-29869	48	5	]	]	PUNCT
ajst-29869	48	6	.	.	PUNCT
ajst-29869	49	1	2.1	2.1	NUM
ajst-29869	49	2	construction	construction	NOUN
ajst-29869	49	3	of	of	ADP
ajst-29869	49	4	random	random	ADJ
ajst-29869	49	5	forest	forest	NOUN
ajst-29869	49	6	the	the	DET
ajst-29869	49	7	random	random	ADJ
ajst-29869	49	8	forest	forest	NOUN
ajst-29869	49	9	algorithm	algorithm	NOUN
ajst-29869	49	10	consists	consist	VERB
ajst-29869	49	11	of	of	ADP
ajst-29869	49	12	the	the	DET
ajst-29869	49	13	following	follow	VERB
ajst-29869	49	14	steps	step	NOUN
ajst-29869	49	15	:	:	PUNCT
ajst-29869	49	16	1.generating	1.generating	NUM
ajst-29869	49	17	multiple	multiple	ADJ
ajst-29869	49	18	bootstrap	bootstrap	NOUN
ajst-29869	49	19	samples	sample	NOUN
ajst-29869	49	20	from	from	ADP
ajst-29869	49	21	the	the	DET
ajst-29869	49	22	original	original	ADJ
ajst-29869	49	23	dataset	dataset	NOUN
ajst-29869	49	24	using	use	VERB
ajst-29869	49	25	sampling	sample	VERB
ajst-29869	49	26	with	with	ADP
ajst-29869	49	27	replacement	replacement	NOUN
ajst-29869	49	28	.	.	PUNCT
ajst-29869	50	1	each	each	DET
ajst-29869	50	2	sample	sample	NOUN
ajst-29869	50	3	is	be	AUX
ajst-29869	50	4	used	use	VERB
ajst-29869	50	5	to	to	PART
ajst-29869	50	6	train	train	VERB
ajst-29869	50	7	an	an	DET
ajst-29869	50	8	individual	individual	ADJ
ajst-29869	50	9	decision	decision	NOUN
ajst-29869	50	10	tree	tree	NOUN
ajst-29869	50	11	.	.	PUNCT
ajst-29869	51	1	2.for	2.for	ADP
ajst-29869	51	2	each	each	DET
ajst-29869	51	3	decision	decision	NOUN
ajst-29869	51	4	tree	tree	NOUN
ajst-29869	51	5	,	,	PUNCT
ajst-29869	51	6	randomly	randomly	ADV
ajst-29869	51	7	selecting	select	VERB
ajst-29869	51	8	a	a	DET
ajst-29869	51	9	subset	subset	NOUN
ajst-29869	51	10	of	of	ADP
ajst-29869	51	11	features	feature	NOUN
ajst-29869	51	12	instead	instead	ADV
ajst-29869	51	13	of	of	ADP
ajst-29869	51	14	considering	consider	VERB
ajst-29869	51	15	all	all	DET
ajst-29869	51	16	features	feature	NOUN
ajst-29869	51	17	,	,	PUNCT
ajst-29869	51	18	reducing	reduce	VERB
ajst-29869	51	19	feature	feature	NOUN
ajst-29869	51	20	dominance	dominance	NOUN
ajst-29869	51	21	and	and	CCONJ
ajst-29869	51	22	ensuring	ensure	VERB
ajst-29869	51	23	diversity	diversity	NOUN
ajst-29869	51	24	.	.	PUNCT
ajst-29869	52	1	3.constructing	3.constructing	NUM
ajst-29869	52	2	decision	decision	NOUN
ajst-29869	52	3	trees	tree	NOUN
ajst-29869	52	4	independently	independently	ADV
ajst-29869	52	5	using	use	VERB
ajst-29869	52	6	the	the	DET
ajst-29869	52	7	selected	select	VERB
ajst-29869	52	8	samples	sample	NOUN
ajst-29869	52	9	and	and	CCONJ
ajst-29869	52	10	features	feature	NOUN
ajst-29869	52	11	.	.	PUNCT
ajst-29869	53	1	4.aggregating	4.aggregating	NUM
ajst-29869	53	2	predictions	prediction	NOUN
ajst-29869	53	3	from	from	ADP
ajst-29869	53	4	all	all	DET
ajst-29869	53	5	trees	tree	NOUN
ajst-29869	53	6	via	via	ADP
ajst-29869	53	7	majority	majority	NOUN
ajst-29869	53	8	voting	voting	NOUN
ajst-29869	53	9	for	for	ADP
ajst-29869	53	10	classification	classification	NOUN
ajst-29869	53	11	tasks	task	NOUN
ajst-29869	53	12	or	or	CCONJ
ajst-29869	53	13	averaging	average	VERB
ajst-29869	53	14	for	for	ADP
ajst-29869	53	15	regression	regression	NOUN
ajst-29869	53	16	tasks	task	NOUN
ajst-29869	53	17	.	.	PUNCT
ajst-29869	54	1	random	random	ADJ
ajst-29869	54	2	forest	forest	NOUN
ajst-29869	54	3	is	be	AUX
ajst-29869	54	4	effective	effective	ADJ
ajst-29869	54	5	in	in	ADP
ajst-29869	54	6	handling	handle	VERB
ajst-29869	54	7	high	high	ADJ
ajst-29869	54	8	-	-	PUNCT
ajst-29869	54	9	dimensional	dimensional	ADJ
ajst-29869	54	10	data	datum	NOUN
ajst-29869	54	11	,	,	PUNCT
ajst-29869	54	12	mitigating	mitigate	VERB
ajst-29869	54	13	noise	noise	NOUN
ajst-29869	54	14	sensitivity	sensitivity	NOUN
ajst-29869	54	15	,	,	PUNCT
ajst-29869	54	16	and	and	CCONJ
ajst-29869	54	17	dealing	deal	VERB
ajst-29869	54	18	with	with	ADP
ajst-29869	54	19	missing	miss	VERB
ajst-29869	54	20	values	value	NOUN
ajst-29869	54	21	.	.	PUNCT
ajst-29869	55	1	however	however	ADV
ajst-29869	55	2	,	,	PUNCT
ajst-29869	55	3	it	it	PRON
ajst-29869	55	4	has	have	VERB
ajst-29869	55	5	drawbacks	drawback	NOUN
ajst-29869	55	6	such	such	ADJ
ajst-29869	55	7	as	as	ADP
ajst-29869	55	8	increased	increase	VERB
ajst-29869	55	9	computational	computational	ADJ
ajst-29869	55	10	complexity	complexity	NOUN
ajst-29869	55	11	and	and	CCONJ
ajst-29869	55	12	reduced	reduce	VERB
ajst-29869	55	13	interpretability	interpretability	NOUN
ajst-29869	55	14	.	.	PUNCT
ajst-29869	56	1	the	the	DET
ajst-29869	56	2	number	number	NOUN
ajst-29869	56	3	of	of	ADP
ajst-29869	56	4	trees	tree	NOUN
ajst-29869	56	5	is	be	AUX
ajst-29869	56	6	a	a	DET
ajst-29869	56	7	crucial	crucial	ADJ
ajst-29869	56	8	hyperparameter	hyperparameter	NOUN
ajst-29869	56	9	affecting	affect	VERB
ajst-29869	56	10	model	model	NOUN
ajst-29869	56	11	performance	performance	NOUN
ajst-29869	56	12	.	.	PUNCT
ajst-29869	57	1	too	too	ADV
ajst-29869	57	2	few	few	ADJ
ajst-29869	57	3	trees	tree	NOUN
ajst-29869	57	4	lead	lead	VERB
ajst-29869	57	5	to	to	ADP
ajst-29869	57	6	instability	instability	NOUN
ajst-29869	57	7	,	,	PUNCT
ajst-29869	57	8	while	while	SCONJ
ajst-29869	57	9	too	too	ADV
ajst-29869	57	10	many	many	ADJ
ajst-29869	57	11	trees	tree	NOUN
ajst-29869	57	12	increase	increase	VERB
ajst-29869	57	13	computational	computational	ADJ
ajst-29869	57	14	cost	cost	NOUN
ajst-29869	57	15	.	.	PUNCT
ajst-29869	58	1	to	to	PART
ajst-29869	58	2	determine	determine	VERB
ajst-29869	58	3	the	the	DET
ajst-29869	58	4	optimal	optimal	ADJ
ajst-29869	58	5	number	number	NOUN
ajst-29869	58	6	of	of	ADP
ajst-29869	58	7	trees	tree	NOUN
ajst-29869	58	8	,	,	PUNCT
ajst-29869	58	9	experiments	experiment	NOUN
ajst-29869	58	10	were	be	AUX
ajst-29869	58	11	conducted	conduct	VERB
ajst-29869	58	12	by	by	ADP
ajst-29869	58	13	testing	test	VERB
ajst-29869	58	14	tree	tree	NOUN
ajst-29869	58	15	counts	count	NOUN
ajst-29869	58	16	from	from	ADP
ajst-29869	58	17	100	100	NUM
ajst-29869	58	18	to	to	ADP
ajst-29869	58	19	1000	1000	NUM
ajst-29869	58	20	(	(	PUNCT
ajst-29869	58	21	step	step	NOUN
ajst-29869	58	22	size	size	NOUN
ajst-29869	58	23	=	=	NOUN
ajst-29869	58	24	100	100	NUM
ajst-29869	58	25	)	)	PUNCT
ajst-29869	58	26	and	and	CCONJ
ajst-29869	58	27	evaluating	evaluate	VERB
ajst-29869	58	28	classification	classification	NOUN
ajst-29869	58	29	accuracy	accuracy	NOUN
ajst-29869	58	30	.	.	PUNCT
ajst-29869	59	1	as	as	SCONJ
ajst-29869	59	2	shown	show	VERB
ajst-29869	59	3	in	in	ADP
ajst-29869	59	4	figure	figure	NOUN
ajst-29869	59	5	1	1	NUM
ajst-29869	59	6	,	,	PUNCT
ajst-29869	59	7	the	the	DET
ajst-29869	59	8	feature	feature	NOUN
ajst-29869	59	9	groups	group	NOUN
ajst-29869	59	10	tested	test	VERB
ajst-29869	59	11	include	include	VERB
ajst-29869	59	12	spectral	spectral	ADJ
ajst-29869	59	13	(	(	PUNCT
ajst-29869	59	14	spectral	spectral	ADJ
ajst-29869	59	15	)	)	PUNCT
ajst-29869	59	16	,	,	PUNCT
ajst-29869	59	17	texture	texture	NOUN
ajst-29869	59	18	(	(	PUNCT
ajst-29869	59	19	texture	texture	NOUN
ajst-29869	59	20	)	)	PUNCT
ajst-29869	59	21	,	,	PUNCT
ajst-29869	59	22	terrain	terrain	NOUN
ajst-29869	59	23	(	(	PUNCT
ajst-29869	59	24	terrain	terrain	NOUN
ajst-29869	59	25	)	)	PUNCT
ajst-29869	59	26	,	,	PUNCT
ajst-29869	59	27	polarization	polarization	NOUN
ajst-29869	59	28	(	(	PUNCT
ajst-29869	59	29	polarization	polarization	NOUN
ajst-29869	59	30	)	)	PUNCT
ajst-29869	59	31	,	,	PUNCT
ajst-29869	59	32	and	and	CCONJ
ajst-29869	59	33	their	their	PRON
ajst-29869	59	34	combinations	combination	NOUN
ajst-29869	59	35	(	(	PUNCT
ajst-29869	59	36	st	st	PROPN
ajst-29869	59	37	,	,	PUNCT
ajst-29869	59	38	stt	stt	PROPN
ajst-29869	59	39	,	,	PUNCT
ajst-29869	59	40	sttp	sttp	ADJ
ajst-29869	59	41	)	)	PUNCT
ajst-29869	59	42	.	.	PUNCT
ajst-29869	60	1	results	result	NOUN
ajst-29869	60	2	indicate	indicate	VERB
ajst-29869	60	3	that	that	SCONJ
ajst-29869	60	4	increasing	increase	VERB
ajst-29869	60	5	tree	tree	NOUN
ajst-29869	60	6	count	count	NOUN
ajst-29869	60	7	reduces	reduce	VERB
ajst-29869	60	8	error	error	NOUN
ajst-29869	60	9	,	,	PUNCT
ajst-29869	60	10	but	but	CCONJ
ajst-29869	60	11	computational	computational	ADJ
ajst-29869	60	12	efficiency	efficiency	NOUN
ajst-29869	60	13	decreases	decrease	VERB
ajst-29869	60	14	.	.	PUNCT
ajst-29869	61	1	accuracy	accuracy	NOUN
ajst-29869	61	2	stabilizes	stabilize	VERB
ajst-29869	61	3	at	at	ADP
ajst-29869	61	4	600	600	NUM
ajst-29869	61	5	trees	tree	NOUN
ajst-29869	61	6	,	,	PUNCT
ajst-29869	61	7	making	make	VERB
ajst-29869	61	8	it	it	PRON
ajst-29869	61	9	the	the	DET
ajst-29869	61	10	optimal	optimal	ADJ
ajst-29869	61	11	choice	choice	NOUN
ajst-29869	61	12	balancing	balance	VERB
ajst-29869	61	13	performance	performance	NOUN
ajst-29869	61	14	and	and	CCONJ
ajst-29869	61	15	efficiency	efficiency	NOUN
ajst-29869	61	16	.	.	PUNCT
ajst-29869	62	1	this	this	DET
ajst-29869	62	2	study	study	NOUN
ajst-29869	62	3	employs	employ	VERB
ajst-29869	62	4	random	random	ADJ
ajst-29869	62	5	forest	forest	NOUN
ajst-29869	62	6	for	for	ADP
ajst-29869	62	7	feature	feature	NOUN
ajst-29869	62	8	selection	selection	NOUN
ajst-29869	62	9	due	due	ADP
ajst-29869	62	10	to	to	ADP
ajst-29869	62	11	its	its	PRON
ajst-29869	62	12	ability	ability	NOUN
ajst-29869	62	13	to	to	PART
ajst-29869	62	14	evaluate	evaluate	VERB
ajst-29869	62	15	all	all	DET
ajst-29869	62	16	features	feature	NOUN
ajst-29869	62	17	while	while	SCONJ
ajst-29869	62	18	mitigating	mitigate	VERB
ajst-29869	62	19	overfitting	overfitte	VERB
ajst-29869	62	20	risks	risk	NOUN
ajst-29869	62	21	and	and	CCONJ
ajst-29869	62	22	enhancing	enhance	VERB
ajst-29869	62	23	robustness	robustness	NOUN
ajst-29869	62	24	.	.	PUNCT
ajst-29869	63	1	its	its	PRON
ajst-29869	63	2	capability	capability	NOUN
ajst-29869	63	3	to	to	PART
ajst-29869	63	4	handle	handle	VERB
ajst-29869	63	5	high	high	ADJ
ajst-29869	63	6	-	-	PUNCT
ajst-29869	63	7	dimensional	dimensional	ADJ
ajst-29869	63	8	data	datum	NOUN
ajst-29869	63	9	ensures	ensure	VERB
ajst-29869	63	10	reliable	reliable	ADJ
ajst-29869	63	11	feature	feature	NOUN
ajst-29869	63	12	selection	selection	NOUN
ajst-29869	63	13	outcomes	outcome	NOUN
ajst-29869	63	14	.	.	PUNCT
ajst-29869	64	1	figure	figure	VERB
ajst-29869	64	2	1	1	NUM
ajst-29869	64	3	.	.	PUNCT
ajst-29869	64	4	impact	impact	NOUN
ajst-29869	64	5	of	of	ADP
ajst-29869	64	6	number	number	NOUN
ajst-29869	64	7	of	of	ADP
ajst-29869	64	8	decision	decision	NOUN
ajst-29869	64	9	trees	tree	NOUN
ajst-29869	64	10	on	on	ADP
ajst-29869	64	11	recognition	recognition	NOUN
ajst-29869	64	12	accuracy	accuracy	NOUN
ajst-29869	64	13	in	in	ADP
ajst-29869	64	14	the	the	DET
ajst-29869	64	15	feature	feature	NOUN
ajst-29869	64	16	selection	selection	NOUN
ajst-29869	64	17	process	process	NOUN
ajst-29869	64	18	,	,	PUNCT
ajst-29869	64	19	we	we	PRON
ajst-29869	64	20	applied	apply	VERB
ajst-29869	64	21	both	both	DET
ajst-29869	64	22	singlefeature	singlefeature	NOUN
ajst-29869	64	23	algorithms	algorithm	NOUN
ajst-29869	64	24	and	and	CCONJ
ajst-29869	64	25	fused	fuse	VERB
ajst-29869	64	26	-	-	PUNCT
ajst-29869	64	27	feature	feature	NOUN
ajst-29869	64	28	algorithms	algorithm	NOUN
ajst-29869	64	29	to	to	PART
ajst-29869	64	30	extract	extract	VERB
ajst-29869	64	31	features	feature	NOUN
ajst-29869	64	32	from	from	ADP
ajst-29869	64	33	mountainous	mountainous	ADJ
ajst-29869	64	34	rice	rice	NOUN
ajst-29869	64	35	samples	sample	NOUN
ajst-29869	64	36	,	,	PUNCT
ajst-29869	64	37	using	use	VERB
ajst-29869	64	38	random	random	ADJ
ajst-29869	64	39	forests	forest	NOUN
ajst-29869	64	40	as	as	ADP
ajst-29869	64	41	the	the	DET
ajst-29869	64	42	classifier	classifier	NOUN
ajst-29869	64	43	.	.	PUNCT
ajst-29869	65	1	to	to	PART
ajst-29869	65	2	reduce	reduce	VERB
ajst-29869	65	3	errors	error	NOUN
ajst-29869	65	4	introduced	introduce	VERB
ajst-29869	65	5	by	by	ADP
ajst-29869	65	6	random	random	ADJ
ajst-29869	65	7	sampling	sampling	NOUN
ajst-29869	65	8	of	of	ADP
ajst-29869	65	9	samples	sample	NOUN
ajst-29869	65	10	,	,	PUNCT
ajst-29869	65	11	each	each	DET
ajst-29869	65	12	algorithm	algorithm	NOUN
ajst-29869	65	13	's	's	PART
ajst-29869	65	14	computation	computation	NOUN
ajst-29869	65	15	was	be	AUX
ajst-29869	65	16	performed	perform	VERB
ajst-29869	65	17	100	100	NUM
ajst-29869	65	18	times	time	NOUN
ajst-29869	65	19	,	,	PUNCT
ajst-29869	65	20	and	and	CCONJ
ajst-29869	65	21	the	the	DET
ajst-29869	65	22	average	average	ADJ
ajst-29869	65	23	value	value	NOUN
ajst-29869	65	24	was	be	AUX
ajst-29869	65	25	taken	take	VERB
ajst-29869	65	26	to	to	PART
ajst-29869	65	27	achieve	achieve	VERB
ajst-29869	65	28	more	more	ADV
ajst-29869	65	29	stable	stable	ADJ
ajst-29869	65	30	rice	rice	NOUN
ajst-29869	65	31	recognition	recognition	NOUN
ajst-29869	65	32	results	result	VERB
ajst-29869	65	33	.	.	PUNCT
ajst-29869	66	1	the	the	DET
ajst-29869	66	2	final	final	ADJ
ajst-29869	66	3	computational	computational	ADJ
ajst-29869	66	4	results	result	NOUN
ajst-29869	66	5	are	be	AUX
ajst-29869	66	6	shown	show	VERB
ajst-29869	66	7	in	in	ADP
ajst-29869	66	8	table	table	NOUN
ajst-29869	66	9	2	2	NUM
ajst-29869	66	10	.	.	PUNCT
ajst-29869	66	11	from	from	ADP
ajst-29869	66	12	the	the	DET
ajst-29869	66	13	analysis	analysis	NOUN
ajst-29869	66	14	in	in	ADP
ajst-29869	66	15	table	table	NOUN
ajst-29869	66	16	2	2	NUM
ajst-29869	66	17	,	,	PUNCT
ajst-29869	66	18	it	it	PRON
ajst-29869	66	19	can	can	AUX
ajst-29869	66	20	be	be	AUX
ajst-29869	66	21	seen	see	VERB
ajst-29869	66	22	that	that	SCONJ
ajst-29869	66	23	the	the	DET
ajst-29869	66	24	recognition	recognition	NOUN
ajst-29869	66	25	rate	rate	NOUN
ajst-29869	66	26	of	of	ADP
ajst-29869	66	27	feature	feature	NOUN
ajst-29869	66	28	combinations	combination	NOUN
ajst-29869	66	29	is	be	AUX
ajst-29869	66	30	significantly	significantly	ADV
ajst-29869	66	31	better	well	ADJ
ajst-29869	66	32	than	than	ADP
ajst-29869	66	33	that	that	PRON
ajst-29869	66	34	of	of	ADP
ajst-29869	66	35	single	single	ADJ
ajst-29869	66	36	-	-	PUNCT
ajst-29869	66	37	feature	feature	NOUN
ajst-29869	66	38	algorithms	algorithm	NOUN
ajst-29869	66	39	,	,	PUNCT
ajst-29869	66	40	and	and	CCONJ
ajst-29869	66	41	its	its	PRON
ajst-29869	66	42	recognition	recognition	NOUN
ajst-29869	66	43	accuracy	accuracy	NOUN
ajst-29869	66	44	remains	remain	VERB
ajst-29869	66	45	stable	stable	ADJ
ajst-29869	66	46	above	above	ADP
ajst-29869	66	47	90	90	NUM
ajst-29869	66	48	%	%	NOUN
ajst-29869	66	49	.	.	PUNCT
ajst-29869	67	1	this	this	DET
ajst-29869	67	2	result	result	NOUN
ajst-29869	67	3	suggests	suggest	VERB
ajst-29869	67	4	that	that	SCONJ
ajst-29869	67	5	,	,	PUNCT
ajst-29869	67	6	in	in	ADP
ajst-29869	67	7	the	the	DET
ajst-29869	67	8	task	task	NOUN
ajst-29869	67	9	of	of	ADP
ajst-29869	67	10	rice	rice	NOUN
ajst-29869	67	11	extraction	extraction	NOUN
ajst-29869	67	12	in	in	ADP
ajst-29869	67	13	mountainous	mountainous	ADJ
ajst-29869	67	14	areas	area	NOUN
ajst-29869	67	15	,	,	PUNCT
ajst-29869	67	16	feature	feature	NOUN
ajst-29869	67	17	combinations	combination	NOUN
ajst-29869	67	18	can	can	AUX
ajst-29869	67	19	enhance	enhance	VERB
ajst-29869	67	20	recognition	recognition	NOUN
ajst-29869	67	21	performance	performance	NOUN
ajst-29869	67	22	and	and	CCONJ
ajst-29869	67	23	stability	stability	NOUN
ajst-29869	67	24	compared	compare	VERB
ajst-29869	67	25	to	to	ADP
ajst-29869	67	26	single	single	ADJ
ajst-29869	67	27	features	feature	NOUN
ajst-29869	67	28	.	.	PUNCT
ajst-29869	68	1	therefore	therefore	ADV
ajst-29869	68	2	,	,	PUNCT
ajst-29869	68	3	to	to	PART
ajst-29869	68	4	further	far	ADV
ajst-29869	68	5	optimize	optimize	VERB
ajst-29869	68	6	the	the	DET
ajst-29869	68	7	feature	feature	NOUN
ajst-29869	68	8	selection	selection	NOUN
ajst-29869	68	9	process	process	NOUN
ajst-29869	68	10	,	,	PUNCT
ajst-29869	68	11	this	this	DET
ajst-29869	68	12	paper	paper	NOUN
ajst-29869	68	13	will	will	AUX
ajst-29869	68	14	use	use	VERB
ajst-29869	68	15	the	the	DET
ajst-29869	68	16	feature	feature	NOUN
ajst-29869	68	17	set	set	NOUN
ajst-29869	68	18	obtained	obtain	VERB
ajst-29869	68	19	from	from	ADP
ajst-29869	68	20	the	the	DET
ajst-29869	68	21	sttp	sttp	ADJ
ajst-29869	68	22	feature	feature	NOUN
ajst-29869	68	23	combination	combination	NOUN
ajst-29869	68	24	as	as	ADP
ajst-29869	68	25	the	the	DET
ajst-29869	68	26	initial	initial	ADJ
ajst-29869	68	27	feature	feature	NOUN
ajst-29869	68	28	set	set	VERB
ajst-29869	68	29	for	for	ADP
ajst-29869	68	30	subsequent	subsequent	ADJ
ajst-29869	68	31	feature	feature	NOUN
ajst-29869	68	32	selection	selection	NOUN
ajst-29869	68	33	.	.	PUNCT
ajst-29869	69	1	table	table	NOUN
ajst-29869	69	2	1	1	NUM
ajst-29869	69	3	.	.	PUNCT
ajst-29869	69	4	result	result	VERB
ajst-29869	69	5	statistical	statistical	ADJ
ajst-29869	69	6	analysis	analysis	NOUN
ajst-29869	69	7	feature	feature	NOUN
ajst-29869	69	8	set	set	VERB
ajst-29869	69	9	number	number	NOUN
ajst-29869	69	10	of	of	ADP
ajst-29869	69	11	features	feature	NOUN
ajst-29869	69	12	recognition	recognition	NOUN
ajst-29869	69	13	accuracy	accuracy	NOUN
ajst-29869	69	14	(	(	PUNCT
ajst-29869	69	15	%	%	NOUN
ajst-29869	69	16	)	)	PUNCT
ajst-29869	69	17	time	time	NOUN
ajst-29869	69	18	(	(	PUNCT
ajst-29869	69	19	min	min	NOUN
ajst-29869	69	20	)	)	PUNCT
ajst-29869	69	21	spectral	spectral	ADJ
ajst-29869	69	22	26	26	NUM
ajst-29869	69	23	92.5	92.5	NUM
ajst-29869	69	24	1286.3	1286.3	NUM
ajst-29869	69	25	texture	texture	NOUN
ajst-29869	69	26	21	21	NUM
ajst-29869	69	27	91.1	91.1	NUM
ajst-29869	69	28	1109.9	1109.9	NUM
ajst-29869	69	29	terrain	terrain	NOUN
ajst-29869	69	30	4	4	NUM
ajst-29869	69	31	84.3	84.3	NUM
ajst-29869	69	32	201.5	201.5	NUM
ajst-29869	69	33	polarization	polarization	NOUN
ajst-29869	69	34	4	4	NUM
ajst-29869	69	35	85.5	85.5	NUM
ajst-29869	69	36	231.3	231.3	NUM
ajst-29869	69	37	st	st	PROPN
ajst-29869	69	38	47	47	NUM
ajst-29869	69	39	93.2	93.2	NUM
ajst-29869	69	40	1327.6	1327.6	NUM
ajst-29869	69	41	stt	stt	NOUN
ajst-29869	69	42	51	51	NUM
ajst-29869	69	43	92.8	92.8	NUM
ajst-29869	69	44	1335.1	1335.1	NUM
ajst-29869	69	45	sttp	sttp	ADV
ajst-29869	69	46	55	55	NUM
ajst-29869	69	47	93.1	93.1	NUM
ajst-29869	69	48	1341.9	1341.9	NUM
ajst-29869	69	49	2.1.3	2.1.3	NUM
ajst-29869	69	50	.	.	PUNCT
ajst-29869	69	51	mutual	mutual	ADJ
ajst-29869	69	52	information	information	NOUN
ajst-29869	69	53	algorithm	algorithm	NOUN
ajst-29869	69	54	the	the	DET
ajst-29869	69	55	mutual	mutual	ADJ
ajst-29869	69	56	information	information	NOUN
ajst-29869	69	57	(	(	PUNCT
ajst-29869	69	58	mi	mi	NOUN
ajst-29869	69	59	)	)	PUNCT
ajst-29869	69	60	algorithm	algorithm	NOUN
ajst-29869	69	61	is	be	AUX
ajst-29869	69	62	a	a	DET
ajst-29869	69	63	feature	feature	NOUN
ajst-29869	69	64	147	147	NUM
ajst-29869	69	65	selection	selection	NOUN
ajst-29869	69	66	method	method	NOUN
ajst-29869	69	67	based	base	VERB
ajst-29869	69	68	on	on	ADP
ajst-29869	69	69	information	information	NOUN
ajst-29869	69	70	theory	theory	NOUN
ajst-29869	69	71	,	,	PUNCT
ajst-29869	69	72	used	use	VERB
ajst-29869	69	73	to	to	PART
ajst-29869	69	74	measure	measure	VERB
ajst-29869	69	75	the	the	DET
ajst-29869	69	76	degree	degree	NOUN
ajst-29869	69	77	of	of	ADP
ajst-29869	69	78	correlation	correlation	NOUN
ajst-29869	69	79	between	between	ADP
ajst-29869	69	80	two	two	NUM
ajst-29869	69	81	variables	variable	NOUN
ajst-29869	69	82	.	.	PUNCT
ajst-29869	70	1	it	it	PRON
ajst-29869	70	2	is	be	AUX
ajst-29869	70	3	commonly	commonly	ADV
ajst-29869	70	4	applied	apply	VERB
ajst-29869	70	5	in	in	ADP
ajst-29869	70	6	fields	field	NOUN
ajst-29869	70	7	such	such	ADJ
ajst-29869	70	8	as	as	ADP
ajst-29869	70	9	feature	feature	NOUN
ajst-29869	70	10	selection	selection	NOUN
ajst-29869	70	11	and	and	CCONJ
ajst-29869	70	12	data	datum	NOUN
ajst-29869	70	13	analysis	analysis	NOUN
ajst-29869	70	14	[	[	X
ajst-29869	70	15	12	12	NUM
ajst-29869	70	16	]	]	PUNCT
ajst-29869	70	17	.	.	PUNCT
ajst-29869	71	1	the	the	DET
ajst-29869	71	2	principle	principle	NOUN
ajst-29869	71	3	of	of	ADP
ajst-29869	71	4	mi	mi	PROPN
ajst-29869	71	5	is	be	AUX
ajst-29869	71	6	to	to	PART
ajst-29869	71	7	calculate	calculate	VERB
ajst-29869	71	8	the	the	DET
ajst-29869	71	9	deviation	deviation	NOUN
ajst-29869	71	10	between	between	ADP
ajst-29869	71	11	the	the	DET
ajst-29869	71	12	joint	joint	ADJ
ajst-29869	71	13	distribution	distribution	NOUN
ajst-29869	71	14	and	and	CCONJ
ajst-29869	71	15	the	the	DET
ajst-29869	71	16	individual	individual	ADJ
ajst-29869	71	17	marginal	marginal	ADJ
ajst-29869	71	18	distributions	distribution	NOUN
ajst-29869	71	19	of	of	ADP
ajst-29869	71	20	two	two	NUM
ajst-29869	71	21	random	random	ADJ
ajst-29869	71	22	variables	variable	NOUN
ajst-29869	71	23	,	,	PUNCT
ajst-29869	71	24	quantifying	quantify	VERB
ajst-29869	71	25	their	their	PRON
ajst-29869	71	26	correlation	correlation	NOUN
ajst-29869	71	27	.	.	PUNCT
ajst-29869	72	1	for	for	ADP
ajst-29869	72	2	two	two	NUM
ajst-29869	72	3	variables	variable	NOUN
ajst-29869	72	4	x	x	PUNCT
ajst-29869	72	5	and	and	CCONJ
ajst-29869	72	6	y	y	PROPN
ajst-29869	72	7	,	,	PUNCT
ajst-29869	72	8	the	the	DET
ajst-29869	72	9	mutual	mutual	ADJ
ajst-29869	72	10	information	information	NOUN
ajst-29869	72	11	value	value	NOUN
ajst-29869	72	12	can	can	AUX
ajst-29869	72	13	be	be	AUX
ajst-29869	72	14	calculated	calculate	VERB
ajst-29869	72	15	using	use	VERB
ajst-29869	72	16	the	the	DET
ajst-29869	72	17	following	follow	VERB
ajst-29869	72	18	formula	formula	NOUN
ajst-29869	72	19	(	(	PUNCT
ajst-29869	72	20	1	1	NUM
ajst-29869	72	21	):	):	PUNCT
ajst-29869	72	22	mi	mi	PROPN
ajst-29869	72	23	x;y	x;y	PROPN
ajst-29869	72	24	=	=	PUNCT
ajst-29869	72	25	p(x	p(x	PROPN
ajst-29869	72	26	,	,	PUNCT
ajst-29869	72	27	y	y	NOUN
ajst-29869	72	28	)	)	PUNCT
ajst-29869	72	29	log	log	NOUN
ajst-29869	72	30	p(x	p(x	PROPN
ajst-29869	72	31	,	,	PUNCT
ajst-29869	72	32	y	y	NOUN
ajst-29869	72	33	)	)	PUNCT
ajst-29869	72	34	p(x)p(y	p(x)p(y	NOUN
ajst-29869	72	35	)	)	PUNCT
ajst-29869	72	36	y∈yx∈x	y∈yx∈x	PROPN
ajst-29869	72	37	1	1	NUM
ajst-29869	72	38	where	where	SCONJ
ajst-29869	72	39	p(x	p(x	PROPN
ajst-29869	72	40	,	,	PUNCT
ajst-29869	72	41	y	y	PROPN
ajst-29869	72	42	)	)	PUNCT
ajst-29869	72	43	represents	represent	VERB
ajst-29869	72	44	the	the	DET
ajst-29869	72	45	joint	joint	ADJ
ajst-29869	72	46	probability	probability	NOUN
ajst-29869	72	47	distribution	distribution	NOUN
ajst-29869	72	48	of	of	ADP
ajst-29869	72	49	x	x	PROPN
ajst-29869	72	50	andy	andy	PROPN
ajst-29869	72	51	;	;	PUNCT
ajst-29869	72	52	p(x	p(x	PROPN
ajst-29869	72	53	)	)	PUNCT
ajst-29869	72	54	and	and	CCONJ
ajst-29869	72	55	p(y	p(y	NOUN
ajst-29869	72	56	)	)	PUNCT
ajst-29869	72	57	represent	represent	VERB
ajst-29869	72	58	the	the	DET
ajst-29869	72	59	marginal	marginal	ADJ
ajst-29869	72	60	probability	probability	NOUN
ajst-29869	72	61	distributions	distribution	NOUN
ajst-29869	72	62	of	of	ADP
ajst-29869	72	63	x	x	X
ajst-29869	72	64	and	and	CCONJ
ajst-29869	72	65	y	y	PROPN
ajst-29869	72	66	,	,	PUNCT
ajst-29869	72	67	respectively	respectively	ADV
ajst-29869	72	68	;	;	PUNCT
ajst-29869	72	69	and	and	CCONJ
ajst-29869	72	70	mi(x;y	mi(x;y	NOUN
ajst-29869	72	71	)	)	PUNCT
ajst-29869	72	72	represents	represent	VERB
ajst-29869	72	73	the	the	DET
ajst-29869	72	74	mutual	mutual	ADJ
ajst-29869	72	75	information	information	NOUN
ajst-29869	72	76	value	value	NOUN
ajst-29869	72	77	between	between	ADP
ajst-29869	72	78	variables	variable	NOUN
ajst-29869	72	79	x	x	PUNCT
ajst-29869	72	80	and	and	CCONJ
ajst-29869	72	81	y.	y.	NOUN
ajst-29869	72	82	the	the	PRON
ajst-29869	72	83	larger	large	ADJ
ajst-29869	72	84	the	the	DET
ajst-29869	72	85	mi	mi	PROPN
ajst-29869	72	86	value	value	NOUN
ajst-29869	72	87	,	,	PUNCT
ajst-29869	72	88	the	the	PRON
ajst-29869	72	89	stronger	strong	ADJ
ajst-29869	72	90	the	the	DET
ajst-29869	72	91	correlation	correlation	NOUN
ajst-29869	72	92	between	between	ADP
ajst-29869	72	93	the	the	DET
ajst-29869	72	94	two	two	NUM
ajst-29869	72	95	variables	variable	NOUN
ajst-29869	72	96	.	.	PUNCT
ajst-29869	73	1	when	when	SCONJ
ajst-29869	73	2	mi(x;y)=0	mi(x;y)=0	NOUN
ajst-29869	73	3	,	,	PUNCT
ajst-29869	73	4	it	it	PRON
ajst-29869	73	5	indicates	indicate	VERB
ajst-29869	73	6	that	that	SCONJ
ajst-29869	73	7	the	the	DET
ajst-29869	73	8	two	two	NUM
ajst-29869	73	9	variables	variable	NOUN
ajst-29869	73	10	are	be	AUX
ajst-29869	73	11	completely	completely	ADV
ajst-29869	73	12	independent	independent	ADJ
ajst-29869	73	13	.	.	PUNCT
ajst-29869	74	1	the	the	DET
ajst-29869	74	2	initial	initial	ADJ
ajst-29869	74	3	feature	feature	NOUN
ajst-29869	74	4	set	set	VERB
ajst-29869	74	5	in	in	ADP
ajst-29869	74	6	this	this	DET
ajst-29869	74	7	study	study	NOUN
ajst-29869	74	8	includes	include	VERB
ajst-29869	74	9	55	55	NUM
ajst-29869	74	10	features	feature	NOUN
ajst-29869	74	11	.	.	PUNCT
ajst-29869	75	1	since	since	SCONJ
ajst-29869	75	2	combining	combine	VERB
ajst-29869	75	3	multiple	multiple	ADJ
ajst-29869	75	4	features	feature	NOUN
ajst-29869	75	5	may	may	AUX
ajst-29869	75	6	result	result	VERB
ajst-29869	75	7	in	in	ADP
ajst-29869	75	8	highly	highly	ADV
ajst-29869	75	9	redundant	redundant	ADJ
ajst-29869	75	10	features	feature	NOUN
ajst-29869	75	11	,	,	PUNCT
ajst-29869	75	12	affecting	affect	VERB
ajst-29869	75	13	the	the	DET
ajst-29869	75	14	efficiency	efficiency	NOUN
ajst-29869	75	15	of	of	ADP
ajst-29869	75	16	subsequent	subsequent	ADJ
ajst-29869	75	17	feature	feature	NOUN
ajst-29869	75	18	selection	selection	NOUN
ajst-29869	75	19	and	and	CCONJ
ajst-29869	75	20	model	model	NOUN
ajst-29869	75	21	accuracy	accuracy	NOUN
ajst-29869	75	22	,	,	PUNCT
ajst-29869	75	23	the	the	DET
ajst-29869	75	24	mutual	mutual	ADJ
ajst-29869	75	25	information	information	NOUN
ajst-29869	75	26	algorithm	algorithm	NOUN
ajst-29869	75	27	is	be	AUX
ajst-29869	75	28	used	use	VERB
ajst-29869	75	29	for	for	ADP
ajst-29869	75	30	preliminary	preliminary	ADJ
ajst-29869	75	31	feature	feature	NOUN
ajst-29869	75	32	screening	screening	NOUN
ajst-29869	75	33	.	.	PUNCT
ajst-29869	76	1	the	the	DET
ajst-29869	76	2	mi	mi	PROPN
ajst-29869	76	3	values	value	NOUN
ajst-29869	76	4	are	be	AUX
ajst-29869	76	5	divided	divide	VERB
ajst-29869	76	6	into	into	ADP
ajst-29869	76	7	three	three	NUM
ajst-29869	76	8	correlation	correlation	NOUN
ajst-29869	76	9	ranges	range	NOUN
ajst-29869	76	10	:	:	PUNCT
ajst-29869	76	11	when	when	SCONJ
ajst-29869	76	12	the	the	DET
ajst-29869	76	13	mi	mi	PROPN
ajst-29869	76	14	value	value	NOUN
ajst-29869	76	15	is	be	AUX
ajst-29869	76	16	greater	great	ADJ
ajst-29869	76	17	than	than	ADP
ajst-29869	76	18	0.5	0.5	NUM
ajst-29869	76	19	,	,	PUNCT
ajst-29869	76	20	it	it	PRON
ajst-29869	76	21	indicates	indicate	VERB
ajst-29869	76	22	a	a	DET
ajst-29869	76	23	high	high	ADJ
ajst-29869	76	24	correlation	correlation	NOUN
ajst-29869	76	25	(	(	PUNCT
ajst-29869	76	26	s	s	NOUN
ajst-29869	76	27	)	)	PUNCT
ajst-29869	76	28	between	between	ADP
ajst-29869	76	29	the	the	DET
ajst-29869	76	30	feature	feature	NOUN
ajst-29869	76	31	and	and	CCONJ
ajst-29869	76	32	the	the	DET
ajst-29869	76	33	target	target	NOUN
ajst-29869	76	34	variable	variable	NOUN
ajst-29869	76	35	;	;	PUNCT
ajst-29869	76	36	when	when	SCONJ
ajst-29869	76	37	the	the	DET
ajst-29869	76	38	mi	mi	PROPN
ajst-29869	76	39	value	value	NOUN
ajst-29869	76	40	is	be	AUX
ajst-29869	76	41	between	between	ADP
ajst-29869	76	42	0.2	0.2	NUM
ajst-29869	76	43	and	and	CCONJ
ajst-29869	76	44	0.5	0.5	NUM
ajst-29869	76	45	,	,	PUNCT
ajst-29869	76	46	it	it	PRON
ajst-29869	76	47	represents	represent	VERB
ajst-29869	76	48	a	a	DET
ajst-29869	76	49	moderate	moderate	ADJ
ajst-29869	76	50	correlation	correlation	NOUN
ajst-29869	76	51	(	(	PUNCT
ajst-29869	76	52	w	w	NOUN
ajst-29869	76	53	)	)	PUNCT
ajst-29869	76	54	;	;	PUNCT
ajst-29869	76	55	and	and	CCONJ
ajst-29869	76	56	when	when	SCONJ
ajst-29869	76	57	the	the	DET
ajst-29869	76	58	mi	mi	PROPN
ajst-29869	76	59	value	value	NOUN
ajst-29869	76	60	is	be	AUX
ajst-29869	76	61	less	less	ADJ
ajst-29869	76	62	than	than	ADP
ajst-29869	76	63	0.2	0.2	NUM
ajst-29869	76	64	,	,	PUNCT
ajst-29869	76	65	it	it	PRON
ajst-29869	76	66	indicates	indicate	VERB
ajst-29869	76	67	low	low	ADJ
ajst-29869	76	68	or	or	CCONJ
ajst-29869	76	69	no	no	PRON
ajst-29869	76	70	correlation	correlation	NOUN
ajst-29869	76	71	(	(	PUNCT
ajst-29869	76	72	n	n	CCONJ
ajst-29869	76	73	)	)	PUNCT
ajst-29869	76	74	with	with	ADP
ajst-29869	76	75	the	the	DET
ajst-29869	76	76	target	target	NOUN
ajst-29869	76	77	variable	variable	NOUN
ajst-29869	76	78	.	.	PUNCT
ajst-29869	77	1	the	the	DET
ajst-29869	77	2	calculation	calculation	NOUN
ajst-29869	77	3	results	result	NOUN
ajst-29869	77	4	are	be	AUX
ajst-29869	77	5	shown	show	VERB
ajst-29869	77	6	in	in	ADP
ajst-29869	77	7	the	the	DET
ajst-29869	77	8	table	table	NOUN
ajst-29869	77	9	below	below	ADV
ajst-29869	77	10	.	.	PUNCT
ajst-29869	78	1	table	table	NOUN
ajst-29869	78	2	2	2	NUM
ajst-29869	78	3	.	.	PUNCT
ajst-29869	78	4	correlation	correlation	NOUN
ajst-29869	78	5	analysis	analysis	NOUN
ajst-29869	78	6	of	of	ADP
ajst-29869	78	7	feature	feature	NOUN
ajst-29869	78	8	set	set	VERB
ajst-29869	78	9	feature	feature	NOUN
ajst-29869	78	10	set	set	VERB
ajst-29869	78	11	number	number	NOUN
ajst-29869	78	12	of	of	ADP
ajst-29869	78	13	features	feature	NOUN
ajst-29869	78	14	recognition	recognition	NOUN
ajst-29869	78	15	accuracy	accuracy	NOUN
ajst-29869	78	16	(	(	PUNCT
ajst-29869	78	17	%	%	NOUN
ajst-29869	78	18	)	)	PUNCT
ajst-29869	78	19	time	time	NOUN
ajst-29869	78	20	(	(	PUNCT
ajst-29869	78	21	min	min	NOUN
ajst-29869	78	22	)	)	PUNCT
ajst-29869	78	23	original	original	ADJ
ajst-29869	78	24	feature	feature	NOUN
ajst-29869	78	25	55	55	NUM
ajst-29869	78	26	93.1	93.1	NUM
ajst-29869	78	27	1341.9	1341.9	NUM
ajst-29869	78	28	mi	mi	X
ajst-29869	78	29	(	(	PUNCT
ajst-29869	78	30	w+n	w+n	PROPN
ajst-29869	78	31	)	)	PUNCT
ajst-29869	78	32	39	39	NUM
ajst-29869	78	33	93.5	93.5	NUM
ajst-29869	78	34	608.1	608.1	NUM
ajst-29869	78	35	mi	mi	X
ajst-29869	78	36	(	(	PUNCT
ajst-29869	78	37	n	n	CCONJ
ajst-29869	78	38	)	)	PUNCT
ajst-29869	78	39	6	6	NUM
ajst-29869	78	40	68.7	68.7	NUM
ajst-29869	78	41	92.4	92.4	NUM
ajst-29869	78	42	from	from	ADP
ajst-29869	78	43	table	table	NOUN
ajst-29869	78	44	2	2	NUM
ajst-29869	78	45	,	,	PUNCT
ajst-29869	78	46	it	it	PRON
ajst-29869	78	47	can	can	AUX
ajst-29869	78	48	be	be	AUX
ajst-29869	78	49	seen	see	VERB
ajst-29869	78	50	that	that	SCONJ
ajst-29869	78	51	after	after	ADP
ajst-29869	78	52	applying	apply	VERB
ajst-29869	78	53	the	the	DET
ajst-29869	78	54	mutual	mutual	ADJ
ajst-29869	78	55	information	information	NOUN
ajst-29869	78	56	algorithm	algorithm	NOUN
ajst-29869	78	57	,	,	PUNCT
ajst-29869	78	58	highly	highly	ADV
ajst-29869	78	59	correlated	correlate	VERB
ajst-29869	78	60	features	feature	NOUN
ajst-29869	78	61	were	be	AUX
ajst-29869	78	62	removed	remove	VERB
ajst-29869	78	63	,	,	PUNCT
ajst-29869	78	64	reducing	reduce	VERB
ajst-29869	78	65	the	the	DET
ajst-29869	78	66	number	number	NOUN
ajst-29869	78	67	of	of	ADP
ajst-29869	78	68	features	feature	NOUN
ajst-29869	78	69	from	from	ADP
ajst-29869	78	70	55	55	NUM
ajst-29869	78	71	to	to	PART
ajst-29869	78	72	36	36	NUM
ajst-29869	78	73	.	.	PUNCT
ajst-29869	79	1	compared	compare	VERB
ajst-29869	79	2	to	to	ADP
ajst-29869	79	3	the	the	DET
ajst-29869	79	4	original	original	ADJ
ajst-29869	79	5	feature	feature	NOUN
ajst-29869	79	6	set	set	NOUN
ajst-29869	79	7	,	,	PUNCT
ajst-29869	79	8	the	the	DET
ajst-29869	79	9	recognition	recognition	NOUN
ajst-29869	79	10	accuracy	accuracy	NOUN
ajst-29869	79	11	improved	improve	VERB
ajst-29869	79	12	by	by	ADP
ajst-29869	79	13	0.4	0.4	NUM
ajst-29869	79	14	%	%	NOUN
ajst-29869	79	15	,	,	PUNCT
ajst-29869	79	16	and	and	CCONJ
ajst-29869	79	17	the	the	DET
ajst-29869	79	18	training	training	NOUN
ajst-29869	79	19	time	time	NOUN
ajst-29869	79	20	was	be	AUX
ajst-29869	79	21	reduced	reduce	VERB
ajst-29869	79	22	to	to	ADP
ajst-29869	79	23	45.3	45.3	NUM
ajst-29869	79	24	%	%	NOUN
ajst-29869	79	25	of	of	ADP
ajst-29869	79	26	the	the	DET
ajst-29869	79	27	original	original	ADJ
ajst-29869	79	28	time	time	NOUN
ajst-29869	79	29	.	.	PUNCT
ajst-29869	80	1	when	when	SCONJ
ajst-29869	80	2	only	only	ADV
ajst-29869	80	3	low	low	ADJ
ajst-29869	80	4	-	-	PUNCT
ajst-29869	80	5	correlation	correlation	NOUN
ajst-29869	80	6	features	feature	NOUN
ajst-29869	80	7	were	be	AUX
ajst-29869	80	8	selected	select	VERB
ajst-29869	80	9	,	,	PUNCT
ajst-29869	80	10	the	the	DET
ajst-29869	80	11	feature	feature	NOUN
ajst-29869	80	12	dimension	dimension	NOUN
ajst-29869	80	13	was	be	AUX
ajst-29869	80	14	reduced	reduce	VERB
ajst-29869	80	15	to	to	ADP
ajst-29869	80	16	6	6	NUM
ajst-29869	80	17	,	,	PUNCT
ajst-29869	80	18	resulting	result	VERB
ajst-29869	80	19	in	in	ADP
ajst-29869	80	20	a	a	DET
ajst-29869	80	21	recognition	recognition	NOUN
ajst-29869	80	22	accuracy	accuracy	NOUN
ajst-29869	80	23	that	that	PRON
ajst-29869	80	24	was	be	AUX
ajst-29869	80	25	too	too	ADV
ajst-29869	80	26	low	low	ADJ
ajst-29869	80	27	to	to	PART
ajst-29869	80	28	meet	meet	VERB
ajst-29869	80	29	practical	practical	ADJ
ajst-29869	80	30	requirements	requirement	NOUN
ajst-29869	80	31	.	.	PUNCT
ajst-29869	81	1	the	the	DET
ajst-29869	81	2	subsequent	subsequent	ADJ
ajst-29869	81	3	feature	feature	NOUN
ajst-29869	81	4	selection	selection	NOUN
ajst-29869	81	5	should	should	AUX
ajst-29869	81	6	use	use	VERB
ajst-29869	81	7	a	a	DET
ajst-29869	81	8	feature	feature	NOUN
ajst-29869	81	9	set	set	NOUN
ajst-29869	81	10	that	that	PRON
ajst-29869	81	11	includes	include	VERB
ajst-29869	81	12	both	both	CCONJ
ajst-29869	81	13	moderate	moderate	ADJ
ajst-29869	81	14	and	and	CCONJ
ajst-29869	81	15	low	low	ADJ
ajst-29869	81	16	correlation	correlation	NOUN
ajst-29869	81	17	features	feature	NOUN
ajst-29869	81	18	as	as	ADP
ajst-29869	81	19	the	the	DET
ajst-29869	81	20	initial	initial	ADJ
ajst-29869	81	21	feature	feature	NOUN
ajst-29869	81	22	set	set	NOUN
ajst-29869	81	23	.	.	PUNCT
ajst-29869	82	1	the	the	DET
ajst-29869	82	2	mi(w+n	mi(w+n	NOUN
ajst-29869	82	3	)	)	PUNCT
ajst-29869	82	4	feature	feature	NOUN
ajst-29869	82	5	set	set	VERB
ajst-29869	82	6	effectively	effectively	ADV
ajst-29869	82	7	improves	improve	VERB
ajst-29869	82	8	recognition	recognition	NOUN
ajst-29869	82	9	accuracy	accuracy	NOUN
ajst-29869	82	10	and	and	CCONJ
ajst-29869	82	11	efficiency	efficiency	NOUN
ajst-29869	82	12	.	.	PUNCT
ajst-29869	83	1	therefore	therefore	ADV
ajst-29869	83	2	,	,	PUNCT
ajst-29869	83	3	this	this	DET
ajst-29869	83	4	feature	feature	NOUN
ajst-29869	83	5	set	set	NOUN
ajst-29869	83	6	is	be	AUX
ajst-29869	83	7	selected	select	VERB
ajst-29869	83	8	as	as	ADP
ajst-29869	83	9	the	the	DET
ajst-29869	83	10	initial	initial	ADJ
ajst-29869	83	11	feature	feature	NOUN
ajst-29869	83	12	set	set	VERB
ajst-29869	83	13	for	for	ADP
ajst-29869	83	14	subsequent	subsequent	ADJ
ajst-29869	83	15	feature	feature	NOUN
ajst-29869	83	16	selection	selection	NOUN
ajst-29869	83	17	.	.	PUNCT
ajst-29869	84	1	2.1.4	2.1.4	NUM
ajst-29869	84	2	.	.	PUNCT
ajst-29869	84	3	relief	relief	NOUN
ajst-29869	84	4	algorithm	algorithm	NOUN
ajst-29869	84	5	from	from	ADP
ajst-29869	84	6	the	the	DET
ajst-29869	84	7	above	above	ADJ
ajst-29869	84	8	initial	initial	ADJ
ajst-29869	84	9	feature	feature	NOUN
ajst-29869	84	10	set	set	NOUN
ajst-29869	84	11	,	,	PUNCT
ajst-29869	84	12	it	it	PRON
ajst-29869	84	13	can	can	AUX
ajst-29869	84	14	be	be	AUX
ajst-29869	84	15	observed	observe	VERB
ajst-29869	84	16	that	that	SCONJ
ajst-29869	84	17	although	although	SCONJ
ajst-29869	84	18	the	the	DET
ajst-29869	84	19	feature	feature	NOUN
ajst-29869	84	20	dimension	dimension	NOUN
ajst-29869	84	21	has	have	AUX
ajst-29869	84	22	been	be	AUX
ajst-29869	84	23	reduced	reduce	VERB
ajst-29869	84	24	,	,	PUNCT
ajst-29869	84	25	the	the	DET
ajst-29869	84	26	feature	feature	NOUN
ajst-29869	84	27	set	set	VERB
ajst-29869	84	28	still	still	ADV
ajst-29869	84	29	possesses	possess	VERB
ajst-29869	84	30	high	high	ADJ
ajst-29869	84	31	dimensionality	dimensionality	NOUN
ajst-29869	84	32	and	and	CCONJ
ajst-29869	84	33	large	large	ADJ
ajst-29869	84	34	computational	computational	ADJ
ajst-29869	84	35	complexity	complexity	NOUN
ajst-29869	84	36	.	.	PUNCT
ajst-29869	85	1	if	if	SCONJ
ajst-29869	85	2	directly	directly	ADV
ajst-29869	85	3	applied	apply	VERB
ajst-29869	85	4	to	to	ADP
ajst-29869	85	5	subsequent	subsequent	ADJ
ajst-29869	85	6	optimization	optimization	NOUN
ajst-29869	85	7	algorithms	algorithm	NOUN
ajst-29869	85	8	,	,	PUNCT
ajst-29869	85	9	it	it	PRON
ajst-29869	85	10	may	may	AUX
ajst-29869	85	11	lead	lead	VERB
ajst-29869	85	12	to	to	ADP
ajst-29869	85	13	excessively	excessively	ADV
ajst-29869	85	14	long	long	ADJ
ajst-29869	85	15	computation	computation	NOUN
ajst-29869	85	16	times	time	NOUN
ajst-29869	85	17	and	and	CCONJ
ajst-29869	85	18	affect	affect	VERB
ajst-29869	85	19	the	the	DET
ajst-29869	85	20	recognition	recognition	NOUN
ajst-29869	85	21	performance	performance	NOUN
ajst-29869	85	22	of	of	ADP
ajst-29869	85	23	the	the	DET
ajst-29869	85	24	final	final	ADJ
ajst-29869	85	25	optimal	optimal	ADJ
ajst-29869	85	26	feature	feature	NOUN
ajst-29869	85	27	subset	subset	NOUN
ajst-29869	85	28	.	.	PUNCT
ajst-29869	86	1	therefore	therefore	ADV
ajst-29869	86	2	,	,	PUNCT
ajst-29869	86	3	secondary	secondary	ADJ
ajst-29869	86	4	feature	feature	NOUN
ajst-29869	86	5	selection	selection	NOUN
ajst-29869	86	6	on	on	ADP
ajst-29869	86	7	the	the	DET
ajst-29869	86	8	initially	initially	ADV
ajst-29869	86	9	screened	screen	VERB
ajst-29869	86	10	feature	feature	NOUN
ajst-29869	86	11	set	set	VERB
ajst-29869	86	12	is	be	AUX
ajst-29869	86	13	crucial	crucial	ADJ
ajst-29869	86	14	.	.	PUNCT
ajst-29869	87	1	this	this	PRON
ajst-29869	87	2	not	not	PART
ajst-29869	87	3	only	only	ADV
ajst-29869	87	4	reduces	reduce	VERB
ajst-29869	87	5	the	the	DET
ajst-29869	87	6	computation	computation	NOUN
ajst-29869	87	7	and	and	CCONJ
ajst-29869	87	8	time	time	NOUN
ajst-29869	87	9	complexity	complexity	NOUN
ajst-29869	87	10	but	but	CCONJ
ajst-29869	87	11	also	also	ADV
ajst-29869	87	12	further	far	ADV
ajst-29869	87	13	enhances	enhance	VERB
ajst-29869	87	14	the	the	DET
ajst-29869	87	15	accuracy	accuracy	NOUN
ajst-29869	87	16	and	and	CCONJ
ajst-29869	87	17	stability	stability	NOUN
ajst-29869	87	18	of	of	ADP
ajst-29869	87	19	the	the	DET
ajst-29869	87	20	algorithm	algorithm	NOUN
ajst-29869	87	21	[	[	X
ajst-29869	87	22	13	13	NUM
ajst-29869	87	23	-	-	SYM
ajst-29869	87	24	15	15	NUM
ajst-29869	87	25	]	]	PUNCT
ajst-29869	87	26	.	.	PUNCT
ajst-29869	88	1	the	the	DET
ajst-29869	88	2	relief	relief	NOUN
ajst-29869	88	3	algorithm	algorithm	NOUN
ajst-29869	88	4	is	be	AUX
ajst-29869	88	5	a	a	DET
ajst-29869	88	6	feature	feature	NOUN
ajst-29869	88	7	selection	selection	NOUN
ajst-29869	88	8	method	method	NOUN
ajst-29869	88	9	designed	design	VERB
ajst-29869	88	10	for	for	ADP
ajst-29869	88	11	binary	binary	ADJ
ajst-29869	88	12	classification	classification	NOUN
ajst-29869	88	13	problems	problem	NOUN
ajst-29869	88	14	.	.	PUNCT
ajst-29869	89	1	by	by	ADP
ajst-29869	89	2	introducing	introduce	VERB
ajst-29869	89	3	a	a	DET
ajst-29869	89	4	weight	weight	NOUN
ajst-29869	89	5	evaluation	evaluation	NOUN
ajst-29869	89	6	mechanism	mechanism	NOUN
ajst-29869	89	7	based	base	VERB
ajst-29869	89	8	on	on	ADP
ajst-29869	89	9	sample	sample	NOUN
ajst-29869	89	10	distances	distance	NOUN
ajst-29869	89	11	,	,	PUNCT
ajst-29869	89	12	it	it	PRON
ajst-29869	89	13	not	not	PART
ajst-29869	89	14	only	only	ADV
ajst-29869	89	15	considers	consider	VERB
ajst-29869	89	16	the	the	DET
ajst-29869	89	17	relationship	relationship	NOUN
ajst-29869	89	18	between	between	ADP
ajst-29869	89	19	each	each	DET
ajst-29869	89	20	sample	sample	NOUN
ajst-29869	89	21	and	and	CCONJ
ajst-29869	89	22	other	other	ADJ
ajst-29869	89	23	samples	sample	NOUN
ajst-29869	89	24	within	within	ADP
ajst-29869	89	25	the	the	DET
ajst-29869	89	26	same	same	ADJ
ajst-29869	89	27	class	class	NOUN
ajst-29869	89	28	but	but	CCONJ
ajst-29869	89	29	also	also	ADV
ajst-29869	89	30	incorporates	incorporate	VERB
ajst-29869	89	31	the	the	DET
ajst-29869	89	32	differences	difference	NOUN
ajst-29869	89	33	with	with	ADP
ajst-29869	89	34	samples	sample	NOUN
ajst-29869	89	35	from	from	ADP
ajst-29869	89	36	other	other	ADJ
ajst-29869	89	37	classes	class	NOUN
ajst-29869	89	38	,	,	PUNCT
ajst-29869	89	39	thus	thus	ADV
ajst-29869	89	40	providing	provide	VERB
ajst-29869	89	41	a	a	DET
ajst-29869	89	42	more	more	ADV
ajst-29869	89	43	comprehensive	comprehensive	ADJ
ajst-29869	89	44	measure	measure	NOUN
ajst-29869	89	45	of	of	ADP
ajst-29869	89	46	feature	feature	NOUN
ajst-29869	89	47	importance	importance	NOUN
ajst-29869	89	48	.	.	PUNCT
ajst-29869	90	1	this	this	PRON
ajst-29869	90	2	makes	make	VERB
ajst-29869	90	3	relief	relief	NOUN
ajst-29869	90	4	a	a	DET
ajst-29869	90	5	widely	widely	ADV
ajst-29869	90	6	used	use	VERB
ajst-29869	90	7	and	and	CCONJ
ajst-29869	90	8	classic	classic	ADJ
ajst-29869	90	9	algorithm	algorithm	NOUN
ajst-29869	90	10	in	in	ADP
ajst-29869	90	11	the	the	DET
ajst-29869	90	12	field	field	NOUN
ajst-29869	90	13	of	of	ADP
ajst-29869	90	14	feature	feature	NOUN
ajst-29869	90	15	selection	selection	NOUN
ajst-29869	90	16	and	and	CCONJ
ajst-29869	90	17	is	be	AUX
ajst-29869	90	18	regarded	regard	VERB
ajst-29869	90	19	as	as	ADP
ajst-29869	90	20	one	one	NUM
ajst-29869	90	21	of	of	ADP
ajst-29869	90	22	the	the	DET
ajst-29869	90	23	most	most	ADV
ajst-29869	90	24	successful	successful	ADJ
ajst-29869	90	25	algorithms	algorithm	NOUN
ajst-29869	90	26	in	in	ADP
ajst-29869	90	27	the	the	DET
ajst-29869	90	28	preprocessing	preprocessing	NOUN
ajst-29869	90	29	phase	phase	NOUN
ajst-29869	90	30	of	of	ADP
ajst-29869	90	31	feature	feature	NOUN
ajst-29869	90	32	selection	selection	NOUN
ajst-29869	90	33	.	.	PUNCT
ajst-29869	91	1	in	in	ADP
ajst-29869	91	2	this	this	DET
ajst-29869	91	3	paper	paper	NOUN
ajst-29869	91	4	,	,	PUNCT
ajst-29869	91	5	the	the	DET
ajst-29869	91	6	relief	relief	NOUN
ajst-29869	91	7	algorithm	algorithm	NOUN
ajst-29869	91	8	is	be	AUX
ajst-29869	91	9	applied	apply	VERB
ajst-29869	91	10	to	to	ADP
ajst-29869	91	11	the	the	DET
ajst-29869	91	12	secondary	secondary	ADJ
ajst-29869	91	13	screening	screening	NOUN
ajst-29869	91	14	of	of	ADP
ajst-29869	91	15	the	the	DET
ajst-29869	91	16	mountainous	mountainous	ADJ
ajst-29869	91	17	rice	rice	NOUN
ajst-29869	91	18	identification	identification	NOUN
ajst-29869	91	19	feature	feature	NOUN
ajst-29869	91	20	set	set	VERB
ajst-29869	91	21	.	.	PUNCT
ajst-29869	92	1	the	the	DET
ajst-29869	92	2	mi	mi	PROPN
ajst-29869	92	3	(	(	PUNCT
ajst-29869	92	4	w+n	w+n	PROPN
ajst-29869	92	5	)	)	PUNCT
ajst-29869	92	6	feature	feature	NOUN
ajst-29869	92	7	set	set	NOUN
ajst-29869	92	8	is	be	AUX
ajst-29869	92	9	used	use	VERB
ajst-29869	92	10	as	as	ADP
ajst-29869	92	11	input	input	NOUN
ajst-29869	92	12	to	to	PART
ajst-29869	92	13	calculate	calculate	VERB
ajst-29869	92	14	the	the	DET
ajst-29869	92	15	weight	weight	NOUN
ajst-29869	92	16	value	value	NOUN
ajst-29869	92	17	of	of	ADP
ajst-29869	92	18	each	each	DET
ajst-29869	92	19	feature	feature	NOUN
ajst-29869	92	20	.	.	PUNCT
ajst-29869	93	1	a	a	DET
ajst-29869	93	2	weight	weight	NOUN
ajst-29869	93	3	threshold	threshold	NOUN
ajst-29869	93	4	is	be	AUX
ajst-29869	93	5	set	set	VERB
ajst-29869	93	6	to	to	PART
ajst-29869	93	7	complete	complete	VERB
ajst-29869	93	8	the	the	DET
ajst-29869	93	9	secondary	secondary	ADJ
ajst-29869	93	10	feature	feature	NOUN
ajst-29869	93	11	selection	selection	NOUN
ajst-29869	93	12	.	.	PUNCT
ajst-29869	94	1	the	the	DET
ajst-29869	94	2	specific	specific	ADJ
ajst-29869	94	3	steps	step	NOUN
ajst-29869	94	4	are	be	AUX
ajst-29869	94	5	as	as	SCONJ
ajst-29869	94	6	follows	follow	VERB
ajst-29869	94	7	:	:	PUNCT
ajst-29869	94	8	randomly	randomly	ADV
ajst-29869	94	9	select	select	VERB
ajst-29869	94	10	a	a	DET
ajst-29869	94	11	sample	sample	NOUN
ajst-29869	94	12	s	s	NOUN
ajst-29869	94	13	,	,	PUNCT
ajst-29869	94	14	find	find	VERB
ajst-29869	94	15	k	k	PROPN
ajst-29869	94	16	nearest	near	ADJ
ajst-29869	94	17	neighbor	neighbor	NOUN
ajst-29869	94	18	samples	sample	NOUN
ajst-29869	94	19	nh	nh	PROPN
ajst-29869	94	20	within	within	ADP
ajst-29869	94	21	the	the	DET
ajst-29869	94	22	same	same	ADJ
ajst-29869	94	23	class	class	NOUN
ajst-29869	94	24	of	of	ADP
ajst-29869	94	25	s	s	PROPN
ajst-29869	94	26	,	,	PUNCT
ajst-29869	94	27	and	and	CCONJ
ajst-29869	94	28	simultaneously	simultaneously	ADV
ajst-29869	94	29	find	find	VERB
ajst-29869	94	30	k	k	PROPN
ajst-29869	94	31	nearest	near	ADJ
ajst-29869	94	32	neighbor	neighbor	NOUN
ajst-29869	94	33	samples	sample	VERB
ajst-29869	94	34	nm	nm	ADV
ajst-29869	94	35	from	from	ADP
ajst-29869	94	36	different	different	ADJ
ajst-29869	94	37	classes	class	NOUN
ajst-29869	94	38	.	.	PUNCT
ajst-29869	95	1	then	then	ADV
ajst-29869	95	2	,	,	PUNCT
ajst-29869	95	3	using	use	VERB
ajst-29869	95	4	an	an	DET
ajst-29869	95	5	iterative	iterative	NOUN
ajst-29869	95	6	update	update	NOUN
ajst-29869	95	7	method	method	NOUN
ajst-29869	95	8	,	,	PUNCT
ajst-29869	95	9	the	the	DET
ajst-29869	95	10	feature	feature	NOUN
ajst-29869	95	11	weight	weight	NOUN
ajst-29869	95	12	ω(x	ω(x	NOUN
ajst-29869	95	13	)	)	PUNCT
ajst-29869	95	14	is	be	AUX
ajst-29869	95	15	calculated	calculate	VERB
ajst-29869	95	16	,	,	PUNCT
ajst-29869	95	17	as	as	SCONJ
ajst-29869	95	18	shown	show	VERB
ajst-29869	95	19	in	in	ADP
ajst-29869	95	20	equations	equation	NOUN
ajst-29869	95	21	(	(	PUNCT
ajst-29869	95	22	2	2	NUM
ajst-29869	95	23	)	)	PUNCT
ajst-29869	95	24	.	.	PUNCT
ajst-29869	96	1	diff	diff	PROPN
ajst-29869	96	2	x	x	SYM
ajst-29869	96	3	,	,	PUNCT
ajst-29869	96	4	s	s	X
ajst-29869	96	5	,	,	PUNCT
ajst-29869	96	6	s	s	NOUN
ajst-29869	96	7	'	'	PUNCT
ajst-29869	96	8	=	=	SYM
ajst-29869	96	9	⎩	⎩	PROPN
ajst-29869	96	10	⎨	⎨	ADJ
ajst-29869	96	11	⎧	⎧	PROPN
ajst-29869	96	12	s	s	PART
ajst-29869	96	13	x	x	INTJ
ajst-29869	96	14	-s	-s	PROPN
ajst-29869	96	15	'	'	PUNCT
ajst-29869	96	16	x	x	SYM
ajst-29869	96	17	max	max	PROPN
ajst-29869	96	18	x	x	SYM
ajst-29869	96	19	min	min	NOUN
ajst-29869	96	20	x	x	SYM
ajst-29869	96	21	x	x	NOUN
ajst-29869	96	22	is	be	AUX
ajst-29869	96	23	continuous	continuous	ADJ
ajst-29869	96	24	0	0	PUNCT
ajst-29869	96	25	x	x	PRON
ajst-29869	96	26	is	be	AUX
ajst-29869	96	27	discrete	discrete	ADJ
ajst-29869	96	28	,	,	PUNCT
ajst-29869	96	29	and	and	CCONJ
ajst-29869	96	30	s	s	VERB
ajst-29869	96	31	x	x	PUNCT
ajst-29869	97	1	=	=	SYM
ajst-29869	97	2	s	s	NOUN
ajst-29869	97	3	'	'	PUNCT
ajst-29869	97	4	x	x	SYM
ajst-29869	97	5	1	1	NUM
ajst-29869	97	6	x	x	NOUN
ajst-29869	97	7	is	be	AUX
ajst-29869	97	8	discrete	discrete	ADJ
ajst-29869	97	9	,	,	PUNCT
ajst-29869	97	10	and	and	CCONJ
ajst-29869	97	11	s	s	NOUN
ajst-29869	97	12	x	x	PART
ajst-29869	97	13	≠s	≠s	NOUN
ajst-29869	97	14	'	'	PUNCT
ajst-29869	97	15	x	x	SYM
ajst-29869	97	16	(	(	PUNCT
ajst-29869	97	17	2	2	NUM
ajst-29869	97	18	)	)	PUNCT
ajst-29869	97	19	in	in	ADP
ajst-29869	97	20	the	the	DET
ajst-29869	97	21	equations	equation	NOUN
ajst-29869	97	22	,	,	PUNCT
ajst-29869	97	23	x	x	PRON
ajst-29869	97	24	represents	represent	VERB
ajst-29869	97	25	a	a	DET
ajst-29869	97	26	feature	feature	NOUN
ajst-29869	97	27	in	in	ADP
ajst-29869	97	28	the	the	DET
ajst-29869	97	29	feature	feature	NOUN
ajst-29869	97	30	set	set	NOUN
ajst-29869	97	31	;	;	PUNCT
ajst-29869	97	32	s	s	X
ajst-29869	97	33	and	and	CCONJ
ajst-29869	97	34	s^	s^	PROPN
ajst-29869	97	35	'	'	PUNCT
ajst-29869	97	36	represent	represent	VERB
ajst-29869	97	37	two	two	NUM
ajst-29869	97	38	randomly	randomly	ADV
ajst-29869	97	39	selected	select	VERB
ajst-29869	97	40	samples	sample	NOUN
ajst-29869	97	41	;	;	PUNCT
ajst-29869	97	42	k	k	X
ajst-29869	97	43	represents	represent	VERB
ajst-29869	97	44	the	the	DET
ajst-29869	97	45	number	number	NOUN
ajst-29869	97	46	of	of	ADP
ajst-29869	97	47	nearest	near	ADJ
ajst-29869	97	48	neighbors	neighbor	NOUN
ajst-29869	97	49	;	;	PUNCT
ajst-29869	97	50	m	m	VERB
ajst-29869	97	51	represents	represent	VERB
ajst-29869	97	52	the	the	DET
ajst-29869	97	53	number	number	NOUN
ajst-29869	97	54	of	of	ADP
ajst-29869	97	55	iterations	iteration	NOUN
ajst-29869	97	56	;	;	PUNCT
ajst-29869	97	57	c	c	NOUN
ajst-29869	97	58	represents	represent	VERB
ajst-29869	97	59	the	the	DET
ajst-29869	97	60	class	class	NOUN
ajst-29869	97	61	label	label	NOUN
ajst-29869	97	62	;	;	PUNCT
ajst-29869	97	63	p(c	p(c	NOUN
ajst-29869	97	64	)	)	PUNCT
ajst-29869	97	65	represents	represent	VERB
ajst-29869	97	66	the	the	DET
ajst-29869	97	67	probability	probability	NOUN
ajst-29869	97	68	that	that	SCONJ
ajst-29869	97	69	the	the	DET
ajst-29869	97	70	feature	feature	NOUN
ajst-29869	97	71	belongs	belong	VERB
ajst-29869	97	72	to	to	ADP
ajst-29869	97	73	class	class	NOUN
ajst-29869	97	74	c	c	NOUN
ajst-29869	97	75	;	;	PUNCT
ajst-29869	97	76	s[x	s[x	X
ajst-29869	97	77	]	]	PUNCT
ajst-29869	97	78	represents	represent	VERB
ajst-29869	97	79	the	the	DET
ajst-29869	97	80	feature	feature	NOUN
ajst-29869	97	81	value	value	NOUN
ajst-29869	97	82	of	of	ADP
ajst-29869	97	83	sample	sample	NOUN
ajst-29869	97	84	s	s	NOUN
ajst-29869	97	85	;	;	PUNCT
ajst-29869	97	86	nh_j	nh_j	NUM
ajst-29869	97	87	represents	represent	VERB
ajst-29869	97	88	the	the	DET
ajst-29869	97	89	j	j	PROPN
ajst-29869	97	90	-	-	PUNCT
ajst-29869	97	91	th	th	X
ajst-29869	97	92	nearest	near	ADJ
ajst-29869	97	93	neighbor	neighbor	NOUN
ajst-29869	97	94	sample	sample	NOUN
ajst-29869	97	95	in	in	ADP
ajst-29869	97	96	class	class	NOUN
ajst-29869	97	97	c	c	NOUN
ajst-29869	97	98	;	;	PUNCT
ajst-29869	97	99	nm(c)_j	nm(c)_j	X
ajst-29869	97	100	represents	represent	VERB
ajst-29869	97	101	the	the	DET
ajst-29869	97	102	j	j	PROPN
ajst-29869	97	103	-	-	PUNCT
ajst-29869	97	104	th	th	X
ajst-29869	97	105	nearest	near	ADJ
ajst-29869	97	106	neighbor	neighbor	NOUN
ajst-29869	97	107	sample	sample	NOUN
ajst-29869	97	108	in	in	ADP
ajst-29869	97	109	the	the	DET
ajst-29869	97	110	non	non	ADJ
ajst-29869	97	111	-	-	PROPN
ajst-29869	97	112	c	c	ADJ
ajst-29869	97	113	class	class	NOUN
ajst-29869	97	114	;	;	PUNCT
ajst-29869	97	115	max(x	max(x	PROPN
ajst-29869	97	116	)	)	PUNCT
ajst-29869	97	117	and	and	CCONJ
ajst-29869	97	118	min(x	min(x	PROPN
ajst-29869	97	119	)	)	PUNCT
ajst-29869	97	120	represent	represent	VERB
ajst-29869	97	121	the	the	DET
ajst-29869	97	122	maximum	maximum	ADJ
ajst-29869	97	123	and	and	CCONJ
ajst-29869	97	124	minimum	minimum	ADJ
ajst-29869	97	125	values	value	NOUN
ajst-29869	97	126	of	of	ADP
ajst-29869	97	127	feature	feature	NOUN
ajst-29869	97	128	x	x	NOUN
ajst-29869	97	129	,	,	PUNCT
ajst-29869	97	130	respectively	respectively	ADV
ajst-29869	97	131	;	;	PUNCT
ajst-29869	97	132	class(s	class(s	NOUN
ajst-29869	97	133	)	)	PUNCT
ajst-29869	97	134	represents	represent	VERB
ajst-29869	97	135	the	the	DET
ajst-29869	97	136	class	class	NOUN
ajst-29869	97	137	of	of	ADP
ajst-29869	97	138	sample	sample	NOUN
ajst-29869	97	139	s	s	PART
ajst-29869	97	140	;	;	PUNCT
ajst-29869	97	141	and	and	CCONJ
ajst-29869	97	142	diff(x	diff(x	PROPN
ajst-29869	97	143	,	,	PUNCT
ajst-29869	97	144	s	s	PROPN
ajst-29869	97	145	,	,	PUNCT
ajst-29869	97	146	s^	s^	VERB
ajst-29869	97	147	'	'	PUNCT
ajst-29869	97	148	)	)	PUNCT
ajst-29869	97	149	represents	represent	VERB
ajst-29869	97	150	the	the	DET
ajst-29869	97	151	difference	difference	NOUN
ajst-29869	97	152	in	in	ADP
ajst-29869	97	153	feature	feature	NOUN
ajst-29869	97	154	x	x	PUNCT
ajst-29869	97	155	between	between	ADP
ajst-29869	97	156	sample	sample	NOUN
ajst-29869	97	157	s	s	NOUN
ajst-29869	97	158	and	and	CCONJ
ajst-29869	97	159	s^	s^	NOUN
ajst-29869	97	160	'	'	PUNCT
ajst-29869	97	161	.	.	PUNCT
ajst-29869	98	1	the	the	DET
ajst-29869	98	2	relief	relief	NOUN
ajst-29869	98	3	algorithm	algorithm	NOUN
ajst-29869	98	4	is	be	AUX
ajst-29869	98	5	essentially	essentially	ADV
ajst-29869	98	6	a	a	DET
ajst-29869	98	7	method	method	NOUN
ajst-29869	98	8	for	for	ADP
ajst-29869	98	9	calculating	calculate	VERB
ajst-29869	98	10	feature	feature	NOUN
ajst-29869	98	11	weights	weight	NOUN
ajst-29869	98	12	and	and	CCONJ
ajst-29869	98	13	does	do	AUX
ajst-29869	98	14	not	not	PART
ajst-29869	98	15	directly	directly	ADV
ajst-29869	98	16	perform	perform	VERB
ajst-29869	98	17	feature	feature	NOUN
ajst-29869	98	18	selection[16	selection[16	PROPN
ajst-29869	98	19	]	]	PUNCT
ajst-29869	98	20	.	.	PUNCT
ajst-29869	99	1	therefore	therefore	ADV
ajst-29869	99	2	,	,	PUNCT
ajst-29869	99	3	a	a	DET
ajst-29869	99	4	threshold	threshold	NOUN
ajst-29869	99	5	for	for	ADP
ajst-29869	99	6	feature	feature	NOUN
ajst-29869	99	7	weights	weight	NOUN
ajst-29869	99	8	can	can	AUX
ajst-29869	99	9	be	be	AUX
ajst-29869	99	10	set	set	VERB
ajst-29869	99	11	manually	manually	ADV
ajst-29869	99	12	to	to	PART
ajst-29869	99	13	select	select	VERB
ajst-29869	99	14	high	high	ADJ
ajst-29869	99	15	-	-	PUNCT
ajst-29869	99	16	weight	weight	NOUN
ajst-29869	99	17	features	feature	NOUN
ajst-29869	99	18	,	,	PUNCT
ajst-29869	99	19	forming	form	VERB
ajst-29869	99	20	the	the	DET
ajst-29869	99	21	optimal	optimal	ADJ
ajst-29869	99	22	feature	feature	NOUN
ajst-29869	99	23	subset	subset	NOUN
ajst-29869	99	24	.	.	PUNCT
ajst-29869	100	1	by	by	ADP
ajst-29869	100	2	setting	set	VERB
ajst-29869	100	3	an	an	DET
ajst-29869	100	4	appropriate	appropriate	ADJ
ajst-29869	100	5	threshold	threshold	NOUN
ajst-29869	100	6	,	,	PUNCT
ajst-29869	100	7	not	not	PART
ajst-29869	100	8	only	only	ADV
ajst-29869	100	9	can	can	AUX
ajst-29869	100	10	the	the	DET
ajst-29869	100	11	feature	feature	NOUN
ajst-29869	100	12	set	set	NOUN
ajst-29869	100	13	's	's	PART
ajst-29869	100	14	dimensionality	dimensionality	NOUN
ajst-29869	100	15	be	be	AUX
ajst-29869	100	16	optimized	optimize	VERB
ajst-29869	100	17	,	,	PUNCT
ajst-29869	100	18	but	but	CCONJ
ajst-29869	100	19	it	it	PRON
ajst-29869	100	20	can	can	AUX
ajst-29869	100	21	also	also	ADV
ajst-29869	100	22	ensure	ensure	VERB
ajst-29869	100	23	that	that	SCONJ
ajst-29869	100	24	the	the	DET
ajst-29869	100	25	selected	select	VERB
ajst-29869	100	26	features	feature	NOUN
ajst-29869	100	27	contribute	contribute	VERB
ajst-29869	100	28	significantly	significantly	ADV
ajst-29869	100	29	to	to	ADP
ajst-29869	100	30	the	the	DET
ajst-29869	100	31	recognition	recognition	NOUN
ajst-29869	100	32	of	of	ADP
ajst-29869	100	33	typical	typical	ADJ
ajst-29869	100	34	regions	region	NOUN
ajst-29869	100	35	.	.	PUNCT
ajst-29869	101	1	in	in	ADP
ajst-29869	101	2	this	this	DET
ajst-29869	101	3	paper	paper	NOUN
ajst-29869	101	4	,	,	PUNCT
ajst-29869	101	5	the	the	DET
ajst-29869	101	6	feature	feature	NOUN
ajst-29869	101	7	weights	weight	NOUN
ajst-29869	101	8	calculated	calculate	VERB
ajst-29869	101	9	by	by	ADP
ajst-29869	101	10	the	the	DET
ajst-29869	101	11	relief	relief	NOUN
ajst-29869	101	12	algorithm	algorithm	NOUN
ajst-29869	101	13	are	be	AUX
ajst-29869	101	14	first	first	ADV
ajst-29869	101	15	normalized	normalize	VERB
ajst-29869	101	16	into	into	ADP
ajst-29869	101	17	percentages	percentage	NOUN
ajst-29869	101	18	to	to	PART
ajst-29869	101	19	facilitate	facilitate	VERB
ajst-29869	101	20	a	a	DET
ajst-29869	101	21	more	more	ADV
ajst-29869	101	22	intuitive	intuitive	ADJ
ajst-29869	101	23	analysis	analysis	NOUN
ajst-29869	101	24	of	of	ADP
ajst-29869	101	25	the	the	DET
ajst-29869	101	26	weight	weight	NOUN
ajst-29869	101	27	distribution	distribution	NOUN
ajst-29869	101	28	range	range	NOUN
ajst-29869	101	29	.	.	PUNCT
ajst-29869	102	1	different	different	ADJ
ajst-29869	102	2	threshold	threshold	NOUN
ajst-29869	102	3	ranges	range	NOUN
ajst-29869	102	4	are	be	AUX
ajst-29869	102	5	set	set	VERB
ajst-29869	102	6	based	base	VERB
ajst-29869	102	7	on	on	ADP
ajst-29869	102	8	the	the	DET
ajst-29869	102	9	feature	feature	NOUN
ajst-29869	102	10	weight	weight	NOUN
ajst-29869	102	11	distribution	distribution	NOUN
ajst-29869	102	12	,	,	PUNCT
ajst-29869	102	13	and	and	CCONJ
ajst-29869	102	14	features	feature	VERB
ajst-29869	102	15	with	with	ADP
ajst-29869	102	16	lower	low	ADJ
ajst-29869	102	17	weights	weight	NOUN
ajst-29869	102	18	are	be	AUX
ajst-29869	102	19	discarded	discard	VERB
ajst-29869	102	20	.	.	PUNCT
ajst-29869	103	1	the	the	DET
ajst-29869	103	2	performance	performance	NOUN
ajst-29869	103	3	of	of	ADP
ajst-29869	103	4	the	the	DET
ajst-29869	103	5	feature	feature	NOUN
ajst-29869	103	6	subsets	subset	NOUN
ajst-29869	103	7	corresponding	correspond	VERB
ajst-29869	103	8	to	to	ADP
ajst-29869	103	9	different	different	ADJ
ajst-29869	103	10	weight	weight	NOUN
ajst-29869	103	11	thresholds	threshold	NOUN
ajst-29869	103	12	is	be	AUX
ajst-29869	103	13	then	then	ADV
ajst-29869	103	14	evaluated	evaluate	VERB
ajst-29869	103	15	using	use	VERB
ajst-29869	103	16	the	the	DET
ajst-29869	103	17	classifier	classifier	NOUN
ajst-29869	103	18	's	's	PART
ajst-29869	103	19	recognition	recognition	NOUN
ajst-29869	103	20	accuracy	accuracy	NOUN
ajst-29869	103	21	.	.	PUNCT
ajst-29869	104	1	specifically	specifically	ADV
ajst-29869	104	2	,	,	PUNCT
ajst-29869	104	3	the	the	DET
ajst-29869	104	4	feature	feature	NOUN
ajst-29869	104	5	weights	weight	NOUN
ajst-29869	104	6	are	be	AUX
ajst-29869	104	7	divided	divide	VERB
ajst-29869	104	8	into	into	ADP
ajst-29869	104	9	five	five	NUM
ajst-29869	104	10	ranges	range	NOUN
ajst-29869	104	11	:	:	PUNCT
ajst-29869	104	12	0.005	0.005	NUM
ajst-29869	104	13	,	,	PUNCT
ajst-29869	104	14	0.010	0.010	NUM
ajst-29869	104	15	,	,	PUNCT
ajst-29869	104	16	0.015	0.015	NUM
ajst-29869	104	17	,	,	PUNCT
ajst-29869	104	18	and	and	CCONJ
ajst-29869	104	19	0.020	0.020	NUM
ajst-29869	104	20	.	.	PUNCT
ajst-29869	105	1	based	base	VERB
ajst-29869	105	2	on	on	ADP
ajst-29869	105	3	the	the	DET
ajst-29869	105	4	random	random	ADJ
ajst-29869	105	5	forest	forest	NOUN
ajst-29869	105	6	classifier	classifier	NOUN
ajst-29869	105	7	,	,	PUNCT
ajst-29869	105	8	the	the	DET
ajst-29869	105	9	recognition	recognition	NOUN
ajst-29869	105	10	148	148	NUM
ajst-29869	105	11	accuracy	accuracy	NOUN
ajst-29869	105	12	and	and	CCONJ
ajst-29869	105	13	number	number	NOUN
ajst-29869	105	14	of	of	ADP
ajst-29869	105	15	features	feature	NOUN
ajst-29869	105	16	are	be	AUX
ajst-29869	105	17	calculated	calculate	VERB
ajst-29869	105	18	for	for	SCONJ
ajst-29869	105	19	each	each	DET
ajst-29869	105	20	weight	weight	NOUN
ajst-29869	105	21	range	range	NOUN
ajst-29869	105	22	to	to	PART
ajst-29869	105	23	select	select	VERB
ajst-29869	105	24	the	the	DET
ajst-29869	105	25	optimal	optimal	ADJ
ajst-29869	105	26	feature	feature	NOUN
ajst-29869	105	27	threshold	threshold	NOUN
ajst-29869	105	28	range	range	NOUN
ajst-29869	105	29	.	.	PUNCT
ajst-29869	106	1	from	from	ADP
ajst-29869	106	2	the	the	DET
ajst-29869	106	3	recognition	recognition	NOUN
ajst-29869	106	4	rate	rate	NOUN
ajst-29869	106	5	results	result	NOUN
ajst-29869	106	6	corresponding	correspond	VERB
ajst-29869	106	7	to	to	ADP
ajst-29869	106	8	the	the	DET
ajst-29869	106	9	different	different	ADJ
ajst-29869	106	10	weight	weight	NOUN
ajst-29869	106	11	ranges	range	VERB
ajst-29869	106	12	in	in	ADP
ajst-29869	106	13	table(3	table(3	PROPN
ajst-29869	106	14	)	)	PUNCT
ajst-29869	106	15	,	,	PUNCT
ajst-29869	106	16	it	it	PRON
ajst-29869	106	17	can	can	AUX
ajst-29869	106	18	be	be	AUX
ajst-29869	106	19	observed	observe	VERB
ajst-29869	106	20	that	that	SCONJ
ajst-29869	106	21	when	when	SCONJ
ajst-29869	106	22	the	the	DET
ajst-29869	106	23	weight	weight	NOUN
ajst-29869	106	24	threshold	threshold	NOUN
ajst-29869	106	25	is	be	AUX
ajst-29869	106	26	0.010	0.010	NUM
ajst-29869	106	27	,	,	PUNCT
ajst-29869	106	28	the	the	DET
ajst-29869	106	29	recognition	recognition	NOUN
ajst-29869	106	30	accuracy	accuracy	NOUN
ajst-29869	106	31	reaches	reach	VERB
ajst-29869	106	32	the	the	DET
ajst-29869	106	33	optimal	optimal	ADJ
ajst-29869	106	34	value	value	NOUN
ajst-29869	106	35	,	,	PUNCT
ajst-29869	106	36	and	and	CCONJ
ajst-29869	106	37	the	the	DET
ajst-29869	106	38	computational	computational	ADJ
ajst-29869	106	39	efficiency	efficiency	NOUN
ajst-29869	106	40	is	be	AUX
ajst-29869	106	41	relatively	relatively	ADV
ajst-29869	106	42	high	high	ADJ
ajst-29869	106	43	.	.	PUNCT
ajst-29869	107	1	this	this	PRON
ajst-29869	107	2	indicates	indicate	VERB
ajst-29869	107	3	that	that	SCONJ
ajst-29869	107	4	within	within	ADP
ajst-29869	107	5	this	this	DET
ajst-29869	107	6	weight	weight	NOUN
ajst-29869	107	7	threshold	threshold	NOUN
ajst-29869	107	8	range	range	NOUN
ajst-29869	107	9	,	,	PUNCT
ajst-29869	107	10	the	the	DET
ajst-29869	107	11	feature	feature	NOUN
ajst-29869	107	12	set	set	NOUN
ajst-29869	107	13	can	can	AUX
ajst-29869	107	14	effectively	effectively	ADV
ajst-29869	107	15	represent	represent	VERB
ajst-29869	107	16	the	the	DET
ajst-29869	107	17	features	feature	NOUN
ajst-29869	107	18	.	.	PUNCT
ajst-29869	108	1	further	further	ADJ
ajst-29869	108	2	analysis	analysis	NOUN
ajst-29869	108	3	reveals	reveal	VERB
ajst-29869	108	4	that	that	SCONJ
ajst-29869	108	5	when	when	SCONJ
ajst-29869	108	6	the	the	DET
ajst-29869	108	7	weight	weight	NOUN
ajst-29869	108	8	threshold	threshold	NOUN
ajst-29869	108	9	increases	increase	VERB
ajst-29869	108	10	to	to	ADP
ajst-29869	108	11	0.015	0.015	NUM
ajst-29869	108	12	or	or	CCONJ
ajst-29869	108	13	0.020	0.020	NUM
ajst-29869	108	14	,	,	PUNCT
ajst-29869	108	15	although	although	SCONJ
ajst-29869	108	16	the	the	DET
ajst-29869	108	17	number	number	NOUN
ajst-29869	108	18	of	of	ADP
ajst-29869	108	19	features	feature	NOUN
ajst-29869	108	20	decreases	decrease	VERB
ajst-29869	108	21	significantly	significantly	ADV
ajst-29869	108	22	,	,	PUNCT
ajst-29869	108	23	the	the	DET
ajst-29869	108	24	recognition	recognition	NOUN
ajst-29869	108	25	accuracy	accuracy	NOUN
ajst-29869	108	26	declines	decline	VERB
ajst-29869	108	27	to	to	ADP
ajst-29869	108	28	varying	vary	VERB
ajst-29869	108	29	degrees	degree	NOUN
ajst-29869	108	30	.	.	PUNCT
ajst-29869	109	1	therefore	therefore	ADV
ajst-29869	109	2	,	,	PUNCT
ajst-29869	109	3	after	after	ADP
ajst-29869	109	4	considering	consider	VERB
ajst-29869	109	5	both	both	PRON
ajst-29869	109	6	recognition	recognition	NOUN
ajst-29869	109	7	accuracy	accuracy	NOUN
ajst-29869	109	8	and	and	CCONJ
ajst-29869	109	9	computational	computational	ADJ
ajst-29869	109	10	efficiency	efficiency	NOUN
ajst-29869	109	11	,	,	PUNCT
ajst-29869	109	12	the	the	DET
ajst-29869	109	13	feature	feature	NOUN
ajst-29869	109	14	set	set	VERB
ajst-29869	109	15	with	with	ADP
ajst-29869	109	16	a	a	DET
ajst-29869	109	17	weight	weight	NOUN
ajst-29869	109	18	threshold	threshold	NOUN
ajst-29869	109	19	of	of	ADP
ajst-29869	109	20	0.010	0.010	NUM
ajst-29869	109	21	is	be	AUX
ajst-29869	109	22	selected	select	VERB
ajst-29869	109	23	as	as	ADP
ajst-29869	109	24	the	the	DET
ajst-29869	109	25	initial	initial	ADJ
ajst-29869	109	26	feature	feature	NOUN
ajst-29869	109	27	set	set	VERB
ajst-29869	109	28	for	for	ADP
ajst-29869	109	29	the	the	DET
ajst-29869	109	30	subsequent	subsequent	ADJ
ajst-29869	109	31	optimal	optimal	ADJ
ajst-29869	109	32	feature	feature	NOUN
ajst-29869	109	33	subset	subset	NOUN
ajst-29869	109	34	,	,	PUNCT
ajst-29869	109	35	with	with	ADP
ajst-29869	109	36	a	a	DET
ajst-29869	109	37	feature	feature	NOUN
ajst-29869	109	38	dimension	dimension	NOUN
ajst-29869	109	39	of	of	ADP
ajst-29869	109	40	28	28	NUM
ajst-29869	109	41	.	.	PUNCT
ajst-29869	110	1	this	this	DET
ajst-29869	110	2	choice	choice	NOUN
ajst-29869	110	3	not	not	PART
ajst-29869	110	4	only	only	ADV
ajst-29869	110	5	ensures	ensure	VERB
ajst-29869	110	6	a	a	DET
ajst-29869	110	7	high	high	ADJ
ajst-29869	110	8	recognition	recognition	NOUN
ajst-29869	110	9	rate	rate	NOUN
ajst-29869	110	10	but	but	CCONJ
ajst-29869	110	11	also	also	ADV
ajst-29869	110	12	significantly	significantly	ADV
ajst-29869	110	13	reduces	reduce	VERB
ajst-29869	110	14	computational	computational	ADJ
ajst-29869	110	15	complexity	complexity	NOUN
ajst-29869	110	16	,	,	PUNCT
ajst-29869	110	17	helping	help	VERB
ajst-29869	110	18	to	to	PART
ajst-29869	110	19	improve	improve	VERB
ajst-29869	110	20	the	the	DET
ajst-29869	110	21	efficiency	efficiency	NOUN
ajst-29869	110	22	and	and	CCONJ
ajst-29869	110	23	accuracy	accuracy	NOUN
ajst-29869	110	24	of	of	ADP
ajst-29869	110	25	subsequent	subsequent	ADJ
ajst-29869	110	26	optimization	optimization	NOUN
ajst-29869	110	27	algorithms	algorithm	NOUN
ajst-29869	110	28	.	.	PUNCT
ajst-29869	111	1	table	table	NOUN
ajst-29869	111	2	3	3	NUM
ajst-29869	111	3	.	.	PUNCT
ajst-29869	112	1	the	the	DET
ajst-29869	112	2	impact	impact	NOUN
ajst-29869	112	3	of	of	ADP
ajst-29869	112	4	different	different	ADJ
ajst-29869	112	5	weight	weight	NOUN
ajst-29869	112	6	thresholds	threshold	NOUN
ajst-29869	112	7	on	on	ADP
ajst-29869	112	8	recognition	recognition	NOUN
ajst-29869	112	9	accuracy	accuracy	NOUN
ajst-29869	112	10	weight	weight	NOUN
ajst-29869	112	11	number	number	NOUN
ajst-29869	112	12	of	of	ADP
ajst-29869	112	13	features	feature	NOUN
ajst-29869	112	14	recognition	recognition	NOUN
ajst-29869	112	15	accuracy	accuracy	NOUN
ajst-29869	112	16	(	(	PUNCT
ajst-29869	112	17	%	%	NOUN
ajst-29869	112	18	)	)	PUNCT
ajst-29869	112	19	time	time	NOUN
ajst-29869	112	20	(	(	PUNCT
ajst-29869	112	21	minutes	minute	NOUN
ajst-29869	112	22	)	)	PUNCT
ajst-29869	112	23	0.005	0.005	NUM
ajst-29869	112	24	33	33	NUM
ajst-29869	112	25	88.1	88.1	NUM
ajst-29869	112	26	591.8	591.8	NUM
ajst-29869	112	27	0.010	0.010	NUM
ajst-29869	112	28	28	28	NUM
ajst-29869	112	29	91.6	91.6	NUM
ajst-29869	112	30	473.5	473.5	NUM
ajst-29869	112	31	0.015	0.015	NUM
ajst-29869	112	32	21	21	NUM
ajst-29869	112	33	86.3	86.3	NUM
ajst-29869	112	34	681.1	681.1	NUM
ajst-29869	112	35	0.020	0.020	NUM
ajst-29869	112	36	12	12	NUM
ajst-29869	112	37	82.1	82.1	NUM
ajst-29869	112	38	737.9	737.9	NUM
ajst-29869	112	39	2.1.5	2.1.5	NUM
ajst-29869	112	40	.	.	PUNCT
ajst-29869	113	1	grey	grey	PROPN
ajst-29869	113	2	wolf	wolf	PROPN
ajst-29869	113	3	optimization	optimization	NOUN
ajst-29869	113	4	algorithm	algorithm	NOUN
ajst-29869	113	5	after	after	SCONJ
ajst-29869	113	6	the	the	DET
ajst-29869	113	7	feature	feature	NOUN
ajst-29869	113	8	subset	subset	NOUN
ajst-29869	113	9	is	be	AUX
ajst-29869	113	10	initially	initially	ADV
ajst-29869	113	11	filtered	filter	VERB
ajst-29869	113	12	through	through	ADP
ajst-29869	113	13	the	the	DET
ajst-29869	113	14	relief	relief	NOUN
ajst-29869	113	15	algorithm	algorithm	NOUN
ajst-29869	113	16	,	,	PUNCT
ajst-29869	113	17	some	some	DET
ajst-29869	113	18	features	feature	NOUN
ajst-29869	113	19	still	still	ADV
ajst-29869	113	20	exhibit	exhibit	VERB
ajst-29869	113	21	high	high	ADJ
ajst-29869	113	22	redundancy	redundancy	NOUN
ajst-29869	113	23	and	and	CCONJ
ajst-29869	113	24	correlation	correlation	NOUN
ajst-29869	113	25	.	.	PUNCT
ajst-29869	114	1	therefore	therefore	ADV
ajst-29869	114	2	,	,	PUNCT
ajst-29869	114	3	the	the	DET
ajst-29869	114	4	grey	grey	ADJ
ajst-29869	114	5	wolf	wolf	PROPN
ajst-29869	114	6	optimization	optimization	NOUN
ajst-29869	114	7	(	(	PUNCT
ajst-29869	114	8	gwo	gwo	NOUN
ajst-29869	114	9	)	)	PUNCT
ajst-29869	114	10	algorithm	algorithm	NOUN
ajst-29869	114	11	is	be	AUX
ajst-29869	114	12	used	use	VERB
ajst-29869	114	13	for	for	ADP
ajst-29869	114	14	further	further	ADJ
ajst-29869	114	15	selection	selection	NOUN
ajst-29869	114	16	and	and	CCONJ
ajst-29869	114	17	optimization	optimization	NOUN
ajst-29869	114	18	of	of	ADP
ajst-29869	114	19	the	the	DET
ajst-29869	114	20	feature	feature	NOUN
ajst-29869	114	21	subset	subset	VERB
ajst-29869	115	1	[	[	X
ajst-29869	115	2	17	17	NUM
ajst-29869	115	3	]	]	PUNCT
ajst-29869	115	4	.	.	PUNCT
ajst-29869	116	1	the	the	DET
ajst-29869	116	2	grey	grey	PROPN
ajst-29869	116	3	wolf	wolf	PROPN
ajst-29869	116	4	optimization	optimization	NOUN
ajst-29869	116	5	(	(	PUNCT
ajst-29869	116	6	gwo	gwo	NOUN
ajst-29869	116	7	)	)	PUNCT
ajst-29869	116	8	algorithm	algorithm	NOUN
ajst-29869	116	9	is	be	AUX
ajst-29869	116	10	a	a	DET
ajst-29869	116	11	metaheuristic	metaheuristic	ADJ
ajst-29869	116	12	optimization	optimization	NOUN
ajst-29869	116	13	algorithm	algorithm	NOUN
ajst-29869	116	14	based	base	VERB
ajst-29869	116	15	on	on	ADP
ajst-29869	116	16	the	the	DET
ajst-29869	116	17	hunting	hunt	VERB
ajst-29869	116	18	behavior	behavior	NOUN
ajst-29869	116	19	of	of	ADP
ajst-29869	116	20	grey	grey	ADJ
ajst-29869	116	21	wolves	wolf	NOUN
ajst-29869	116	22	in	in	ADP
ajst-29869	116	23	nature	nature	NOUN
ajst-29869	116	24	.	.	PUNCT
ajst-29869	117	1	the	the	DET
ajst-29869	117	2	algorithm	algorithm	NOUN
ajst-29869	117	3	simulates	simulate	VERB
ajst-29869	117	4	the	the	DET
ajst-29869	117	5	social	social	ADJ
ajst-29869	117	6	hierarchy	hierarchy	NOUN
ajst-29869	117	7	and	and	CCONJ
ajst-29869	117	8	hunting	hunt	VERB
ajst-29869	117	9	strategy	strategy	NOUN
ajst-29869	117	10	of	of	ADP
ajst-29869	117	11	grey	grey	ADJ
ajst-29869	117	12	wolves	wolf	NOUN
ajst-29869	117	13	,	,	PUNCT
ajst-29869	117	14	dividing	divide	VERB
ajst-29869	117	15	them	they	PRON
ajst-29869	117	16	into	into	ADP
ajst-29869	117	17	four	four	NUM
ajst-29869	117	18	levels	level	NOUN
ajst-29869	117	19	:	:	PUNCT
ajst-29869	117	20	α	α	PRON
ajst-29869	117	21	wolves	wolf	NOUN
ajst-29869	117	22	,	,	PUNCT
ajst-29869	117	23	β	β	NOUN
ajst-29869	117	24	wolves	wolf	NOUN
ajst-29869	117	25	,	,	PUNCT
ajst-29869	117	26	δ	δ	NOUN
ajst-29869	117	27	wolves	wolf	NOUN
ajst-29869	117	28	,	,	PUNCT
ajst-29869	117	29	and	and	CCONJ
ajst-29869	117	30	ω	ω	NUM
ajst-29869	117	31	wolves	wolf	NOUN
ajst-29869	117	32	,	,	PUNCT
ajst-29869	117	33	each	each	PRON
ajst-29869	117	34	representing	represent	VERB
ajst-29869	117	35	different	different	ADJ
ajst-29869	117	36	roles	role	NOUN
ajst-29869	117	37	in	in	ADP
ajst-29869	117	38	the	the	DET
ajst-29869	117	39	population	population	NOUN
ajst-29869	117	40	.	.	PUNCT
ajst-29869	118	1	the	the	DET
ajst-29869	118	2	α	α	PROPN
ajst-29869	118	3	wolf	wolf	NOUN
ajst-29869	118	4	is	be	AUX
ajst-29869	118	5	the	the	DET
ajst-29869	118	6	leader	leader	NOUN
ajst-29869	118	7	of	of	ADP
ajst-29869	118	8	the	the	DET
ajst-29869	118	9	group	group	NOUN
ajst-29869	118	10	,	,	PUNCT
ajst-29869	118	11	responsible	responsible	ADJ
ajst-29869	118	12	for	for	ADP
ajst-29869	118	13	providing	provide	VERB
ajst-29869	118	14	decision	decision	NOUN
ajst-29869	118	15	-	-	PUNCT
ajst-29869	118	16	making	make	VERB
ajst-29869	118	17	direction	direction	NOUN
ajst-29869	118	18	;	;	PUNCT
ajst-29869	118	19	the	the	DET
ajst-29869	118	20	β	β	PROPN
ajst-29869	118	21	wolf	wolf	PROPN
ajst-29869	118	22	is	be	AUX
ajst-29869	118	23	the	the	DET
ajst-29869	118	24	secondary	secondary	ADJ
ajst-29869	118	25	leader	leader	NOUN
ajst-29869	118	26	,	,	PUNCT
ajst-29869	118	27	assisting	assist	VERB
ajst-29869	118	28	the	the	DET
ajst-29869	118	29	α	α	PRON
ajst-29869	118	30	wolf	wolf	NOUN
ajst-29869	118	31	in	in	ADP
ajst-29869	118	32	decision	decision	NOUN
ajst-29869	118	33	-	-	PUNCT
ajst-29869	118	34	making	making	NOUN
ajst-29869	118	35	;	;	PUNCT
ajst-29869	118	36	the	the	DET
ajst-29869	118	37	δ	δ	PROPN
ajst-29869	118	38	wolf	wolf	PROPN
ajst-29869	118	39	maintains	maintain	VERB
ajst-29869	118	40	the	the	DET
ajst-29869	118	41	group	group	NOUN
ajst-29869	118	42	’s	’s	PART
ajst-29869	118	43	order	order	NOUN
ajst-29869	118	44	;	;	PUNCT
ajst-29869	118	45	the	the	DET
ajst-29869	118	46	ω	ω	PROPN
ajst-29869	118	47	wolf	wolf	NOUN
ajst-29869	118	48	is	be	AUX
ajst-29869	118	49	a	a	DET
ajst-29869	118	50	common	common	ADJ
ajst-29869	118	51	member	member	NOUN
ajst-29869	118	52	,	,	PUNCT
ajst-29869	118	53	responsible	responsible	ADJ
ajst-29869	118	54	for	for	ADP
ajst-29869	118	55	executing	execute	VERB
ajst-29869	118	56	tasks	task	NOUN
ajst-29869	118	57	.	.	PUNCT
ajst-29869	119	1	in	in	ADP
ajst-29869	119	2	the	the	DET
ajst-29869	119	3	gwo	gwo	PROPN
ajst-29869	119	4	algorithm	algorithm	PROPN
ajst-29869	119	5	,	,	PUNCT
ajst-29869	119	6	the	the	DET
ajst-29869	119	7	population	population	NOUN
ajst-29869	119	8	's	's	PART
ajst-29869	119	9	search	search	NOUN
ajst-29869	119	10	process	process	NOUN
ajst-29869	119	11	is	be	AUX
ajst-29869	119	12	mainly	mainly	ADV
ajst-29869	119	13	divided	divide	VERB
ajst-29869	119	14	into	into	ADP
ajst-29869	119	15	three	three	NUM
ajst-29869	119	16	stages	stage	NOUN
ajst-29869	119	17	:	:	PUNCT
ajst-29869	119	18	surrounding	surround	VERB
ajst-29869	119	19	,	,	PUNCT
ajst-29869	119	20	hunting	hunting	NOUN
ajst-29869	119	21	,	,	PUNCT
ajst-29869	119	22	and	and	CCONJ
ajst-29869	119	23	attacking	attack	VERB
ajst-29869	119	24	,	,	PUNCT
ajst-29869	119	25	dynamically	dynamically	ADV
ajst-29869	119	26	adjusting	adjust	VERB
ajst-29869	119	27	the	the	DET
ajst-29869	119	28	search	search	NOUN
ajst-29869	119	29	range	range	NOUN
ajst-29869	119	30	and	and	CCONJ
ajst-29869	119	31	direction	direction	NOUN
ajst-29869	119	32	to	to	PART
ajst-29869	119	33	gradually	gradually	ADV
ajst-29869	119	34	approach	approach	VERB
ajst-29869	119	35	the	the	DET
ajst-29869	119	36	global	global	ADJ
ajst-29869	119	37	optimal	optimal	ADJ
ajst-29869	119	38	solution	solution	NOUN
ajst-29869	119	39	[	[	X
ajst-29869	119	40	18	18	NUM
ajst-29869	119	41	]	]	PUNCT
ajst-29869	119	42	.	.	PUNCT
ajst-29869	120	1	the	the	DET
ajst-29869	120	2	core	core	ADJ
ajst-29869	120	3	idea	idea	NOUN
ajst-29869	120	4	of	of	ADP
ajst-29869	120	5	the	the	DET
ajst-29869	120	6	gwo	gwo	PROPN
ajst-29869	120	7	algorithm	algorithm	NOUN
ajst-29869	120	8	is	be	AUX
ajst-29869	120	9	to	to	PART
ajst-29869	120	10	simulate	simulate	VERB
ajst-29869	120	11	the	the	DET
ajst-29869	120	12	hunting	hunt	VERB
ajst-29869	120	13	behavior	behavior	NOUN
ajst-29869	120	14	of	of	ADP
ajst-29869	120	15	grey	grey	ADJ
ajst-29869	120	16	wolf	wolf	PROPN
ajst-29869	120	17	groups	group	NOUN
ajst-29869	120	18	to	to	PART
ajst-29869	120	19	find	find	VERB
ajst-29869	120	20	the	the	DET
ajst-29869	120	21	optimal	optimal	ADJ
ajst-29869	120	22	solution	solution	NOUN
ajst-29869	120	23	.	.	PUNCT
ajst-29869	121	1	in	in	ADP
ajst-29869	121	2	the	the	DET
ajst-29869	121	3	solution	solution	NOUN
ajst-29869	121	4	space	space	NOUN
ajst-29869	121	5	,	,	PUNCT
ajst-29869	121	6	each	each	DET
ajst-29869	121	7	grey	grey	ADJ
ajst-29869	121	8	wolf	wolf	NOUN
ajst-29869	121	9	represents	represent	VERB
ajst-29869	121	10	a	a	DET
ajst-29869	121	11	candidate	candidate	NOUN
ajst-29869	121	12	solution	solution	NOUN
ajst-29869	121	13	,	,	PUNCT
ajst-29869	121	14	and	and	CCONJ
ajst-29869	121	15	its	its	PRON
ajst-29869	121	16	position	position	NOUN
ajst-29869	121	17	represents	represent	VERB
ajst-29869	121	18	a	a	DET
ajst-29869	121	19	feature	feature	NOUN
ajst-29869	121	20	subset	subset	NOUN
ajst-29869	121	21	.	.	PUNCT
ajst-29869	122	1	by	by	ADP
ajst-29869	122	2	dynamically	dynamically	ADV
ajst-29869	122	3	adjusting	adjust	VERB
ajst-29869	122	4	the	the	DET
ajst-29869	122	5	grey	grey	ADJ
ajst-29869	122	6	wolves	wolf	NOUN
ajst-29869	122	7	'	'	PART
ajst-29869	122	8	positions	position	NOUN
ajst-29869	122	9	,	,	PUNCT
ajst-29869	122	10	the	the	DET
ajst-29869	122	11	algorithm	algorithm	NOUN
ajst-29869	122	12	gradually	gradually	ADV
ajst-29869	122	13	approaches	approach	VERB
ajst-29869	122	14	the	the	DET
ajst-29869	122	15	optimal	optimal	ADJ
ajst-29869	122	16	feature	feature	NOUN
ajst-29869	122	17	subset	subset	NOUN
ajst-29869	122	18	.	.	PUNCT
ajst-29869	123	1	the	the	DET
ajst-29869	123	2	algorithm	algorithm	NOUN
ajst-29869	123	3	updates	update	VERB
ajst-29869	123	4	the	the	DET
ajst-29869	123	5	positions	position	NOUN
ajst-29869	123	6	of	of	ADP
ajst-29869	123	7	the	the	DET
ajst-29869	123	8	α	α	PROPN
ajst-29869	123	9	,	,	PUNCT
ajst-29869	123	10	β	β	NOUN
ajst-29869	123	11	,	,	PUNCT
ajst-29869	123	12	and	and	CCONJ
ajst-29869	123	13	δ	δ	PROPN
ajst-29869	123	14	wolves	wolf	NOUN
ajst-29869	123	15	as	as	ADP
ajst-29869	123	16	reference	reference	NOUN
ajst-29869	123	17	points	point	NOUN
ajst-29869	123	18	,	,	PUNCT
ajst-29869	123	19	guiding	guide	VERB
ajst-29869	123	20	other	other	ADJ
ajst-29869	123	21	grey	grey	ADJ
ajst-29869	123	22	wolves	wolf	NOUN
ajst-29869	123	23	to	to	PART
ajst-29869	123	24	update	update	VERB
ajst-29869	123	25	their	their	PRON
ajst-29869	123	26	positions	position	NOUN
ajst-29869	123	27	,	,	PUNCT
ajst-29869	123	28	causing	cause	VERB
ajst-29869	123	29	the	the	DET
ajst-29869	123	30	population	population	NOUN
ajst-29869	123	31	to	to	PART
ajst-29869	123	32	converge	converge	VERB
ajst-29869	123	33	toward	toward	ADP
ajst-29869	123	34	the	the	DET
ajst-29869	123	35	optimal	optimal	ADJ
ajst-29869	123	36	solution	solution	NOUN
ajst-29869	123	37	.	.	PUNCT
ajst-29869	124	1	gwo	gwo	PROPN
ajst-29869	124	2	has	have	VERB
ajst-29869	124	3	the	the	DET
ajst-29869	124	4	advantages	advantage	NOUN
ajst-29869	124	5	of	of	ADP
ajst-29869	124	6	being	be	AUX
ajst-29869	124	7	simple	simple	ADJ
ajst-29869	124	8	to	to	PART
ajst-29869	124	9	implement	implement	VERB
ajst-29869	124	10	,	,	PUNCT
ajst-29869	124	11	fast	fast	ADJ
ajst-29869	124	12	convergence	convergence	NOUN
ajst-29869	124	13	speed	speed	NOUN
ajst-29869	124	14	,	,	PUNCT
ajst-29869	124	15	and	and	CCONJ
ajst-29869	124	16	strong	strong	ADJ
ajst-29869	124	17	global	global	ADJ
ajst-29869	124	18	search	search	NOUN
ajst-29869	124	19	capability	capability	NOUN
ajst-29869	124	20	.	.	PUNCT
ajst-29869	125	1	it	it	PRON
ajst-29869	125	2	has	have	AUX
ajst-29869	125	3	been	be	AUX
ajst-29869	125	4	widely	widely	ADV
ajst-29869	125	5	applied	apply	VERB
ajst-29869	125	6	in	in	ADP
ajst-29869	125	7	feature	feature	NOUN
ajst-29869	125	8	selection	selection	NOUN
ajst-29869	125	9	,	,	PUNCT
ajst-29869	125	10	image	image	NOUN
ajst-29869	125	11	processing	processing	NOUN
ajst-29869	125	12	,	,	PUNCT
ajst-29869	125	13	path	path	NOUN
ajst-29869	125	14	planning	planning	NOUN
ajst-29869	125	15	,	,	PUNCT
ajst-29869	125	16	and	and	CCONJ
ajst-29869	125	17	machine	machine	NOUN
ajst-29869	125	18	learning	learning	NOUN
ajst-29869	125	19	.	.	PUNCT
ajst-29869	126	1	in	in	ADP
ajst-29869	126	2	the	the	DET
ajst-29869	126	3	gwo	gwo	PROPN
ajst-29869	126	4	algorithm	algorithm	PROPN
ajst-29869	126	5	,	,	PUNCT
ajst-29869	126	6	the	the	DET
ajst-29869	126	7	search	search	NOUN
ajst-29869	126	8	behavior	behavior	NOUN
ajst-29869	126	9	of	of	ADP
ajst-29869	126	10	the	the	DET
ajst-29869	126	11	population	population	NOUN
ajst-29869	126	12	is	be	AUX
ajst-29869	126	13	achieved	achieve	VERB
ajst-29869	126	14	by	by	ADP
ajst-29869	126	15	surrounding	surround	VERB
ajst-29869	126	16	and	and	CCONJ
ajst-29869	126	17	attacking	attack	VERB
ajst-29869	126	18	prey	prey	NOUN
ajst-29869	126	19	.	.	PUNCT
ajst-29869	127	1	the	the	DET
ajst-29869	127	2	position	position	NOUN
ajst-29869	127	3	update	update	NOUN
ajst-29869	127	4	formula	formula	NOUN
ajst-29869	127	5	is	be	AUX
ajst-29869	127	6	based	base	VERB
ajst-29869	127	7	on	on	ADP
ajst-29869	127	8	the	the	DET
ajst-29869	127	9	prey	prey	NOUN
ajst-29869	127	10	's	's	PART
ajst-29869	127	11	position	position	NOUN
ajst-29869	127	12	and	and	CCONJ
ajst-29869	127	13	the	the	DET
ajst-29869	127	14	average	average	ADJ
ajst-29869	127	15	distance	distance	NOUN
ajst-29869	127	16	of	of	ADP
ajst-29869	127	17	other	other	ADJ
ajst-29869	127	18	wolves	wolf	NOUN
ajst-29869	127	19	in	in	ADP
ajst-29869	127	20	the	the	DET
ajst-29869	127	21	group	group	NOUN
ajst-29869	127	22	,	,	PUNCT
ajst-29869	127	23	iteratively	iteratively	ADV
ajst-29869	127	24	adjusting	adjust	VERB
ajst-29869	127	25	positions	position	NOUN
ajst-29869	127	26	to	to	PART
ajst-29869	127	27	achieve	achieve	VERB
ajst-29869	127	28	efficient	efficient	ADJ
ajst-29869	127	29	search	search	NOUN
ajst-29869	127	30	in	in	ADP
ajst-29869	127	31	the	the	DET
ajst-29869	127	32	solution	solution	NOUN
ajst-29869	127	33	space	space	NOUN
ajst-29869	127	34	.	.	PUNCT
ajst-29869	128	1	although	although	SCONJ
ajst-29869	128	2	gwo	gwo	PROPN
ajst-29869	128	3	has	have	VERB
ajst-29869	128	4	strong	strong	ADJ
ajst-29869	128	5	global	global	ADJ
ajst-29869	128	6	search	search	NOUN
ajst-29869	128	7	ability	ability	NOUN
ajst-29869	128	8	,	,	PUNCT
ajst-29869	128	9	its	its	PRON
ajst-29869	128	10	performance	performance	NOUN
ajst-29869	128	11	depends	depend	VERB
ajst-29869	128	12	on	on	ADP
ajst-29869	128	13	the	the	DET
ajst-29869	128	14	specific	specific	ADJ
ajst-29869	128	15	problem	problem	NOUN
ajst-29869	128	16	and	and	CCONJ
ajst-29869	128	17	requires	require	VERB
ajst-29869	128	18	parameter	parameter	NOUN
ajst-29869	128	19	tuning	tuning	NOUN
ajst-29869	128	20	based	base	VERB
ajst-29869	128	21	on	on	ADP
ajst-29869	128	22	the	the	DET
ajst-29869	128	23	actual	actual	ADJ
ajst-29869	128	24	scenario	scenario	NOUN
ajst-29869	128	25	.	.	PUNCT
ajst-29869	129	1	for	for	ADP
ajst-29869	129	2	example	example	NOUN
ajst-29869	129	3	,	,	PUNCT
ajst-29869	129	4	controlling	control	VERB
ajst-29869	129	5	the	the	DET
ajst-29869	129	6	dynamic	dynamic	ADJ
ajst-29869	129	7	weight	weight	NOUN
ajst-29869	129	8	of	of	ADP
ajst-29869	129	9	the	the	DET
ajst-29869	129	10	search	search	NOUN
ajst-29869	129	11	factors	factor	NOUN
ajst-29869	129	12	can	can	AUX
ajst-29869	129	13	effectively	effectively	ADV
ajst-29869	129	14	balance	balance	VERB
ajst-29869	129	15	global	global	ADJ
ajst-29869	129	16	and	and	CCONJ
ajst-29869	129	17	local	local	ADJ
ajst-29869	129	18	search	search	NOUN
ajst-29869	129	19	capabilities	capability	NOUN
ajst-29869	129	20	,	,	PUNCT
ajst-29869	129	21	improving	improve	VERB
ajst-29869	129	22	the	the	DET
ajst-29869	129	23	algorithm	algorithm	NOUN
ajst-29869	129	24	's	's	PART
ajst-29869	129	25	convergence	convergence	NOUN
ajst-29869	129	26	speed	speed	NOUN
ajst-29869	129	27	and	and	CCONJ
ajst-29869	129	28	optimization	optimization	NOUN
ajst-29869	129	29	effect	effect	NOUN
ajst-29869	129	30	.	.	PUNCT
ajst-29869	130	1	in	in	ADP
ajst-29869	130	2	this	this	DET
ajst-29869	130	3	paper	paper	NOUN
ajst-29869	130	4	,	,	PUNCT
ajst-29869	130	5	a	a	DET
ajst-29869	130	6	combination	combination	NOUN
ajst-29869	130	7	of	of	ADP
ajst-29869	130	8	the	the	DET
ajst-29869	130	9	random	random	ADJ
ajst-29869	130	10	forest	forest	NOUN
ajst-29869	130	11	algorithm	algorithm	NOUN
ajst-29869	130	12	and	and	CCONJ
ajst-29869	130	13	gwo	gwo	PROPN
ajst-29869	130	14	(	(	PUNCT
ajst-29869	130	15	gwo_rf	gwo_rf	PROPN
ajst-29869	130	16	)	)	PUNCT
ajst-29869	130	17	is	be	AUX
ajst-29869	130	18	proposed	propose	VERB
ajst-29869	130	19	,	,	PUNCT
ajst-29869	130	20	using	use	VERB
ajst-29869	130	21	the	the	DET
ajst-29869	130	22	random	random	ADJ
ajst-29869	130	23	forest	forest	NOUN
ajst-29869	130	24	classifier	classifier	NOUN
ajst-29869	130	25	as	as	SCONJ
ajst-29869	130	26	the	the	DET
ajst-29869	130	27	fitness	fitness	NOUN
ajst-29869	130	28	function	function	NOUN
ajst-29869	130	29	for	for	ADP
ajst-29869	130	30	the	the	DET
ajst-29869	130	31	gwo	gwo	PROPN
ajst-29869	130	32	algorithm	algorithm	PROPN
ajst-29869	130	33	to	to	PART
ajst-29869	130	34	evaluate	evaluate	VERB
ajst-29869	130	35	the	the	DET
ajst-29869	130	36	feature	feature	NOUN
ajst-29869	130	37	subsets	subset	NOUN
ajst-29869	130	38	.	.	PUNCT
ajst-29869	131	1	specifically	specifically	ADV
ajst-29869	131	2	,	,	PUNCT
ajst-29869	131	3	the	the	DET
ajst-29869	131	4	gwo	gwo	PROPN
ajst-29869	131	5	algorithm	algorithm	PROPN
ajst-29869	131	6	selects	select	VERB
ajst-29869	131	7	a	a	DET
ajst-29869	131	8	feature	feature	NOUN
ajst-29869	131	9	subset	subset	VERB
ajst-29869	131	10	during	during	ADP
ajst-29869	131	11	each	each	DET
ajst-29869	131	12	iteration	iteration	NOUN
ajst-29869	131	13	,	,	PUNCT
ajst-29869	131	14	inputs	input	VERB
ajst-29869	131	15	it	it	PRON
ajst-29869	131	16	into	into	ADP
ajst-29869	131	17	the	the	DET
ajst-29869	131	18	random	random	ADJ
ajst-29869	131	19	forest	forest	NOUN
ajst-29869	131	20	classifier	classifier	NOUN
ajst-29869	131	21	for	for	ADP
ajst-29869	131	22	classification	classification	NOUN
ajst-29869	131	23	,	,	PUNCT
ajst-29869	131	24	and	and	CCONJ
ajst-29869	131	25	uses	use	VERB
ajst-29869	131	26	classification	classification	NOUN
ajst-29869	131	27	accuracy	accuracy	NOUN
ajst-29869	131	28	as	as	ADP
ajst-29869	131	29	the	the	DET
ajst-29869	131	30	fitness	fitness	NOUN
ajst-29869	131	31	function	function	NOUN
ajst-29869	131	32	’s	’s	PART
ajst-29869	131	33	objective	objective	ADJ
ajst-29869	131	34	value	value	NOUN
ajst-29869	131	35	.	.	PUNCT
ajst-29869	132	1	based	base	VERB
ajst-29869	132	2	on	on	ADP
ajst-29869	132	3	the	the	DET
ajst-29869	132	4	fitness	fitness	NOUN
ajst-29869	132	5	values	value	NOUN
ajst-29869	132	6	,	,	PUNCT
ajst-29869	132	7	the	the	DET
ajst-29869	132	8	gwo	gwo	PROPN
ajst-29869	132	9	algorithm	algorithm	PROPN
ajst-29869	132	10	adjusts	adjust	VERB
ajst-29869	132	11	the	the	DET
ajst-29869	132	12	position	position	NOUN
ajst-29869	132	13	of	of	ADP
ajst-29869	132	14	each	each	DET
ajst-29869	132	15	grey	grey	ADJ
ajst-29869	132	16	wolf	wolf	NOUN
ajst-29869	132	17	,	,	PUNCT
ajst-29869	132	18	optimizing	optimize	VERB
ajst-29869	132	19	the	the	DET
ajst-29869	132	20	feature	feature	NOUN
ajst-29869	132	21	subset	subset	NOUN
ajst-29869	132	22	selection	selection	NOUN
ajst-29869	132	23	process	process	NOUN
ajst-29869	132	24	.	.	PUNCT
ajst-29869	133	1	the	the	DET
ajst-29869	133	2	gwo_rf	gwo_rf	PROPN
ajst-29869	133	3	algorithm	algorithm	NOUN
ajst-29869	133	4	not	not	PART
ajst-29869	133	5	only	only	ADV
ajst-29869	133	6	improves	improve	VERB
ajst-29869	133	7	feature	feature	NOUN
ajst-29869	133	8	selection	selection	NOUN
ajst-29869	133	9	efficiency	efficiency	NOUN
ajst-29869	133	10	but	but	CCONJ
ajst-29869	133	11	also	also	ADV
ajst-29869	133	12	reduces	reduce	VERB
ajst-29869	133	13	the	the	DET
ajst-29869	133	14	classifier	classifier	NOUN
ajst-29869	133	15	's	's	PART
ajst-29869	133	16	computational	computational	ADJ
ajst-29869	133	17	complexity	complexity	NOUN
ajst-29869	133	18	,	,	PUNCT
ajst-29869	133	19	ultimately	ultimately	ADV
ajst-29869	133	20	obtaining	obtain	VERB
ajst-29869	133	21	the	the	DET
ajst-29869	133	22	optimal	optimal	ADJ
ajst-29869	133	23	feature	feature	NOUN
ajst-29869	133	24	subset	subset	NOUN
ajst-29869	133	25	.	.	PUNCT
ajst-29869	134	1	the	the	DET
ajst-29869	134	2	flowchart	flowchart	NOUN
ajst-29869	134	3	of	of	ADP
ajst-29869	134	4	the	the	DET
ajst-29869	134	5	gwo_rf	gwo_rf	PROPN
ajst-29869	134	6	algorithm	algorithm	NOUN
ajst-29869	134	7	is	be	AUX
ajst-29869	134	8	shown	show	VERB
ajst-29869	134	9	in	in	ADP
ajst-29869	134	10	figure	figure	NOUN
ajst-29869	134	11	2	2	NUM
ajst-29869	134	12	:	:	PUNCT
ajst-29869	134	13	(	(	PUNCT
ajst-29869	134	14	1	1	X
ajst-29869	134	15	)	)	PUNCT
ajst-29869	134	16	initialize	initialize	VERB
ajst-29869	134	17	the	the	DET
ajst-29869	134	18	population	population	NOUN
ajst-29869	134	19	:	:	PUNCT
ajst-29869	134	20	randomly	randomly	ADV
ajst-29869	134	21	generate	generate	VERB
ajst-29869	134	22	a	a	DET
ajst-29869	134	23	certain	certain	ADJ
ajst-29869	134	24	number	number	NOUN
ajst-29869	134	25	of	of	ADP
ajst-29869	134	26	grey	grey	ADJ
ajst-29869	134	27	wolves	wolf	NOUN
ajst-29869	134	28	,	,	PUNCT
ajst-29869	134	29	where	where	SCONJ
ajst-29869	134	30	each	each	DET
ajst-29869	134	31	grey	grey	ADJ
ajst-29869	134	32	wolf	wolf	PROPN
ajst-29869	134	33	’s	’s	PART
ajst-29869	134	34	position	position	NOUN
ajst-29869	134	35	represents	represent	VERB
ajst-29869	134	36	a	a	DET
ajst-29869	134	37	feature	feature	NOUN
ajst-29869	134	38	subset	subset	NOUN
ajst-29869	134	39	.	.	PUNCT
ajst-29869	135	1	each	each	DET
ajst-29869	135	2	element	element	NOUN
ajst-29869	135	3	in	in	ADP
ajst-29869	135	4	the	the	DET
ajst-29869	135	5	position	position	NOUN
ajst-29869	135	6	vector	vector	NOUN
ajst-29869	135	7	corresponds	correspond	VERB
ajst-29869	135	8	to	to	ADP
ajst-29869	135	9	a	a	DET
ajst-29869	135	10	feature	feature	NOUN
ajst-29869	135	11	,	,	PUNCT
ajst-29869	135	12	with	with	ADP
ajst-29869	135	13	values	value	NOUN
ajst-29869	135	14	of	of	ADP
ajst-29869	135	15	0	0	NUM
ajst-29869	135	16	or	or	CCONJ
ajst-29869	135	17	1	1	NUM
ajst-29869	135	18	indicating	indicate	VERB
ajst-29869	135	19	whether	whether	SCONJ
ajst-29869	135	20	the	the	DET
ajst-29869	135	21	feature	feature	NOUN
ajst-29869	135	22	is	be	AUX
ajst-29869	135	23	selected	select	VERB
ajst-29869	135	24	.	.	PUNCT
ajst-29869	136	1	the	the	DET
ajst-29869	136	2	population	population	NOUN
ajst-29869	136	3	size	size	NOUN
ajst-29869	136	4	,	,	PUNCT
ajst-29869	136	5	maximum	maximum	ADJ
ajst-29869	136	6	iteration	iteration	NOUN
ajst-29869	136	7	count	count	NOUN
ajst-29869	136	8	,	,	PUNCT
ajst-29869	136	9	and	and	CCONJ
ajst-29869	136	10	relevant	relevant	ADJ
ajst-29869	136	11	parameters	parameter	NOUN
ajst-29869	136	12	(	(	PUNCT
ajst-29869	136	13	such	such	ADJ
ajst-29869	136	14	as	as	ADP
ajst-29869	136	15	a	a	PRON
ajst-29869	136	16	,	,	PUNCT
ajst-29869	136	17	a	a	PRON
ajst-29869	136	18	,	,	PUNCT
ajst-29869	136	19	c	c	NOUN
ajst-29869	136	20	,	,	PUNCT
ajst-29869	136	21	etc	etc	X
ajst-29869	136	22	.	.	X
ajst-29869	136	23	)	)	PUNCT
ajst-29869	136	24	are	be	AUX
ajst-29869	136	25	also	also	ADV
ajst-29869	136	26	set	set	VERB
ajst-29869	136	27	.	.	PUNCT
ajst-29869	137	1	(	(	PUNCT
ajst-29869	137	2	2	2	X
ajst-29869	137	3	)	)	PUNCT
ajst-29869	137	4	calculate	calculate	NOUN
ajst-29869	137	5	fitness	fitness	NOUN
ajst-29869	137	6	:	:	PUNCT
ajst-29869	137	7	use	use	VERB
ajst-29869	137	8	the	the	DET
ajst-29869	137	9	random	random	ADJ
ajst-29869	137	10	forest	forest	NOUN
ajst-29869	137	11	algorithm	algorithm	NOUN
ajst-29869	137	12	to	to	PART
ajst-29869	137	13	evaluate	evaluate	VERB
ajst-29869	137	14	the	the	DET
ajst-29869	137	15	classification	classification	NOUN
ajst-29869	137	16	accuracy	accuracy	NOUN
ajst-29869	137	17	of	of	ADP
ajst-29869	137	18	each	each	DET
ajst-29869	137	19	grey	grey	ADJ
ajst-29869	137	20	wolf	wolf	PROPN
ajst-29869	137	21	’s	’s	PART
ajst-29869	137	22	corresponding	corresponding	ADJ
ajst-29869	137	23	feature	feature	NOUN
ajst-29869	137	24	subset	subset	NOUN
ajst-29869	137	25	,	,	PUNCT
ajst-29869	137	26	treating	treat	VERB
ajst-29869	137	27	the	the	DET
ajst-29869	137	28	classification	classification	NOUN
ajst-29869	137	29	accuracy	accuracy	NOUN
ajst-29869	137	30	as	as	ADP
ajst-29869	137	31	the	the	DET
ajst-29869	137	32	grey	grey	PROPN
ajst-29869	137	33	wolf	wolf	PROPN
ajst-29869	137	34	’s	’s	PART
ajst-29869	137	35	fitness	fitness	NOUN
ajst-29869	137	36	value	value	NOUN
ajst-29869	137	37	.	.	PUNCT
ajst-29869	138	1	a	a	DET
ajst-29869	138	2	higher	high	ADJ
ajst-29869	138	3	fitness	fitness	NOUN
ajst-29869	138	4	value	value	NOUN
ajst-29869	138	5	indicates	indicate	VERB
ajst-29869	138	6	stronger	strong	ADJ
ajst-29869	138	7	classification	classification	NOUN
ajst-29869	138	8	capability	capability	NOUN
ajst-29869	138	9	of	of	ADP
ajst-29869	138	10	the	the	DET
ajst-29869	138	11	feature	feature	NOUN
ajst-29869	138	12	subset	subset	NOUN
ajst-29869	138	13	.	.	PUNCT
ajst-29869	139	1	(	(	PUNCT
ajst-29869	139	2	3	3	X
ajst-29869	139	3	)	)	PUNCT
ajst-29869	139	4	select	select	ADJ
ajst-29869	139	5	α	α	X
ajst-29869	139	6	,	,	PUNCT
ajst-29869	139	7	β	β	NOUN
ajst-29869	139	8	,	,	PUNCT
ajst-29869	139	9	and	and	CCONJ
ajst-29869	139	10	δ	δ	PROPN
ajst-29869	139	11	wolves	wolf	NOUN
ajst-29869	139	12	:	:	PUNCT
ajst-29869	139	13	sort	sort	VERB
ajst-29869	139	14	the	the	DET
ajst-29869	139	15	population	population	NOUN
ajst-29869	139	16	based	base	VERB
ajst-29869	139	17	on	on	ADP
ajst-29869	139	18	the	the	DET
ajst-29869	139	19	fitness	fitness	NOUN
ajst-29869	139	20	values	value	NOUN
ajst-29869	139	21	of	of	ADP
ajst-29869	139	22	the	the	DET
ajst-29869	139	23	grey	grey	ADJ
ajst-29869	139	24	wolves	wolf	NOUN
ajst-29869	139	25	,	,	PUNCT
ajst-29869	139	26	selecting	select	VERB
ajst-29869	139	27	the	the	DET
ajst-29869	139	28	three	three	NUM
ajst-29869	139	29	wolves	wolf	NOUN
ajst-29869	139	30	with	with	ADP
ajst-29869	139	31	the	the	DET
ajst-29869	139	32	highest	high	ADJ
ajst-29869	139	33	fitness	fitness	NOUN
ajst-29869	139	34	values	value	NOUN
ajst-29869	139	35	as	as	ADP
ajst-29869	139	36	the	the	DET
ajst-29869	139	37	α	α	NOUN
ajst-29869	139	38	(	(	PUNCT
ajst-29869	139	39	optimal	optimal	ADJ
ajst-29869	139	40	solution	solution	NOUN
ajst-29869	139	41	)	)	PUNCT
ajst-29869	139	42	,	,	PUNCT
ajst-29869	139	43	β	β	X
ajst-29869	139	44	(	(	PUNCT
ajst-29869	139	45	second	second	ADV
ajst-29869	139	46	-	-	PUNCT
ajst-29869	139	47	best	good	ADJ
ajst-29869	139	48	solution	solution	NOUN
ajst-29869	139	49	)	)	PUNCT
ajst-29869	139	50	,	,	PUNCT
ajst-29869	139	51	and	and	CCONJ
ajst-29869	139	52	δ	δ	PROPN
ajst-29869	139	53	(	(	PUNCT
ajst-29869	139	54	third	third	ADV
ajst-29869	139	55	-	-	PUNCT
ajst-29869	139	56	best	good	ADJ
ajst-29869	139	57	solution	solution	NOUN
ajst-29869	139	58	)	)	PUNCT
ajst-29869	139	59	wolves	wolf	NOUN
ajst-29869	139	60	.	.	PUNCT
ajst-29869	140	1	the	the	DET
ajst-29869	140	2	remaining	remain	VERB
ajst-29869	140	3	wolves	wolf	NOUN
ajst-29869	140	4	are	be	AUX
ajst-29869	140	5	considered	consider	VERB
ajst-29869	140	6	ordinary	ordinary	ADJ
ajst-29869	140	7	members	member	NOUN
ajst-29869	140	8	.	.	PUNCT
ajst-29869	141	1	(	(	PUNCT
ajst-29869	141	2	4	4	X
ajst-29869	141	3	)	)	PUNCT
ajst-29869	141	4	update	update	VERB
ajst-29869	141	5	the	the	DET
ajst-29869	141	6	grey	grey	ADJ
ajst-29869	141	7	wolf	wolf	PROPN
ajst-29869	141	8	positions	position	NOUN
ajst-29869	141	9	:	:	PUNCT
ajst-29869	141	10	the	the	DET
ajst-29869	141	11	positions	position	NOUN
ajst-29869	141	12	of	of	ADP
ajst-29869	141	13	ordinary	ordinary	ADJ
ajst-29869	141	14	grey	grey	ADJ
ajst-29869	141	15	wolves	wolf	NOUN
ajst-29869	141	16	are	be	AUX
ajst-29869	141	17	updated	update	VERB
ajst-29869	141	18	based	base	VERB
ajst-29869	141	19	on	on	ADP
ajst-29869	141	20	the	the	DET
ajst-29869	141	21	positions	position	NOUN
ajst-29869	141	22	of	of	ADP
ajst-29869	141	23	the	the	DET
ajst-29869	141	24	α	α	PROPN
ajst-29869	141	25	,	,	PUNCT
ajst-29869	141	26	β	β	NOUN
ajst-29869	141	27	,	,	PUNCT
ajst-29869	141	28	and	and	CCONJ
ajst-29869	141	29	δ	δ	PROPN
ajst-29869	141	30	wolves	wolf	NOUN
ajst-29869	141	31	,	,	PUNCT
ajst-29869	141	32	as	as	ADV
ajst-29869	141	33	well	well	ADV
ajst-29869	141	34	as	as	ADP
ajst-29869	141	35	the	the	DET
ajst-29869	141	36	dynamically	dynamically	ADV
ajst-29869	141	37	adjusted	adjust	VERB
ajst-29869	141	38	coefficients	coefficient	NOUN
ajst-29869	141	39	a	a	PRON
ajst-29869	141	40	and	and	CCONJ
ajst-29869	141	41	a	a	PRON
ajst-29869	141	42	,	,	PUNCT
ajst-29869	141	43	using	use	VERB
ajst-29869	141	44	the	the	DET
ajst-29869	141	45	following	follow	VERB
ajst-29869	141	46	formula	formula	NOUN
ajst-29869	141	47	:	:	PUNCT
ajst-29869	141	48	𝐷	𝐷	PROPN
ajst-29869	141	49	𝐶	𝐶	PROPN
ajst-29869	141	50	∙	∙	PROPN
ajst-29869	141	51	�	�	PROPN
ajst-29869	141	52	⃗	⃗	PROPN
ajst-29869	141	53	�	�	PROPN
ajst-29869	141	54	�	�	PROPN
ajst-29869	141	55	⃗	⃗	NOUN
ajst-29869	141	56	�	�	PROPN
ajst-29869	141	57	3	3	NUM
ajst-29869	141	58	𝐷	𝐷	PROPN
ajst-29869	141	59	𝐶	𝐶	PROPN
ajst-29869	141	60	∙	∙	PROPN
ajst-29869	141	61	�	�	PROPN
ajst-29869	141	62	⃗	⃗	PROPN
ajst-29869	141	63	�	�	PROPN
ajst-29869	141	64	�	�	PROPN
ajst-29869	141	65	⃗	⃗	NOUN
ajst-29869	141	66	�	�	PROPN
ajst-29869	141	67	4	4	NUM
ajst-29869	141	68	𝐷	𝐷	NOUN
ajst-29869	141	69	𝐶	𝐶	PROPN
ajst-29869	141	70	∙	∙	PROPN
ajst-29869	141	71	�	�	PROPN
ajst-29869	141	72	⃗	⃗	PROPN
ajst-29869	141	73	�	�	PROPN
ajst-29869	141	74	�	�	PROPN
ajst-29869	141	75	⃗	⃗	PROPN
ajst-29869	141	76	�	�	PROPN
ajst-29869	141	77	5	5	NUM
ajst-29869	141	78	�	�	PROPN
ajst-29869	141	79	⃗	⃗	NOUN
ajst-29869	141	80	�	�	PROPN
ajst-29869	141	81	𝑡	𝑡	PROPN
ajst-29869	141	82	1	1	NUM
ajst-29869	141	83	�	�	PROPN
ajst-29869	141	84	⃗	⃗	PROPN
ajst-29869	141	85	�	�	PROPN
ajst-29869	141	86	�	�	PROPN
ajst-29869	141	87	⃗	⃗	PROPN
ajst-29869	141	88	�	�	PROPN
ajst-29869	141	89	�	�	PROPN
ajst-29869	141	90	⃗	⃗	NOUN
ajst-29869	141	91	�	�	PROPN
ajst-29869	141	92	3	3	NUM
ajst-29869	141	93	6	6	NUM
ajst-29869	141	94	the	the	DET
ajst-29869	141	95	position	position	NOUN
ajst-29869	141	96	vectors	vector	NOUN
ajst-29869	141	97	�	�	NOUN
ajst-29869	141	98	⃗	⃗	PART
ajst-29869	141	99	�	�	PROPN
ajst-29869	141	100	and	and	CCONJ
ajst-29869	141	101	�	�	PROPN
ajst-29869	141	102	⃗	⃗	PROPN
ajst-29869	141	103	�	�	PROPN
ajst-29869	141	104	𝑎𝑛𝑑	𝑎𝑛𝑑	ADJ
ajst-29869	141	105	�	�	PROPN
ajst-29869	141	106	⃗	⃗	PROPN
ajst-29869	141	107	�	�	PROPN
ajst-29869	141	108	represent	represent	VERB
ajst-29869	141	109	the	the	DET
ajst-29869	141	110	positions	position	NOUN
ajst-29869	141	111	of	of	ADP
ajst-29869	141	112	the	the	DET
ajst-29869	141	113	α	α	PROPN
ajst-29869	141	114	,	,	PUNCT
ajst-29869	141	115	β	β	NOUN
ajst-29869	141	116	,	,	PUNCT
ajst-29869	141	117	and	and	CCONJ
ajst-29869	141	118	δ	δ	PROPN
ajst-29869	141	119	wolves	wolf	NOUN
ajst-29869	141	120	,	,	PUNCT
ajst-29869	141	121	respectively	respectively	ADV
ajst-29869	141	122	;	;	PUNCT
ajst-29869	141	123	𝑋	𝑋	PROPN
ajst-29869	141	124	⃗denotes	⃗denote	VERB
ajst-29869	141	125	the	the	DET
ajst-29869	141	126	current	current	ADJ
ajst-29869	141	127	position	position	NOUN
ajst-29869	141	128	of	of	ADP
ajst-29869	141	129	the	the	DET
ajst-29869	141	130	grey	grey	ADJ
ajst-29869	141	131	wolf	wolf	PROPN
ajst-29869	141	132	.	.	PUNCT
ajst-29869	142	1	the	the	DET
ajst-29869	142	2	coefficients	coefficient	NOUN
ajst-29869	142	3	149	149	NUM
ajst-29869	142	4	𝐶	𝐶	PROPN
ajst-29869	142	5	,	,	PUNCT
ajst-29869	142	6	𝐶	𝐶	PROPN
ajst-29869	142	7	𝑎𝑛𝑑	𝑎𝑛𝑑	PROPN
ajst-29869	142	8	𝐶	𝐶	PROPN
ajst-29869	142	9	are	be	AUX
ajst-29869	142	10	random	random	ADJ
ajst-29869	142	11	weight	weight	NOUN
ajst-29869	142	12	factors	factor	NOUN
ajst-29869	142	13	,	,	PUNCT
ajst-29869	142	14	while	while	SCONJ
ajst-29869	142	15	𝐷	𝐷	PROPN
ajst-29869	142	16	,	,	PUNCT
ajst-29869	142	17	𝐷	𝐷	PROPN
ajst-29869	142	18	𝑎𝑛𝑑	𝑎𝑛𝑑	ADJ
ajst-29869	142	19	𝐷	𝐷	PROPN
ajst-29869	142	20	represent	represent	VERB
ajst-29869	142	21	the	the	DET
ajst-29869	142	22	distances	distance	NOUN
ajst-29869	142	23	between	between	ADP
ajst-29869	142	24	the	the	DET
ajst-29869	142	25	grey	grey	ADJ
ajst-29869	142	26	wolf	wolf	NOUN
ajst-29869	142	27	and	and	CCONJ
ajst-29869	142	28	the	the	DET
ajst-29869	142	29	α	α	NOUN
ajst-29869	142	30	,	,	PUNCT
ajst-29869	142	31	β	β	NOUN
ajst-29869	142	32	,	,	PUNCT
ajst-29869	142	33	and	and	CCONJ
ajst-29869	142	34	δ	δ	PROPN
ajst-29869	142	35	wolves	wolf	NOUN
ajst-29869	142	36	.	.	PUNCT
ajst-29869	143	1	(	(	PUNCT
ajst-29869	143	2	5	5	X
ajst-29869	143	3	)	)	PUNCT
ajst-29869	143	4	iterative	iterative	NOUN
ajst-29869	143	5	update	update	NOUN
ajst-29869	143	6	:	:	PUNCT
ajst-29869	143	7	steps	step	NOUN
ajst-29869	143	8	(	(	PUNCT
ajst-29869	143	9	2	2	NUM
ajst-29869	143	10	)	)	PUNCT
ajst-29869	143	11	to	to	ADP
ajst-29869	143	12	(	(	PUNCT
ajst-29869	143	13	4	4	X
ajst-29869	143	14	)	)	PUNCT
ajst-29869	143	15	are	be	AUX
ajst-29869	143	16	repeated	repeat	VERB
ajst-29869	143	17	.	.	PUNCT
ajst-29869	144	1	in	in	ADP
ajst-29869	144	2	each	each	DET
ajst-29869	144	3	iteration	iteration	NOUN
ajst-29869	144	4	,	,	PUNCT
ajst-29869	144	5	the	the	DET
ajst-29869	144	6	positions	position	NOUN
ajst-29869	144	7	of	of	ADP
ajst-29869	144	8	the	the	DET
ajst-29869	144	9	α	α	PROPN
ajst-29869	144	10	,	,	PUNCT
ajst-29869	144	11	β	β	NOUN
ajst-29869	144	12	,	,	PUNCT
ajst-29869	144	13	and	and	CCONJ
ajst-29869	144	14	δ	δ	PROPN
ajst-29869	144	15	wolves	wolf	NOUN
ajst-29869	144	16	,	,	PUNCT
ajst-29869	144	17	along	along	ADP
ajst-29869	144	18	with	with	ADP
ajst-29869	144	19	their	their	PRON
ajst-29869	144	20	corresponding	correspond	VERB
ajst-29869	144	21	fitness	fitness	NOUN
ajst-29869	144	22	values	value	NOUN
ajst-29869	144	23	,	,	PUNCT
ajst-29869	144	24	are	be	AUX
ajst-29869	144	25	updated	update	VERB
ajst-29869	144	26	.	.	PUNCT
ajst-29869	145	1	the	the	DET
ajst-29869	145	2	positions	position	NOUN
ajst-29869	145	3	of	of	ADP
ajst-29869	145	4	the	the	DET
ajst-29869	145	5	grey	grey	ADJ
ajst-29869	145	6	wolves	wolf	NOUN
ajst-29869	145	7	in	in	ADP
ajst-29869	145	8	the	the	DET
ajst-29869	145	9	population	population	NOUN
ajst-29869	145	10	are	be	AUX
ajst-29869	145	11	gradually	gradually	ADV
ajst-29869	145	12	optimized	optimize	VERB
ajst-29869	145	13	,	,	PUNCT
ajst-29869	145	14	and	and	CCONJ
ajst-29869	145	15	the	the	DET
ajst-29869	145	16	search	search	NOUN
ajst-29869	145	17	for	for	ADP
ajst-29869	145	18	the	the	DET
ajst-29869	145	19	global	global	ADJ
ajst-29869	145	20	optimal	optimal	ADJ
ajst-29869	145	21	feature	feature	NOUN
ajst-29869	145	22	subset	subset	NOUN
ajst-29869	145	23	continues	continue	VERB
ajst-29869	145	24	.	.	PUNCT
ajst-29869	146	1	(	(	PUNCT
ajst-29869	146	2	6	6	X
ajst-29869	146	3	)	)	PUNCT
ajst-29869	146	4	termination	termination	NOUN
ajst-29869	146	5	condition	condition	NOUN
ajst-29869	146	6	:	:	PUNCT
ajst-29869	146	7	the	the	DET
ajst-29869	146	8	algorithm	algorithm	NOUN
ajst-29869	146	9	terminates	terminate	VERB
ajst-29869	146	10	when	when	SCONJ
ajst-29869	146	11	the	the	DET
ajst-29869	146	12	maximum	maximum	ADJ
ajst-29869	146	13	number	number	NOUN
ajst-29869	146	14	of	of	ADP
ajst-29869	146	15	iterations	iteration	NOUN
ajst-29869	146	16	is	be	AUX
ajst-29869	146	17	reached	reach	VERB
ajst-29869	146	18	,	,	PUNCT
ajst-29869	146	19	or	or	CCONJ
ajst-29869	146	20	when	when	SCONJ
ajst-29869	146	21	the	the	DET
ajst-29869	146	22	fitness	fitness	NOUN
ajst-29869	146	23	value	value	NOUN
ajst-29869	146	24	of	of	ADP
ajst-29869	146	25	the	the	DET
ajst-29869	146	26	α	α	NOUN
ajst-29869	146	27	wolf	wolf	NOUN
ajst-29869	146	28	no	no	ADV
ajst-29869	146	29	longer	long	ADV
ajst-29869	146	30	shows	show	VERB
ajst-29869	146	31	significant	significant	ADJ
ajst-29869	146	32	changes	change	NOUN
ajst-29869	146	33	.	.	PUNCT
ajst-29869	147	1	(	(	PUNCT
ajst-29869	147	2	7	7	X
ajst-29869	147	3	)	)	PUNCT
ajst-29869	147	4	output	output	NOUN
ajst-29869	147	5	result	result	NOUN
ajst-29869	147	6	:	:	PUNCT
ajst-29869	147	7	the	the	DET
ajst-29869	147	8	feature	feature	NOUN
ajst-29869	147	9	subset	subset	VERB
ajst-29869	147	10	corresponding	correspond	VERB
ajst-29869	147	11	to	to	ADP
ajst-29869	147	12	the	the	DET
ajst-29869	147	13	α	α	PROPN
ajst-29869	147	14	wolf	wolf	NOUN
ajst-29869	147	15	is	be	AUX
ajst-29869	147	16	output	output	NOUN
ajst-29869	147	17	as	as	SCONJ
ajst-29869	147	18	the	the	DET
ajst-29869	147	19	optimal	optimal	ADJ
ajst-29869	147	20	feature	feature	NOUN
ajst-29869	147	21	subset	subset	NOUN
ajst-29869	147	22	.	.	PUNCT
ajst-29869	148	1	the	the	DET
ajst-29869	148	2	gwo_rf	gwo_rf	PROPN
ajst-29869	148	3	algorithm	algorithm	PROPN
ajst-29869	148	4	effectively	effectively	ADV
ajst-29869	148	5	reduces	reduce	VERB
ajst-29869	148	6	the	the	DET
ajst-29869	148	7	feature	feature	NOUN
ajst-29869	148	8	dimensionality	dimensionality	NOUN
ajst-29869	148	9	while	while	SCONJ
ajst-29869	148	10	improving	improve	VERB
ajst-29869	148	11	classification	classification	NOUN
ajst-29869	148	12	accuracy	accuracy	NOUN
ajst-29869	148	13	,	,	PUNCT
ajst-29869	148	14	ultimately	ultimately	ADV
ajst-29869	148	15	yielding	yield	VERB
ajst-29869	148	16	the	the	DET
ajst-29869	148	17	optimal	optimal	ADJ
ajst-29869	148	18	feature	feature	NOUN
ajst-29869	148	19	subset	subset	NOUN
ajst-29869	148	20	.	.	PUNCT
ajst-29869	149	1	figure	figure	NOUN
ajst-29869	149	2	2	2	NUM
ajst-29869	149	3	.	.	PUNCT
ajst-29869	150	1	the	the	DET
ajst-29869	150	2	process	process	NOUN
ajst-29869	150	3	of	of	ADP
ajst-29869	150	4	the	the	DET
ajst-29869	150	5	gwo_rf	gwo_rf	PROPN
ajst-29869	150	6	2.2	2.2	NUM
ajst-29869	150	7	.	.	PUNCT
ajst-29869	150	8	comparison	comparison	NOUN
ajst-29869	150	9	experiment	experiment	NOUN
ajst-29869	150	10	to	to	PART
ajst-29869	150	11	verify	verify	VERB
ajst-29869	150	12	the	the	DET
ajst-29869	150	13	effectiveness	effectiveness	NOUN
ajst-29869	150	14	of	of	ADP
ajst-29869	150	15	the	the	DET
ajst-29869	150	16	proposed	propose	VERB
ajst-29869	150	17	mrg_rf	mrg_rf	X
ajst-29869	150	18	feature	feature	NOUN
ajst-29869	150	19	selection	selection	NOUN
ajst-29869	150	20	model	model	NOUN
ajst-29869	150	21	,	,	PUNCT
ajst-29869	150	22	this	this	DET
ajst-29869	150	23	paper	paper	NOUN
ajst-29869	150	24	compares	compare	VERB
ajst-29869	150	25	it	it	PRON
ajst-29869	150	26	with	with	ADP
ajst-29869	150	27	classical	classical	ADJ
ajst-29869	150	28	feature	feature	NOUN
ajst-29869	150	29	selection	selection	NOUN
ajst-29869	150	30	methods	method	NOUN
ajst-29869	150	31	,	,	PUNCT
ajst-29869	150	32	including	include	VERB
ajst-29869	150	33	the	the	DET
ajst-29869	150	34	commonly	commonly	ADV
ajst-29869	150	35	used	use	VERB
ajst-29869	150	36	information	information	NOUN
ajst-29869	150	37	gain	gain	NOUN
ajst-29869	150	38	(	(	PUNCT
ajst-29869	150	39	ig	ig	NOUN
ajst-29869	150	40	)	)	PUNCT
ajst-29869	150	41	and	and	CCONJ
ajst-29869	150	42	genetic	genetic	ADJ
ajst-29869	150	43	algorithm	algorithm	NOUN
ajst-29869	150	44	(	(	PUNCT
ajst-29869	150	45	ga	ga	PROPN
ajst-29869	150	46	)	)	PUNCT
ajst-29869	150	47	,	,	PUNCT
ajst-29869	150	48	to	to	PART
ajst-29869	150	49	comprehensively	comprehensively	ADV
ajst-29869	150	50	evaluate	evaluate	VERB
ajst-29869	150	51	the	the	DET
ajst-29869	150	52	performance	performance	NOUN
ajst-29869	150	53	of	of	ADP
ajst-29869	150	54	the	the	DET
ajst-29869	150	55	three	three	NUM
ajst-29869	150	56	methods	method	NOUN
ajst-29869	150	57	in	in	ADP
ajst-29869	150	58	terms	term	NOUN
ajst-29869	150	59	of	of	ADP
ajst-29869	150	60	accuracy	accuracy	NOUN
ajst-29869	150	61	improvement	improvement	NOUN
ajst-29869	150	62	and	and	CCONJ
ajst-29869	150	63	computational	computational	ADJ
ajst-29869	150	64	efficiency	efficiency	NOUN
ajst-29869	150	65	.	.	PUNCT
ajst-29869	151	1	table	table	NOUN
ajst-29869	151	2	4	4	NUM
ajst-29869	151	3	.	.	PUNCT
ajst-29869	152	1	results	result	NOUN
ajst-29869	152	2	of	of	ADP
ajst-29869	152	3	different	different	ADJ
ajst-29869	152	4	feature	feature	NOUN
ajst-29869	152	5	selection	selection	NOUN
ajst-29869	152	6	methods	method	NOUN
ajst-29869	152	7	method	method	VERB
ajst-29869	152	8	number	number	NOUN
ajst-29869	152	9	of	of	ADP
ajst-29869	152	10	features	feature	NOUN
ajst-29869	152	11	recognition	recognition	NOUN
ajst-29869	152	12	accuracy	accuracy	NOUN
ajst-29869	152	13	(	(	PUNCT
ajst-29869	152	14	%	%	NOUN
ajst-29869	152	15	)	)	PUNCT
ajst-29869	152	16	time	time	NOUN
ajst-29869	152	17	(	(	PUNCT
ajst-29869	152	18	minutes	minute	NOUN
ajst-29869	152	19	)	)	PUNCT
ajst-29869	152	20	proposed	propose	VERB
ajst-29869	152	21	method	method	NOUN
ajst-29869	152	22	19	19	NUM
ajst-29869	152	23	92.3	92.3	NUM
ajst-29869	152	24	426.0	426.0	NUM
ajst-29869	152	25	ga	ga	NOUN
ajst-29869	152	26	25	25	NUM
ajst-29869	152	27	87.7	87.7	NUM
ajst-29869	152	28	496.9	496.9	NUM
ajst-29869	152	29	ig	ig	PROPN
ajst-29869	152	30	31	31	NUM
ajst-29869	152	31	89.1	89.1	NUM
ajst-29869	152	32	523.7	523.7	NUM
ajst-29869	152	33	the	the	DET
ajst-29869	152	34	mrg_rf	mrg_rf	PROPN
ajst-29869	152	35	model	model	NOUN
ajst-29869	152	36	proposed	propose	VERB
ajst-29869	152	37	in	in	ADP
ajst-29869	152	38	this	this	DET
ajst-29869	152	39	paper	paper	NOUN
ajst-29869	152	40	outperforms	outperform	VERB
ajst-29869	152	41	traditional	traditional	ADJ
ajst-29869	152	42	feature	feature	NOUN
ajst-29869	152	43	selection	selection	NOUN
ajst-29869	152	44	methods	method	NOUN
ajst-29869	152	45	.	.	PUNCT
ajst-29869	153	1	in	in	ADP
ajst-29869	153	2	terms	term	NOUN
ajst-29869	153	3	of	of	ADP
ajst-29869	153	4	feature	feature	NOUN
ajst-29869	153	5	count	count	NOUN
ajst-29869	153	6	,	,	PUNCT
ajst-29869	153	7	the	the	DET
ajst-29869	153	8	proposed	propose	VERB
ajst-29869	153	9	method	method	NOUN
ajst-29869	153	10	selects	select	VERB
ajst-29869	153	11	only	only	ADV
ajst-29869	153	12	19	19	NUM
ajst-29869	153	13	efficient	efficient	ADJ
ajst-29869	153	14	features	feature	NOUN
ajst-29869	153	15	,	,	PUNCT
ajst-29869	153	16	ensuring	ensure	VERB
ajst-29869	153	17	low	low	ADJ
ajst-29869	153	18	redundancy	redundancy	NOUN
ajst-29869	153	19	while	while	SCONJ
ajst-29869	153	20	simplifying	simplify	VERB
ajst-29869	153	21	the	the	DET
ajst-29869	153	22	feature	feature	NOUN
ajst-29869	153	23	set	set	VERB
ajst-29869	153	24	.	.	PUNCT
ajst-29869	154	1	regarding	regard	VERB
ajst-29869	154	2	accuracy	accuracy	NOUN
ajst-29869	154	3	,	,	PUNCT
ajst-29869	154	4	the	the	DET
ajst-29869	154	5	multi	multi	ADJ
ajst-29869	154	6	-	-	ADJ
ajst-29869	154	7	optimization	optimization	ADJ
ajst-29869	154	8	strategy	strategy	NOUN
ajst-29869	154	9	used	use	VERB
ajst-29869	154	10	in	in	ADP
ajst-29869	154	11	this	this	DET
ajst-29869	154	12	method	method	NOUN
ajst-29869	154	13	better	well	ADV
ajst-29869	154	14	captures	capture	VERB
ajst-29869	154	15	the	the	DET
ajst-29869	154	16	key	key	ADJ
ajst-29869	154	17	nonlinear	nonlinear	ADJ
ajst-29869	154	18	relationships	relationship	NOUN
ajst-29869	154	19	and	and	CCONJ
ajst-29869	154	20	potential	potential	ADJ
ajst-29869	154	21	patterns	pattern	NOUN
ajst-29869	154	22	in	in	ADP
ajst-29869	154	23	the	the	DET
ajst-29869	154	24	data	datum	NOUN
ajst-29869	154	25	,	,	PUNCT
ajst-29869	154	26	resulting	result	VERB
ajst-29869	154	27	in	in	ADP
ajst-29869	154	28	the	the	DET
ajst-29869	154	29	most	most	ADV
ajst-29869	154	30	precise	precise	ADJ
ajst-29869	154	31	feature	feature	NOUN
ajst-29869	154	32	combination	combination	NOUN
ajst-29869	154	33	.	.	PUNCT
ajst-29869	155	1	in	in	ADP
ajst-29869	155	2	terms	term	NOUN
ajst-29869	155	3	of	of	ADP
ajst-29869	155	4	efficiency	efficiency	NOUN
ajst-29869	155	5	,	,	PUNCT
ajst-29869	155	6	the	the	DET
ajst-29869	155	7	proposed	propose	VERB
ajst-29869	155	8	method	method	NOUN
ajst-29869	155	9	achieves	achieve	VERB
ajst-29869	155	10	higher	high	ADJ
ajst-29869	155	11	accuracy	accuracy	NOUN
ajst-29869	155	12	with	with	ADP
ajst-29869	155	13	a	a	DET
ajst-29869	155	14	lower	low	ADJ
ajst-29869	155	15	time	time	NOUN
ajst-29869	155	16	cost	cost	NOUN
ajst-29869	155	17	.	.	PUNCT
ajst-29869	156	1	3	3	X
ajst-29869	156	2	.	.	X
ajst-29869	156	3	conclusion	conclusion	NOUN
ajst-29869	156	4	this	this	DET
ajst-29869	156	5	study	study	NOUN
ajst-29869	156	6	presents	present	VERB
ajst-29869	156	7	a	a	DET
ajst-29869	156	8	novel	novel	ADJ
ajst-29869	156	9	feature	feature	NOUN
ajst-29869	156	10	selection	selection	NOUN
ajst-29869	156	11	algorithm	algorithm	NOUN
ajst-29869	156	12	for	for	ADP
ajst-29869	156	13	rice	rice	NOUN
ajst-29869	156	14	crop	crop	NOUN
ajst-29869	156	15	recognition	recognition	NOUN
ajst-29869	156	16	in	in	ADP
ajst-29869	156	17	mountainous	mountainous	ADJ
ajst-29869	156	18	regions	region	NOUN
ajst-29869	156	19	,	,	PUNCT
ajst-29869	156	20	which	which	PRON
ajst-29869	156	21	integrates	integrate	VERB
ajst-29869	156	22	the	the	DET
ajst-29869	156	23	relief	relief	NOUN
ajst-29869	156	24	algorithm	algorithm	NOUN
ajst-29869	156	25	and	and	CCONJ
ajst-29869	156	26	the	the	DET
ajst-29869	156	27	grey	grey	ADJ
ajst-29869	156	28	wolf	wolf	PROPN
ajst-29869	156	29	optimizer	optimizer	NOUN
ajst-29869	156	30	(	(	PUNCT
ajst-29869	156	31	gwo	gwo	PROPN
ajst-29869	156	32	)	)	PUNCT
ajst-29869	156	33	to	to	PART
ajst-29869	156	34	effectively	effectively	ADV
ajst-29869	156	35	reduce	reduce	VERB
ajst-29869	156	36	feature	feature	NOUN
ajst-29869	156	37	redundancy	redundancy	NOUN
ajst-29869	156	38	and	and	CCONJ
ajst-29869	156	39	enhance	enhance	VERB
ajst-29869	156	40	the	the	DET
ajst-29869	156	41	classification	classification	NOUN
ajst-29869	156	42	accuracy	accuracy	NOUN
ajst-29869	156	43	.	.	PUNCT
ajst-29869	157	1	the	the	DET
ajst-29869	157	2	proposed	propose	VERB
ajst-29869	157	3	method	method	NOUN
ajst-29869	157	4	first	first	ADV
ajst-29869	157	5	utilizes	utilize	VERB
ajst-29869	157	6	the	the	DET
ajst-29869	157	7	relief	relief	NOUN
ajst-29869	157	8	algorithm	algorithm	NOUN
ajst-29869	157	9	to	to	PART
ajst-29869	157	10	identify	identify	VERB
ajst-29869	157	11	important	important	ADJ
ajst-29869	157	12	features	feature	NOUN
ajst-29869	157	13	from	from	ADP
ajst-29869	157	14	an	an	DET
ajst-29869	157	15	initial	initial	ADJ
ajst-29869	157	16	feature	feature	NOUN
ajst-29869	157	17	set	set	NOUN
ajst-29869	157	18	,	,	PUNCT
ajst-29869	157	19	followed	follow	VERB
ajst-29869	157	20	by	by	ADP
ajst-29869	157	21	further	further	ADJ
ajst-29869	157	22	optimization	optimization	NOUN
ajst-29869	157	23	using	use	VERB
ajst-29869	157	24	the	the	DET
ajst-29869	157	25	gwo	gwo	NOUN
ajst-29869	157	26	to	to	PART
ajst-29869	157	27	refine	refine	VERB
ajst-29869	157	28	the	the	DET
ajst-29869	157	29	feature	feature	NOUN
ajst-29869	157	30	subset	subset	VERB
ajst-29869	157	31	.	.	PUNCT
ajst-29869	158	1	the	the	DET
ajst-29869	158	2	results	result	NOUN
ajst-29869	158	3	show	show	VERB
ajst-29869	158	4	that	that	SCONJ
ajst-29869	158	5	the	the	DET
ajst-29869	158	6	combined	combined	ADJ
ajst-29869	158	7	use	use	NOUN
ajst-29869	158	8	of	of	ADP
ajst-29869	158	9	relief	relief	NOUN
ajst-29869	158	10	and	and	CCONJ
ajst-29869	158	11	gwo	gwo	PROPN
ajst-29869	158	12	significantly	significantly	ADV
ajst-29869	158	13	improves	improve	VERB
ajst-29869	158	14	both	both	DET
ajst-29869	158	15	feature	feature	NOUN
ajst-29869	158	16	selection	selection	NOUN
ajst-29869	158	17	efficiency	efficiency	NOUN
ajst-29869	158	18	and	and	CCONJ
ajst-29869	158	19	classification	classification	NOUN
ajst-29869	158	20	performance	performance	NOUN
ajst-29869	158	21	,	,	PUNCT
ajst-29869	158	22	as	as	SCONJ
ajst-29869	158	23	demonstrated	demonstrate	VERB
ajst-29869	158	24	by	by	ADP
ajst-29869	158	25	the	the	DET
ajst-29869	158	26	increased	increase	VERB
ajst-29869	158	27	recognition	recognition	NOUN
ajst-29869	158	28	accuracy	accuracy	NOUN
ajst-29869	158	29	and	and	CCONJ
ajst-29869	158	30	reduced	reduced	ADJ
ajst-29869	158	31	computation	computation	NOUN
ajst-29869	158	32	time	time	NOUN
ajst-29869	158	33	.	.	PUNCT
ajst-29869	159	1	by	by	ADP
ajst-29869	159	2	leveraging	leverage	VERB
ajst-29869	159	3	these	these	DET
ajst-29869	159	4	algorithms	algorithm	NOUN
ajst-29869	159	5	,	,	PUNCT
ajst-29869	159	6	the	the	DET
ajst-29869	159	7	study	study	NOUN
ajst-29869	159	8	successfully	successfully	ADV
ajst-29869	159	9	identifies	identify	VERB
ajst-29869	159	10	an	an	DET
ajst-29869	159	11	optimal	optimal	ADJ
ajst-29869	159	12	feature	feature	NOUN
ajst-29869	159	13	subset	subset	VERB
ajst-29869	159	14	that	that	SCONJ
ajst-29869	159	15	balances	balance	VERB
ajst-29869	159	16	high	high	ADJ
ajst-29869	159	17	classification	classification	NOUN
ajst-29869	159	18	accuracy	accuracy	NOUN
ajst-29869	159	19	with	with	ADP
ajst-29869	159	20	low	low	ADJ
ajst-29869	159	21	computational	computational	ADJ
ajst-29869	159	22	complexity	complexity	NOUN
ajst-29869	159	23	.	.	PUNCT
ajst-29869	160	1	this	this	DET
ajst-29869	160	2	research	research	NOUN
ajst-29869	160	3	demonstrates	demonstrate	VERB
ajst-29869	160	4	the	the	DET
ajst-29869	160	5	potential	potential	NOUN
ajst-29869	160	6	of	of	ADP
ajst-29869	160	7	machine	machine	NOUN
ajst-29869	160	8	learningbased	learningbase	VERB
ajst-29869	160	9	feature	feature	NOUN
ajst-29869	160	10	selection	selection	NOUN
ajst-29869	160	11	techniques	technique	NOUN
ajst-29869	160	12	in	in	ADP
ajst-29869	160	13	precision	precision	NOUN
ajst-29869	160	14	agriculture	agriculture	NOUN
ajst-29869	160	15	,	,	PUNCT
ajst-29869	160	16	particularly	particularly	ADV
ajst-29869	160	17	in	in	ADP
ajst-29869	160	18	the	the	DET
ajst-29869	160	19	challenging	challenging	ADJ
ajst-29869	160	20	environment	environment	NOUN
ajst-29869	160	21	of	of	ADP
ajst-29869	160	22	mountainous	mountainous	ADJ
ajst-29869	160	23	regions	region	NOUN
ajst-29869	160	24	.	.	PUNCT
ajst-29869	161	1	future	future	ADJ
ajst-29869	161	2	work	work	NOUN
ajst-29869	161	3	will	will	AUX
ajst-29869	161	4	explore	explore	VERB
ajst-29869	161	5	the	the	DET
ajst-29869	161	6	application	application	NOUN
ajst-29869	161	7	of	of	ADP
ajst-29869	161	8	this	this	DET
ajst-29869	161	9	method	method	NOUN
ajst-29869	161	10	to	to	ADP
ajst-29869	161	11	larger	large	ADJ
ajst-29869	161	12	-	-	PUNCT
ajst-29869	161	13	scale	scale	NOUN
ajst-29869	161	14	agricultural	agricultural	ADJ
ajst-29869	161	15	monitoring	monitoring	NOUN
ajst-29869	161	16	tasks	task	NOUN
ajst-29869	161	17	and	and	CCONJ
ajst-29869	161	18	further	further	ADJ
ajst-29869	161	19	refinement	refinement	NOUN
ajst-29869	161	20	of	of	ADP
ajst-29869	161	21	the	the	DET
ajst-29869	161	22	feature	feature	NOUN
ajst-29869	161	23	selection	selection	NOUN
ajst-29869	161	24	process	process	NOUN
ajst-29869	161	25	for	for	ADP
ajst-29869	161	26	even	even	ADV
ajst-29869	161	27	more	more	ADV
ajst-29869	161	28	complex	complex	ADJ
ajst-29869	161	29	agricultural	agricultural	ADJ
ajst-29869	161	30	scenarios	scenario	NOUN
ajst-29869	161	31	.	.	PUNCT
ajst-29869	162	1	references	reference	NOUN
ajst-29869	162	2	[	[	X
ajst-29869	162	3	1	1	NUM
ajst-29869	162	4	]	]	PUNCT
ajst-29869	162	5	l.	l.	PROPN
ajst-29869	162	6	zhang	zhang	PROPN
ajst-29869	162	7	,	,	PUNCT
ajst-29869	162	8	h.	h.	PROPN
ajst-29869	162	9	li	li	PROPN
ajst-29869	162	10	,	,	PUNCT
ajst-29869	162	11	and	and	CCONJ
ajst-29869	162	12	x.	x.	PROPN
ajst-29869	162	13	xu	xu	PROPN
ajst-29869	162	14	,	,	PUNCT
ajst-29869	162	15	“	"	PUNCT
ajst-29869	162	16	a	a	DET
ajst-29869	162	17	novel	novel	ADJ
ajst-29869	162	18	feature	feature	NOUN
ajst-29869	162	19	selection	selection	NOUN
ajst-29869	162	20	method	method	NOUN
ajst-29869	162	21	for	for	ADP
ajst-29869	162	22	remote	remote	ADJ
ajst-29869	162	23	sensing	sense	VERB
ajst-29869	162	24	image	image	NOUN
ajst-29869	162	25	classification	classification	NOUN
ajst-29869	162	26	based	base	VERB
ajst-29869	162	27	on	on	ADP
ajst-29869	162	28	mutual	mutual	ADJ
ajst-29869	162	29	information	information	NOUN
ajst-29869	162	30	and	and	CCONJ
ajst-29869	162	31	relief	relief	NOUN
ajst-29869	162	32	algorithm	algorithm	NOUN
ajst-29869	162	33	,	,	PUNCT
ajst-29869	162	34	”	"	PUNCT
ajst-29869	162	35	remote	remote	ADJ
ajst-29869	162	36	sensing	sensing	NOUN
ajst-29869	162	37	,	,	PUNCT
ajst-29869	162	38	vol	vol	NOUN
ajst-29869	162	39	.	.	PROPN
ajst-29869	162	40	12	12	NUM
ajst-29869	162	41	,	,	PUNCT
ajst-29869	162	42	no	no	INTJ
ajst-29869	162	43	.	.	NOUN
ajst-29869	162	44	5	5	NUM
ajst-29869	162	45	,	,	PUNCT
ajst-29869	162	46	pp	pp	ADJ
ajst-29869	162	47	.	.	PUNCT
ajst-29869	162	48	1012–1025	1012–1025	NUM
ajst-29869	162	49	,	,	PUNCT
ajst-29869	162	50	may	may	AUX
ajst-29869	162	51	2020	2020	NUM
ajst-29869	162	52	.	.	PUNCT
ajst-29869	163	1	[	[	X
ajst-29869	163	2	2	2	X
ajst-29869	163	3	]	]	PUNCT
ajst-29869	163	4	h.	h.	PROPN
ajst-29869	163	5	zhang	zhang	PROPN
ajst-29869	163	6	,	,	PUNCT
ajst-29869	163	7	z.	z.	PROPN
ajst-29869	163	8	chen	chen	PROPN
ajst-29869	163	9	,	,	PUNCT
ajst-29869	163	10	and	and	CCONJ
ajst-29869	163	11	m.	m.	PROPN
ajst-29869	163	12	zhang	zhang	PROPN
ajst-29869	163	13	,	,	PUNCT
ajst-29869	163	14	“	"	PUNCT
ajst-29869	163	15	a	a	DET
ajst-29869	163	16	hybrid	hybrid	ADJ
ajst-29869	163	17	feature	feature	NOUN
ajst-29869	163	18	selection	selection	NOUN
ajst-29869	163	19	method	method	NOUN
ajst-29869	163	20	for	for	ADP
ajst-29869	163	21	hyperspectral	hyperspectral	ADJ
ajst-29869	163	22	image	image	NOUN
ajst-29869	163	23	classification	classification	NOUN
ajst-29869	163	24	using	use	VERB
ajst-29869	163	25	relief	relief	NOUN
ajst-29869	163	26	and	and	CCONJ
ajst-29869	163	27	genetic	genetic	ADJ
ajst-29869	163	28	algorithm	algorithm	NOUN
ajst-29869	163	29	,	,	PUNCT
ajst-29869	163	30	”	"	PUNCT
ajst-29869	163	31	ieee	ieee	NOUN
ajst-29869	163	32	trans	tran	NOUN
ajst-29869	163	33	.	.	PUNCT
ajst-29869	164	1	geosci	geosci	PROPN
ajst-29869	164	2	.	.	PUNCT
ajst-29869	165	1	remote	remote	PROPN
ajst-29869	165	2	sens	sens	PROPN
ajst-29869	165	3	.	.	PROPN
ajst-29869	165	4	,	,	PUNCT
ajst-29869	165	5	vol	vol	NOUN
ajst-29869	165	6	.	.	PROPN
ajst-29869	166	1	58	58	NUM
ajst-29869	166	2	,	,	PUNCT
ajst-29869	166	3	no	no	INTJ
ajst-29869	166	4	.	.	NOUN
ajst-29869	166	5	4	4	NUM
ajst-29869	166	6	,	,	PUNCT
ajst-29869	166	7	pp	pp	ADJ
ajst-29869	166	8	.	.	PUNCT
ajst-29869	167	1	2790–2801	2790–2801	NUM
ajst-29869	167	2	,	,	PUNCT
ajst-29869	167	3	apr	apr	NOUN
ajst-29869	167	4	.	.	PUNCT
ajst-29869	167	5	2020	2020	NUM
ajst-29869	167	6	.	.	PUNCT
ajst-29869	168	1	[	[	X
ajst-29869	168	2	3	3	X
ajst-29869	168	3	]	]	X
ajst-29869	168	4	y.	y.	PROPN
ajst-29869	168	5	wang	wang	PROPN
ajst-29869	168	6	,	,	PUNCT
ajst-29869	168	7	l.	l.	PROPN
ajst-29869	168	8	zhang	zhang	PROPN
ajst-29869	168	9	,	,	PUNCT
ajst-29869	168	10	and	and	CCONJ
ajst-29869	168	11	x.	x.	PROPN
ajst-29869	168	12	li	li	PROPN
ajst-29869	168	13	,	,	PUNCT
ajst-29869	168	14	“	"	PUNCT
ajst-29869	168	15	gray	gray	ADJ
ajst-29869	168	16	wolf	wolf	PROPN
ajst-29869	168	17	optimization	optimization	NOUN
ajst-29869	168	18	algorithm	algorithm	NOUN
ajst-29869	168	19	based	base	VERB
ajst-29869	168	20	feature	feature	NOUN
ajst-29869	168	21	selection	selection	NOUN
ajst-29869	168	22	for	for	ADP
ajst-29869	168	23	remote	remote	ADJ
ajst-29869	168	24	sensing	sense	VERB
ajst-29869	168	25	image	image	NOUN
ajst-29869	168	26	classification	classification	NOUN
ajst-29869	168	27	,	,	PUNCT
ajst-29869	168	28	”	"	PUNCT
ajst-29869	168	29	isprs	isprs	NOUN
ajst-29869	168	30	j.	j.	PROPN
ajst-29869	168	31	photogramm	photogramm	PROPN
ajst-29869	168	32	.	.	PUNCT
ajst-29869	169	1	remote	remote	PROPN
ajst-29869	169	2	sens	sens	PROPN
ajst-29869	169	3	.	.	PROPN
ajst-29869	169	4	,	,	PUNCT
ajst-29869	169	5	vol	vol	NOUN
ajst-29869	169	6	.	.	PROPN
ajst-29869	169	7	163	163	NUM
ajst-29869	169	8	,	,	PUNCT
ajst-29869	169	9	pp	pp	ADJ
ajst-29869	169	10	.	.	PUNCT
ajst-29869	170	1	177–189	177–189	NUM
ajst-29869	170	2	,	,	PUNCT
ajst-29869	170	3	feb	feb	PROPN
ajst-29869	170	4	.	.	PROPN
ajst-29869	170	5	2020	2020	NUM
ajst-29869	170	6	.	.	PUNCT
ajst-29869	171	1	[	[	X
ajst-29869	171	2	4	4	X
ajst-29869	171	3	]	]	PUNCT
ajst-29869	171	4	z.	z.	PROPN
ajst-29869	171	5	li	li	PROPN
ajst-29869	171	6	,	,	PUNCT
ajst-29869	171	7	l.	l.	PROPN
ajst-29869	171	8	zhang	zhang	PROPN
ajst-29869	171	9	,	,	PUNCT
ajst-29869	171	10	and	and	CCONJ
ajst-29869	171	11	x.	x.	PROPN
ajst-29869	171	12	chen	chen	PROPN
ajst-29869	171	13	,	,	PUNCT
ajst-29869	171	14	“	"	PUNCT
ajst-29869	171	15	feature	feature	NOUN
ajst-29869	171	16	selection	selection	NOUN
ajst-29869	171	17	for	for	ADP
ajst-29869	171	18	highdimensional	highdimensional	ADJ
ajst-29869	171	19	remote	remote	ADJ
ajst-29869	171	20	sensing	sense	VERB
ajst-29869	171	21	image	image	NOUN
ajst-29869	171	22	classification	classification	NOUN
ajst-29869	171	23	using	use	VERB
ajst-29869	171	24	adaptive	adaptive	ADJ
ajst-29869	171	25	genetic	genetic	ADJ
ajst-29869	171	26	algorithm	algorithm	NOUN
ajst-29869	171	27	and	and	CCONJ
ajst-29869	171	28	random	random	ADJ
ajst-29869	171	29	forests	forest	NOUN
ajst-29869	171	30	,	,	PUNCT
ajst-29869	171	31	”	"	PUNCT
ajst-29869	171	32	remote	remote	ADJ
ajst-29869	171	33	sensing	sensing	NOUN
ajst-29869	171	34	,	,	PUNCT
ajst-29869	171	35	vol	vol	NOUN
ajst-29869	171	36	.	.	PROPN
ajst-29869	171	37	13	13	NUM
ajst-29869	171	38	,	,	PUNCT
ajst-29869	171	39	no	no	INTJ
ajst-29869	171	40	.	.	NOUN
ajst-29869	171	41	3	3	NUM
ajst-29869	171	42	,	,	PUNCT
ajst-29869	171	43	pp	pp	ADJ
ajst-29869	171	44	.	.	PUNCT
ajst-29869	172	1	459–471	459–471	NUM
ajst-29869	172	2	,	,	PUNCT
ajst-29869	172	3	mar	mar	PROPN
ajst-29869	172	4	.	.	PROPN
ajst-29869	172	5	2021	2021	NUM
ajst-29869	172	6	.	.	PUNCT
ajst-29869	173	1	[	[	X
ajst-29869	173	2	5	5	NUM
ajst-29869	173	3	]	]	PUNCT
ajst-29869	173	4	r.	r.	PROPN
ajst-29869	173	5	liu	liu	PROPN
ajst-29869	173	6	,	,	PUNCT
ajst-29869	173	7	z.	z.	PROPN
ajst-29869	173	8	huang	huang	PROPN
ajst-29869	173	9	,	,	PUNCT
ajst-29869	173	10	and	and	CCONJ
ajst-29869	173	11	x.	x.	PROPN
ajst-29869	173	12	chen	chen	PROPN
ajst-29869	173	13	,	,	PUNCT
ajst-29869	173	14	“	"	PUNCT
ajst-29869	173	15	a	a	DET
ajst-29869	173	16	hybrid	hybrid	ADJ
ajst-29869	173	17	feature	feature	NOUN
ajst-29869	173	18	selection	selection	NOUN
ajst-29869	173	19	method	method	NOUN
ajst-29869	173	20	based	base	VERB
ajst-29869	173	21	on	on	ADP
ajst-29869	173	22	relief	relief	NOUN
ajst-29869	173	23	and	and	CCONJ
ajst-29869	173	24	particle	particle	NOUN
ajst-29869	173	25	swarm	swarm	NOUN
ajst-29869	173	26	optimization	optimization	NOUN
ajst-29869	173	27	for	for	ADP
ajst-29869	173	28	remote	remote	ADJ
ajst-29869	173	29	sensing	sense	VERB
ajst-29869	173	30	applications	application	NOUN
ajst-29869	173	31	,	,	PUNCT
ajst-29869	173	32	”	"	PUNCT
ajst-29869	173	33	ieee	ieee	NOUN
ajst-29869	173	34	access	access	NOUN
ajst-29869	173	35	,	,	PUNCT
ajst-29869	173	36	vol	vol	NOUN
ajst-29869	173	37	.	.	PROPN
ajst-29869	173	38	8	8	NUM
ajst-29869	173	39	,	,	PUNCT
ajst-29869	173	40	pp	pp	ADJ
ajst-29869	173	41	.	.	PUNCT
ajst-29869	173	42	134285	134285	NUM
ajst-29869	173	43	–	–	PUNCT
ajst-29869	173	44	134294	134294	NUM
ajst-29869	173	45	,	,	PUNCT
ajst-29869	173	46	2020	2020	NUM
ajst-29869	173	47	.	.	PUNCT
ajst-29869	174	1	[	[	X
ajst-29869	174	2	6	6	NUM
ajst-29869	174	3	]	]	PUNCT
ajst-29869	174	4	m.	m.	PROPN
ajst-29869	174	5	zhang	zhang	PROPN
ajst-29869	174	6	,	,	PUNCT
ajst-29869	174	7	x.	x.	PROPN
ajst-29869	174	8	wang	wang	PROPN
ajst-29869	174	9	,	,	PUNCT
ajst-29869	174	10	and	and	CCONJ
ajst-29869	174	11	j.	j.	PROPN
ajst-29869	174	12	yang	yang	PROPN
ajst-29869	174	13	,	,	PUNCT
ajst-29869	174	14	“	"	PUNCT
ajst-29869	174	15	efficient	efficient	ADJ
ajst-29869	174	16	feature	feature	NOUN
ajst-29869	174	17	selection	selection	NOUN
ajst-29869	174	18	method	method	NOUN
ajst-29869	174	19	based	base	VERB
ajst-29869	174	20	on	on	ADP
ajst-29869	174	21	gray	gray	ADJ
ajst-29869	174	22	wolf	wolf	NOUN
ajst-29869	174	23	optimizer	optimizer	NOUN
ajst-29869	174	24	for	for	ADP
ajst-29869	174	25	land	land	NOUN
ajst-29869	174	26	cover	cover	NOUN
ajst-29869	174	27	classification	classification	NOUN
ajst-29869	174	28	in	in	ADP
ajst-29869	174	29	complex	complex	ADJ
ajst-29869	174	30	terrain	terrain	NOUN
ajst-29869	174	31	,	,	PUNCT
ajst-29869	174	32	”	"	PUNCT
ajst-29869	174	33	int	int	NOUN
ajst-29869	174	34	.	.	PUNCT
ajst-29869	175	1	j.	j.	PROPN
ajst-29869	175	2	appl	appl	PROPN
ajst-29869	175	3	.	.	PUNCT
ajst-29869	176	1	earth	earth	PROPN
ajst-29869	176	2	obs	obs	PROPN
ajst-29869	176	3	.	.	PROPN
ajst-29869	176	4	geoinf	geoinf	PROPN
ajst-29869	176	5	.	.	PUNCT
ajst-29869	177	1	,	,	PUNCT
ajst-29869	177	2	vol	vol	NOUN
ajst-29869	177	3	.	.	PROPN
ajst-29869	178	1	90	90	NUM
ajst-29869	178	2	,	,	PUNCT
ajst-29869	178	3	pp	pp	ADJ
ajst-29869	178	4	.	.	PUNCT
ajst-29869	179	1	102115	102115	NUM
ajst-29869	179	2	,	,	PUNCT
ajst-29869	179	3	dec	dec	PROPN
ajst-29869	179	4	.	.	PROPN
ajst-29869	179	5	2020	2020	NUM
ajst-29869	179	6	.	.	PUNCT
ajst-29869	180	1	[	[	X
ajst-29869	180	2	7	7	X
ajst-29869	180	3	]	]	PUNCT
ajst-29869	180	4	j.	j.	PROPN
ajst-29869	180	5	liu	liu	PROPN
ajst-29869	180	6	,	,	PUNCT
ajst-29869	180	7	z.	z.	PROPN
ajst-29869	180	8	han	han	PROPN
ajst-29869	180	9	,	,	PUNCT
ajst-29869	180	10	and	and	CCONJ
ajst-29869	180	11	y.	y.	PROPN
ajst-29869	180	12	ma	ma	PROPN
ajst-29869	180	13	,	,	PUNCT
ajst-29869	180	14	“	"	PUNCT
ajst-29869	180	15	a	a	DET
ajst-29869	180	16	novel	novel	ADJ
ajst-29869	180	17	feature	feature	NOUN
ajst-29869	180	18	selection	selection	NOUN
ajst-29869	180	19	method	method	NOUN
ajst-29869	180	20	based	base	VERB
ajst-29869	180	21	on	on	ADP
ajst-29869	180	22	improved	improved	ADJ
ajst-29869	180	23	relieff	relieff	NOUN
ajst-29869	180	24	and	and	CCONJ
ajst-29869	180	25	gray	gray	ADJ
ajst-29869	180	26	wolf	wolf	PROPN
ajst-29869	180	27	optimization	optimization	NOUN
ajst-29869	180	28	algorithm	algorithm	NOUN
ajst-29869	180	29	for	for	ADP
ajst-29869	180	30	remote	remote	ADJ
ajst-29869	180	31	sensing	sense	VERB
ajst-29869	180	32	image	image	NOUN
ajst-29869	180	33	classification	classification	NOUN
ajst-29869	180	34	,	,	PUNCT
ajst-29869	180	35	”	"	PUNCT
ajst-29869	180	36	ieee	ieee	NOUN
ajst-29869	180	37	trans	tran	NOUN
ajst-29869	180	38	.	.	PUNCT
ajst-29869	181	1	geosci	geosci	PROPN
ajst-29869	181	2	.	.	PUNCT
ajst-29869	182	1	remote	remote	PROPN
ajst-29869	182	2	sens	sens	PROPN
ajst-29869	182	3	.	.	PROPN
ajst-29869	182	4	,	,	PUNCT
ajst-29869	182	5	vol	vol	NOUN
ajst-29869	182	6	.	.	PROPN
ajst-29869	182	7	60	60	NUM
ajst-29869	182	8	,	,	PUNCT
ajst-29869	182	9	no	no	INTJ
ajst-29869	182	10	.	.	NOUN
ajst-29869	182	11	7	7	NUM
ajst-29869	182	12	,	,	PUNCT
ajst-29869	182	13	pp	pp	ADJ
ajst-29869	182	14	.	.	PUNCT
ajst-29869	183	1	4319–4329	4319–4329	NUM
ajst-29869	183	2	,	,	PUNCT
ajst-29869	183	3	jul	jul	PROPN
ajst-29869	183	4	.	.	PROPN
ajst-29869	183	5	2022	2022	NUM
ajst-29869	183	6	.	.	PUNCT
ajst-29869	184	1	[	[	X
ajst-29869	184	2	8	8	NUM
ajst-29869	184	3	]	]	X
ajst-29869	184	4	y.	y.	PROPN
ajst-29869	184	5	xu	xu	PROPN
ajst-29869	184	6	,	,	PUNCT
ajst-29869	184	7	s.	s.	PROPN
ajst-29869	184	8	zhang	zhang	PROPN
ajst-29869	184	9	,	,	PUNCT
ajst-29869	184	10	and	and	CCONJ
ajst-29869	184	11	q.	q.	PROPN
ajst-29869	184	12	lu	lu	PROPN
ajst-29869	184	13	,	,	PUNCT
ajst-29869	184	14	“	"	PUNCT
ajst-29869	184	15	feature	feature	NOUN
ajst-29869	184	16	selection	selection	NOUN
ajst-29869	184	17	and	and	CCONJ
ajst-29869	184	18	classification	classification	NOUN
ajst-29869	184	19	of	of	ADP
ajst-29869	184	20	remote	remote	ADJ
ajst-29869	184	21	sensing	sensing	NOUN
ajst-29869	184	22	images	image	NOUN
ajst-29869	184	23	based	base	VERB
ajst-29869	184	24	on	on	ADP
ajst-29869	184	25	hybrid	hybrid	ADJ
ajst-29869	184	26	gwo	gwo	NOUN
ajst-29869	184	27	and	and	CCONJ
ajst-29869	184	28	svm	svm	ADJ
ajst-29869	184	29	,	,	PUNCT
ajst-29869	184	30	”	"	PUNCT
ajst-29869	184	31	remote	remote	ADJ
ajst-29869	184	32	sensing	sensing	NOUN
ajst-29869	184	33	,	,	PUNCT
ajst-29869	184	34	vol	vol	NOUN
ajst-29869	184	35	.	.	PROPN
ajst-29869	184	36	13	13	NUM
ajst-29869	184	37	,	,	PUNCT
ajst-29869	184	38	no	no	INTJ
ajst-29869	184	39	.	.	NOUN
ajst-29869	184	40	18	18	NUM
ajst-29869	184	41	,	,	PUNCT
ajst-29869	184	42	pp	pp	ADJ
ajst-29869	184	43	.	.	PUNCT
ajst-29869	185	1	3548–3560	3548–3560	NUM
ajst-29869	185	2	,	,	PUNCT
ajst-29869	185	3	sept	sept	PROPN
ajst-29869	185	4	.	.	PROPN
ajst-29869	185	5	2021	2021	NUM
ajst-29869	185	6	.	.	PUNCT
ajst-29869	186	1	150	150	NUM
ajst-29869	187	1	[	[	SYM
ajst-29869	187	2	9	9	NUM
ajst-29869	187	3	]	]	PUNCT
ajst-29869	187	4	x.	x.	NOUN
ajst-29869	187	5	wang	wang	PROPN
ajst-29869	187	6	,	,	PUNCT
ajst-29869	187	7	j.	j.	PROPN
ajst-29869	187	8	yang	yang	PROPN
ajst-29869	187	9	,	,	PUNCT
ajst-29869	187	10	and	and	CCONJ
ajst-29869	187	11	z.	z.	PROPN
ajst-29869	187	12	zhang	zhang	PROPN
ajst-29869	187	13	,	,	PUNCT
ajst-29869	187	14	“	"	PUNCT
ajst-29869	187	15	remote	remote	ADJ
ajst-29869	187	16	sensing	sense	VERB
ajst-29869	187	17	image	image	NOUN
ajst-29869	187	18	classification	classification	NOUN
ajst-29869	187	19	using	use	VERB
ajst-29869	187	20	a	a	DET
ajst-29869	187	21	feature	feature	NOUN
ajst-29869	187	22	selection	selection	NOUN
ajst-29869	187	23	method	method	NOUN
ajst-29869	187	24	based	base	VERB
ajst-29869	187	25	on	on	ADP
ajst-29869	187	26	gray	gray	ADJ
ajst-29869	187	27	wolf	wolf	PROPN
ajst-29869	187	28	optimization	optimization	NOUN
ajst-29869	187	29	algorithm	algorithm	NOUN
ajst-29869	187	30	and	and	CCONJ
ajst-29869	187	31	decision	decision	NOUN
ajst-29869	187	32	tree	tree	NOUN
ajst-29869	187	33	,	,	PUNCT
ajst-29869	187	34	”	"	PUNCT
ajst-29869	187	35	comput	comput	NOUN
ajst-29869	187	36	.	.	PUNCT
ajst-29869	188	1	geosci	geosci	PROPN
ajst-29869	188	2	.	.	PUNCT
ajst-29869	188	3	,	,	PUNCT
ajst-29869	188	4	vol	vol	NOUN
ajst-29869	188	5	.	.	PROPN
ajst-29869	188	6	149	149	NUM
ajst-29869	188	7	,	,	PUNCT
ajst-29869	188	8	pp	pp	ADJ
ajst-29869	188	9	.	.	PUNCT
ajst-29869	188	10	104670	104670	NUM
ajst-29869	188	11	,	,	PUNCT
ajst-29869	188	12	oct	oct	PROPN
ajst-29869	188	13	.	.	PROPN
ajst-29869	188	14	2021	2021	NUM
ajst-29869	188	15	.	.	PUNCT
ajst-29869	189	1	[	[	X
ajst-29869	189	2	10	10	NUM
ajst-29869	189	3	]	]	X
ajst-29869	189	4	y.	y.	PROPN
ajst-29869	189	5	tang	tang	PROPN
ajst-29869	189	6	,	,	PUNCT
ajst-29869	189	7	w.	w.	PROPN
ajst-29869	189	8	xu	xu	PROPN
ajst-29869	189	9	,	,	PUNCT
ajst-29869	189	10	and	and	CCONJ
ajst-29869	189	11	j.	j.	PROPN
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ajst-29869	189	13	,	,	PUNCT
ajst-29869	189	14	“	"	PUNCT
ajst-29869	189	15	a	a	DET
ajst-29869	189	16	new	new	ADJ
ajst-29869	189	17	hybrid	hybrid	ADJ
ajst-29869	189	18	feature	feature	NOUN
ajst-29869	189	19	selection	selection	NOUN
ajst-29869	189	20	approach	approach	NOUN
ajst-29869	189	21	for	for	ADP
ajst-29869	189	22	remote	remote	ADJ
ajst-29869	189	23	sensing	sense	VERB
ajst-29869	189	24	image	image	NOUN
ajst-29869	189	25	classification	classification	NOUN
ajst-29869	189	26	:	:	PUNCT
ajst-29869	189	27	relief	relief	NOUN
ajst-29869	189	28	and	and	CCONJ
ajst-29869	189	29	mutual	mutual	ADJ
ajst-29869	189	30	information	information	NOUN
ajst-29869	189	31	-	-	PUNCT
ajst-29869	189	32	based	base	VERB
ajst-29869	189	33	grey	grey	PROPN
ajst-29869	189	34	wolf	wolf	PROPN
ajst-29869	189	35	optimization	optimization	NOUN
ajst-29869	189	36	,	,	PUNCT
ajst-29869	189	37	”	"	PUNCT
ajst-29869	189	38	j.	j.	PROPN
ajst-29869	189	39	appl	appl	PROPN
ajst-29869	189	40	.	.	PROPN
ajst-29869	190	1	remote	remote	PROPN
ajst-29869	190	2	sens	sens	PROPN
ajst-29869	190	3	.	.	PROPN
ajst-29869	190	4	,	,	PUNCT
ajst-29869	190	5	vol	vol	NOUN
ajst-29869	190	6	.	.	PROPN
ajst-29869	191	1	14	14	NUM
ajst-29869	191	2	,	,	PUNCT
ajst-29869	191	3	no	no	INTJ
ajst-29869	191	4	.	.	NOUN
ajst-29869	191	5	2	2	NUM
ajst-29869	191	6	,	,	PUNCT
ajst-29869	191	7	pp	pp	ADJ
ajst-29869	191	8	.	.	PUNCT
ajst-29869	192	1	026501	026501	NUM
ajst-29869	192	2	,	,	PUNCT
ajst-29869	192	3	apr	apr	PROPN
ajst-29869	192	4	.	.	PUNCT
ajst-29869	193	1	2020	2020	NUM
ajst-29869	193	2	.	.	PUNCT
ajst-29869	194	1	[	[	X
ajst-29869	194	2	11	11	NUM
ajst-29869	194	3	]	]	PUNCT
ajst-29869	194	4	l.	l.	PROPN
ajst-29869	194	5	liu	liu	PROPN
ajst-29869	194	6	,	,	PUNCT
ajst-29869	194	7	s.	s.	PROPN
ajst-29869	194	8	xie	xie	PROPN
ajst-29869	194	9	,	,	PUNCT
ajst-29869	194	10	and	and	CCONJ
ajst-29869	194	11	w.	w.	PROPN
ajst-29869	194	12	zheng	zheng	PROPN
ajst-29869	194	13	,	,	PUNCT
ajst-29869	194	14	“	"	PUNCT
ajst-29869	194	15	remote	remote	ADJ
ajst-29869	194	16	sensing	sense	VERB
ajst-29869	194	17	image	image	NOUN
ajst-29869	194	18	classification	classification	NOUN
ajst-29869	194	19	using	use	VERB
ajst-29869	194	20	hybrid	hybrid	ADJ
ajst-29869	194	21	feature	feature	NOUN
ajst-29869	194	22	selection	selection	NOUN
ajst-29869	194	23	and	and	CCONJ
ajst-29869	194	24	machine	machine	NOUN
ajst-29869	194	25	learning	learning	NOUN
ajst-29869	194	26	methods	method	NOUN
ajst-29869	194	27	,	,	PUNCT
ajst-29869	194	28	”	"	PUNCT
ajst-29869	194	29	sensors	sensor	NOUN
ajst-29869	194	30	,	,	PUNCT
ajst-29869	194	31	vol	vol	NOUN
ajst-29869	194	32	.	.	PROPN
ajst-29869	194	33	21	21	NUM
ajst-29869	194	34	,	,	PUNCT
ajst-29869	194	35	no	no	INTJ
ajst-29869	194	36	.	.	NOUN
ajst-29869	194	37	3	3	NUM
ajst-29869	194	38	,	,	PUNCT
ajst-29869	194	39	pp	pp	ADJ
ajst-29869	194	40	.	.	PUNCT
ajst-29869	195	1	798–812	798–812	NUM
ajst-29869	195	2	,	,	PUNCT
ajst-29869	195	3	mar	mar	PROPN
ajst-29869	195	4	.	.	PROPN
ajst-29869	195	5	2021	2021	NUM
ajst-29869	195	6	.	.	PUNCT
ajst-29869	196	1	[	[	X
ajst-29869	196	2	12	12	NUM
ajst-29869	196	3	]	]	PUNCT
ajst-29869	196	4	z.	z.	PROPN
ajst-29869	196	5	wang	wang	PROPN
ajst-29869	196	6	,	,	PUNCT
ajst-29869	196	7	h.	h.	PROPN
ajst-29869	196	8	zhang	zhang	PROPN
ajst-29869	196	9	,	,	PUNCT
ajst-29869	196	10	and	and	CCONJ
ajst-29869	196	11	p.	p.	PROPN
ajst-29869	196	12	wang	wang	PROPN
ajst-29869	196	13	,	,	PUNCT
ajst-29869	196	14	“	"	PUNCT
ajst-29869	196	15	feature	feature	NOUN
ajst-29869	196	16	selection	selection	NOUN
ajst-29869	196	17	for	for	ADP
ajst-29869	196	18	remote	remote	ADJ
ajst-29869	196	19	sensing	sense	VERB
ajst-29869	196	20	image	image	NOUN
ajst-29869	196	21	classification	classification	NOUN
ajst-29869	196	22	based	base	VERB
ajst-29869	196	23	on	on	ADP
ajst-29869	196	24	hybrid	hybrid	ADJ
ajst-29869	196	25	algorithms	algorithm	NOUN
ajst-29869	196	26	of	of	ADP
ajst-29869	196	27	gray	gray	ADJ
ajst-29869	196	28	wolf	wolf	NOUN
ajst-29869	196	29	optimization	optimization	NOUN
ajst-29869	196	30	and	and	CCONJ
ajst-29869	196	31	random	random	ADJ
ajst-29869	196	32	forests	forest	NOUN
ajst-29869	196	33	,	,	PUNCT
ajst-29869	196	34	”	"	PUNCT
ajst-29869	196	35	j.	j.	PROPN
ajst-29869	196	36	sensors	sensors	PROPN
ajst-29869	196	37	,	,	PUNCT
ajst-29869	196	38	vol	vol	NOUN
ajst-29869	196	39	.	.	PROPN
ajst-29869	196	40	2021	2021	NUM
ajst-29869	196	41	,	,	PUNCT
ajst-29869	196	42	pp	pp	ADJ
ajst-29869	196	43	.	.	PUNCT
ajst-29869	197	1	183458	183458	NUM
ajst-29869	197	2	,	,	PUNCT
ajst-29869	197	3	feb	feb	PROPN
ajst-29869	197	4	.	.	PROPN
ajst-29869	197	5	2021	2021	NUM
ajst-29869	197	6	.	.	PUNCT
ajst-29869	198	1	[	[	X
ajst-29869	198	2	13	13	NUM
ajst-29869	198	3	]	]	PUNCT
ajst-29869	198	4	j.	j.	PROPN
ajst-29869	198	5	yang	yang	PROPN
ajst-29869	198	6	,	,	PUNCT
ajst-29869	198	7	w.	w.	PROPN
ajst-29869	198	8	xie	xie	PROPN
ajst-29869	198	9	,	,	PUNCT
ajst-29869	198	10	and	and	CCONJ
ajst-29869	198	11	y.	y.	PROPN
ajst-29869	198	12	chen	chen	PROPN
ajst-29869	198	13	,	,	PUNCT
ajst-29869	198	14	“	"	PUNCT
ajst-29869	198	15	a	a	DET
ajst-29869	198	16	novel	novel	ADJ
ajst-29869	198	17	feature	feature	NOUN
ajst-29869	198	18	selection	selection	NOUN
ajst-29869	198	19	method	method	NOUN
ajst-29869	198	20	for	for	ADP
ajst-29869	198	21	hyperspectral	hyperspectral	ADJ
ajst-29869	198	22	remote	remote	ADJ
ajst-29869	198	23	sensing	sense	VERB
ajst-29869	198	24	image	image	NOUN
ajst-29869	198	25	classification	classification	NOUN
ajst-29869	198	26	based	base	VERB
ajst-29869	198	27	on	on	ADP
ajst-29869	198	28	hybrid	hybrid	ADJ
ajst-29869	198	29	genetic	genetic	ADJ
ajst-29869	198	30	algorithm	algorithm	NOUN
ajst-29869	198	31	and	and	CCONJ
ajst-29869	198	32	gray	gray	ADJ
ajst-29869	198	33	wolf	wolf	PROPN
ajst-29869	198	34	optimization	optimization	NOUN
ajst-29869	198	35	,	,	PUNCT
ajst-29869	198	36	”	"	PUNCT
ajst-29869	198	37	ieee	ieee	NOUN
ajst-29869	198	38	trans	trans	PROPN
ajst-29869	198	39	.	.	PUNCT
ajst-29869	199	1	geosci	geosci	PROPN
ajst-29869	199	2	.	.	PUNCT
ajst-29869	200	1	remote	remote	PROPN
ajst-29869	200	2	sens	sens	PROPN
ajst-29869	200	3	.	.	PROPN
ajst-29869	200	4	,	,	PUNCT
ajst-29869	200	5	vol	vol	NOUN
ajst-29869	200	6	.	.	PROPN
ajst-29869	201	1	59	59	NUM
ajst-29869	201	2	,	,	PUNCT
ajst-29869	201	3	no	no	INTJ
ajst-29869	201	4	.	.	NOUN
ajst-29869	201	5	11	11	NUM
ajst-29869	201	6	,	,	PUNCT
ajst-29869	201	7	pp	pp	ADJ
ajst-29869	201	8	.	.	PUNCT
ajst-29869	202	1	9060	9060	NUM
ajst-29869	202	2	–	–	PUNCT
ajst-29869	202	3	9074	9074	NUM
ajst-29869	202	4	,	,	PUNCT
ajst-29869	202	5	nov	nov	PROPN
ajst-29869	202	6	.	.	PROPN
ajst-29869	202	7	2021	2021	NUM
ajst-29869	202	8	.	.	PUNCT
ajst-29869	203	1	[	[	X
ajst-29869	203	2	14	14	NUM
ajst-29869	203	3	]	]	X
ajst-29869	203	4	h.	h.	PROPN
ajst-29869	203	5	chen	chen	PROPN
ajst-29869	203	6	,	,	PUNCT
ajst-29869	203	7	y.	y.	PROPN
ajst-29869	203	8	wu	wu	PROPN
ajst-29869	203	9	,	,	PUNCT
ajst-29869	203	10	and	and	CCONJ
ajst-29869	203	11	m.	m.	PROPN
ajst-29869	203	12	jiang	jiang	PROPN
ajst-29869	203	13	,	,	PUNCT
ajst-29869	203	14	“	"	PUNCT
ajst-29869	203	15	feature	feature	NOUN
ajst-29869	203	16	selection	selection	NOUN
ajst-29869	203	17	for	for	ADP
ajst-29869	203	18	highdimensional	highdimensional	ADJ
ajst-29869	203	19	remote	remote	ADJ
ajst-29869	203	20	sensing	sense	VERB
ajst-29869	203	21	data	datum	NOUN
ajst-29869	203	22	using	use	VERB
ajst-29869	203	23	a	a	DET
ajst-29869	203	24	hybrid	hybrid	ADJ
ajst-29869	203	25	method	method	NOUN
ajst-29869	203	26	of	of	ADP
ajst-29869	203	27	the	the	DET
ajst-29869	203	28	genetic	genetic	ADJ
ajst-29869	203	29	algorithm	algorithm	NOUN
ajst-29869	203	30	and	and	CCONJ
ajst-29869	203	31	simulated	simulated	ADJ
ajst-29869	203	32	annealing	annealing	NOUN
ajst-29869	203	33	,	,	PUNCT
ajst-29869	203	34	”	"	PUNCT
ajst-29869	203	35	comput	comput	NOUN
ajst-29869	203	36	.	.	PUNCT
ajst-29869	204	1	geosci	geosci	PROPN
ajst-29869	204	2	.	.	PUNCT
ajst-29869	204	3	,	,	PUNCT
ajst-29869	204	4	vol	vol	NOUN
ajst-29869	204	5	.	.	PROPN
ajst-29869	204	6	130	130	NUM
ajst-29869	204	7	,	,	PUNCT
ajst-29869	204	8	pp	pp	ADJ
ajst-29869	204	9	.	.	PUNCT
ajst-29869	205	1	38–47	38–47	NUM
ajst-29869	205	2	,	,	PUNCT
ajst-29869	205	3	feb	feb	PROPN
ajst-29869	205	4	.	.	PROPN
ajst-29869	205	5	2020	2020	NUM
ajst-29869	205	6	.	.	PUNCT
ajst-29869	206	1	[	[	X
ajst-29869	206	2	15	15	NUM
ajst-29869	206	3	]	]	X
ajst-29869	206	4	w.	w.	PROPN
ajst-29869	206	5	liu	liu	PROPN
ajst-29869	206	6	,	,	PUNCT
ajst-29869	206	7	x.	x.	PROPN
ajst-29869	206	8	zhang	zhang	PROPN
ajst-29869	206	9	,	,	PUNCT
ajst-29869	206	10	and	and	CCONJ
ajst-29869	206	11	h.	h.	PROPN
ajst-29869	206	12	yang	yang	PROPN
ajst-29869	206	13	,	,	PUNCT
ajst-29869	206	14	“	"	PUNCT
ajst-29869	206	15	a	a	DET
ajst-29869	206	16	novel	novel	ADJ
ajst-29869	206	17	hybrid	hybrid	ADJ
ajst-29869	206	18	feature	feature	NOUN
ajst-29869	206	19	selection	selection	NOUN
ajst-29869	206	20	method	method	NOUN
ajst-29869	206	21	based	base	VERB
ajst-29869	206	22	on	on	ADP
ajst-29869	206	23	relieff	relieff	NOUN
ajst-29869	206	24	and	and	CCONJ
ajst-29869	206	25	whale	whale	NOUN
ajst-29869	206	26	optimization	optimization	NOUN
ajst-29869	206	27	algorithm	algorithm	NOUN
ajst-29869	206	28	for	for	ADP
ajst-29869	206	29	remote	remote	ADJ
ajst-29869	206	30	sensing	sense	VERB
ajst-29869	206	31	image	image	NOUN
ajst-29869	206	32	classification	classification	NOUN
ajst-29869	206	33	,	,	PUNCT
ajst-29869	206	34	”	"	PUNCT
ajst-29869	206	35	remote	remote	ADJ
ajst-29869	206	36	sensing	sensing	NOUN
ajst-29869	206	37	,	,	PUNCT
ajst-29869	206	38	vol	vol	NOUN
ajst-29869	206	39	.	.	PROPN
ajst-29869	206	40	12	12	NUM
ajst-29869	206	41	,	,	PUNCT
ajst-29869	206	42	no	no	INTJ
ajst-29869	206	43	.	.	NOUN
ajst-29869	206	44	22	22	NUM
ajst-29869	206	45	,	,	PUNCT
ajst-29869	206	46	pp	pp	ADJ
ajst-29869	206	47	.	.	PUNCT
ajst-29869	206	48	3745–3758	3745–3758	NUM
ajst-29869	206	49	,	,	PUNCT
ajst-29869	206	50	nov	nov	PROPN
ajst-29869	206	51	.	.	PROPN
ajst-29869	206	52	2020	2020	NUM
ajst-29869	206	53	.	.	PUNCT
ajst-29869	207	1	[	[	X
ajst-29869	207	2	16	16	NUM
ajst-29869	207	3	]	]	PUNCT
ajst-29869	207	4	x.	x.	PROPN
ajst-29869	207	5	xie	xie	PROPN
ajst-29869	207	6	,	,	PUNCT
ajst-29869	207	7	w.	w.	PROPN
ajst-29869	207	8	li	li	PROPN
ajst-29869	207	9	,	,	PUNCT
ajst-29869	207	10	and	and	CCONJ
ajst-29869	207	11	j.	j.	PROPN
ajst-29869	207	12	li	li	PROPN
ajst-29869	207	13	,	,	PUNCT
ajst-29869	207	14	“	"	PUNCT
ajst-29869	207	15	hybrid	hybrid	ADJ
ajst-29869	207	16	feature	feature	NOUN
ajst-29869	207	17	selection	selection	NOUN
ajst-29869	207	18	algorithm	algorithm	NOUN
ajst-29869	207	19	based	base	VERB
ajst-29869	207	20	on	on	ADP
ajst-29869	207	21	gray	gray	ADJ
ajst-29869	207	22	wolf	wolf	PROPN
ajst-29869	207	23	optimization	optimization	NOUN
ajst-29869	207	24	and	and	CCONJ
ajst-29869	207	25	principal	principal	ADJ
ajst-29869	207	26	component	component	NOUN
ajst-29869	207	27	analysis	analysis	NOUN
ajst-29869	207	28	for	for	ADP
ajst-29869	207	29	hyperspectral	hyperspectral	ADJ
ajst-29869	207	30	remote	remote	ADJ
ajst-29869	207	31	sensing	sense	VERB
ajst-29869	207	32	image	image	NOUN
ajst-29869	207	33	classification	classification	NOUN
ajst-29869	207	34	,	,	PUNCT
ajst-29869	207	35	”	"	PUNCT
ajst-29869	207	36	computers	computer	NOUN
ajst-29869	207	37	,	,	PUNCT
ajst-29869	207	38	environment	environment	NOUN
ajst-29869	207	39	and	and	CCONJ
ajst-29869	207	40	urban	urban	ADJ
ajst-29869	207	41	systems	system	NOUN
ajst-29869	207	42	,	,	PUNCT
ajst-29869	207	43	vol	vol	NOUN
ajst-29869	207	44	.	.	PROPN
ajst-29869	207	45	87	87	NUM
ajst-29869	207	46	,	,	PUNCT
ajst-29869	207	47	pp	pp	ADJ
ajst-29869	207	48	.	.	PUNCT
ajst-29869	207	49	101610	101610	NUM
ajst-29869	207	50	,	,	PUNCT
ajst-29869	207	51	mar	mar	PROPN
ajst-29869	207	52	.	.	PROPN
ajst-29869	207	53	2021	2021	NUM
ajst-29869	207	54	.	.	PUNCT
ajst-29869	208	1	[	[	X
ajst-29869	208	2	17	17	NUM
ajst-29869	208	3	]	]	PUNCT
ajst-29869	208	4	q.	q.	PROPN
ajst-29869	208	5	zhang	zhang	PROPN
ajst-29869	208	6	,	,	PUNCT
ajst-29869	208	7	x.	x.	PROPN
ajst-29869	208	8	zhao	zhao	PROPN
ajst-29869	208	9	,	,	PUNCT
ajst-29869	208	10	and	and	CCONJ
ajst-29869	208	11	l.	l.	PROPN
ajst-29869	208	12	zhang	zhang	PROPN
ajst-29869	208	13	,	,	PUNCT
ajst-29869	208	14	“	"	PUNCT
ajst-29869	208	15	improving	improve	VERB
ajst-29869	208	16	remote	remote	ADJ
ajst-29869	208	17	sensing	sense	VERB
ajst-29869	208	18	image	image	NOUN
ajst-29869	208	19	classification	classification	NOUN
ajst-29869	208	20	through	through	ADP
ajst-29869	208	21	feature	feature	NOUN
ajst-29869	208	22	selection	selection	NOUN
ajst-29869	208	23	and	and	CCONJ
ajst-29869	208	24	dimensionality	dimensionality	NOUN
ajst-29869	208	25	reduction	reduction	NOUN
ajst-29869	208	26	methods	method	NOUN
ajst-29869	208	27	,	,	PUNCT
ajst-29869	208	28	”	"	PUNCT
ajst-29869	208	29	isprs	isprs	PROPN
ajst-29869	208	30	ann	ann	PROPN
ajst-29869	208	31	.	.	PUNCT
ajst-29869	208	32	photogramm	photogramm	PROPN
ajst-29869	208	33	.	.	PUNCT
ajst-29869	209	1	remote	remote	PROPN
ajst-29869	209	2	sens	sens	PROPN
ajst-29869	209	3	.	.	PROPN
ajst-29869	209	4	spatial	spatial	PROPN
ajst-29869	209	5	inf	inf	PROPN
ajst-29869	209	6	.	.	PUNCT
ajst-29869	210	1	sci	sci	PROPN
ajst-29869	210	2	.	.	PROPN
ajst-29869	210	3	,	,	PUNCT
ajst-29869	210	4	vol	vol	NOUN
ajst-29869	210	5	.	.	PUNCT
ajst-29869	211	1	vi-3	vi-3	PROPN
ajst-29869	211	2	,	,	PUNCT
ajst-29869	211	3	pp	pp	X
ajst-29869	211	4	.	.	PUNCT
ajst-29869	212	1	37–46	37–46	NUM
ajst-29869	212	2	,	,	PUNCT
ajst-29869	212	3	sept	sept	PROPN
ajst-29869	212	4	.	.	PROPN
ajst-29869	212	5	2021	2021	NUM
ajst-29869	212	6	.	.	PUNCT
ajst-29869	213	1	[	[	X
ajst-29869	213	2	18	18	NUM
ajst-29869	213	3	]	]	PUNCT
ajst-29869	213	4	m.	m.	NOUN
ajst-29869	213	5	liu	liu	PROPN
ajst-29869	213	6	,	,	PUNCT
ajst-29869	213	7	z.	z.	PROPN
ajst-29869	213	8	li	li	PROPN
ajst-29869	213	9	,	,	PUNCT
ajst-29869	213	10	and	and	CCONJ
ajst-29869	213	11	y.	y.	PROPN
ajst-29869	213	12	yu	yu	PROPN
ajst-29869	213	13	,	,	PUNCT
ajst-29869	213	14	“	"	PUNCT
ajst-29869	213	15	a	a	DET
ajst-29869	213	16	new	new	ADJ
ajst-29869	213	17	approach	approach	NOUN
ajst-29869	213	18	for	for	ADP
ajst-29869	213	19	feature	feature	NOUN
ajst-29869	213	20	selection	selection	NOUN
ajst-29869	213	21	in	in	ADP
ajst-29869	213	22	remote	remote	ADJ
ajst-29869	213	23	sensing	sense	VERB
ajst-29869	213	24	image	image	NOUN
ajst-29869	213	25	classification	classification	NOUN
ajst-29869	213	26	using	use	VERB
ajst-29869	213	27	hybrid	hybrid	ADJ
ajst-29869	213	28	eature	eature	NOUN
ajst-29869	213	29	selection	selection	NOUN
ajst-29869	213	30	algorithms	algorithm	NOUN
ajst-29869	213	31	,	,	PUNCT
ajst-29869	213	32	”	"	PUNCT
ajst-29869	213	33	j.	j.	PROPN
ajst-29869	213	34	earth	earth	PROPN
ajst-29869	213	35	sci	sci	PROPN
ajst-29869	213	36	.	.	PROPN
ajst-29869	213	37	,	,	PUNCT
ajst-29869	213	38	vol	vol	NOUN
ajst-29869	213	39	.	.	PUNCT
ajst-29869	213	40	2020	2020	NUM
ajst-29869	213	41	,	,	PUNCT
ajst-29869	213	42	pp	pp	ADV
ajst-29869	213	43	.	.	PUNCT
ajst-29869	214	1	320–329	320–329	NUM
ajst-29869	214	2	,	,	PUNCT
ajst-29869	214	3	jun	jun	PROPN
ajst-29869	214	4	.	.	PROPN
ajst-29869	214	5	2020	2020	NUM
ajst-29869	214	6	.	.	PUNCT