id	sid	tid	token	lemma	pos
fcis-2971	1	1	frontiers	frontier	NOUN
fcis-2971	1	2	in	in	ADP
fcis-2971	1	3	computing	computing	NOUN
fcis-2971	1	4	and	and	CCONJ
fcis-2971	1	5	intelligent	intelligent	ADJ
fcis-2971	1	6	systems	system	NOUN
fcis-2971	1	7	issn	issn	VERB
fcis-2971	1	8	:	:	PUNCT
fcis-2971	1	9	2832	2832	NUM
fcis-2971	1	10	-	-	SYM
fcis-2971	1	11	6024	6024	NUM
fcis-2971	1	12	|	|	NOUN
fcis-2971	1	13	vol	vol	NOUN
fcis-2971	1	14	.	.	PROPN
fcis-2971	2	1	2	2	NUM
fcis-2971	2	2	,	,	PUNCT
fcis-2971	2	3	no	no	INTJ
fcis-2971	2	4	.	.	NOUN
fcis-2971	2	5	1	1	NUM
fcis-2971	2	6	,	,	PUNCT
fcis-2971	2	7	2022	2022	NUM
fcis-2971	2	8	76	76	NUM
fcis-2971	2	9	a	a	DET
fcis-2971	2	10	network	network	NOUN
fcis-2971	2	11	intrusion	intrusion	NOUN
fcis-2971	2	12	detection	detection	NOUN
fcis-2971	2	13	model	model	NOUN
fcis-2971	2	14	based	base	VERB
fcis-2971	2	15	on	on	ADP
fcis-2971	2	16	principal	principal	ADJ
fcis-2971	2	17	component	component	NOUN
fcis-2971	2	18	analysis	analysis	NOUN
fcis-2971	2	19	and	and	CCONJ
fcis-2971	2	20	random	random	ADJ
fcis-2971	2	21	forest	forest	NOUN
fcis-2971	2	22	le	le	X
fcis-2971	2	23	yang	yang	PROPN
fcis-2971	2	24	1	1	NUM
fcis-2971	2	25	,	,	PUNCT
fcis-2971	2	26	hua	hua	PROPN
fcis-2971	3	1	chen2	chen2	NOUN
fcis-2971	3	2	*	*	PUNCT
fcis-2971	3	3	1	1	NUM
fcis-2971	3	4	foshan	foshan	PROPN
fcis-2971	3	5	network	network	NOUN
fcis-2971	3	6	security	security	NOUN
fcis-2971	3	7	emergency	emergency	NOUN
fcis-2971	3	8	command	command	NOUN
fcis-2971	3	9	center	center	NOUN
fcis-2971	3	10	,	,	PUNCT
fcis-2971	3	11	foshan	foshan	PROPN
fcis-2971	3	12	,	,	PUNCT
fcis-2971	3	13	528000	528000	NUM
fcis-2971	3	14	,	,	PUNCT
fcis-2971	3	15	china	china	PROPN
fcis-2971	3	16	2	2	NUM
fcis-2971	3	17	foshan	foshan	PROPN
fcis-2971	3	18	human	human	PROPN
fcis-2971	3	19	resources	resource	NOUN
fcis-2971	3	20	public	public	ADJ
fcis-2971	3	21	service	service	NOUN
fcis-2971	3	22	center	center	NOUN
fcis-2971	3	23	,	,	PUNCT
fcis-2971	3	24	foshan	foshan	PROPN
fcis-2971	3	25	,	,	PUNCT
fcis-2971	3	26	528000	528000	NUM
fcis-2971	3	27	,	,	PUNCT
fcis-2971	3	28	china	china	PROPN
fcis-2971	3	29	*	*	PUNCT
fcis-2971	3	30	corresponding	correspond	VERB
fcis-2971	3	31	author	author	NOUN
fcis-2971	3	32	:	:	PUNCT
fcis-2971	4	1	hua	hua	PROPN
fcis-2971	4	2	chen	chen	PROPN
fcis-2971	4	3	(	(	PUNCT
fcis-2971	4	4	email	email	NOUN
fcis-2971	4	5	:	:	PUNCT
fcis-2971	4	6	chenh393@mail2.sysu.edu.cn	chenh393@mail2.sysu.edu.cn	NOUN
fcis-2971	4	7	)	)	PUNCT
fcis-2971	4	8	abstract	abstract	NOUN
fcis-2971	4	9	:	:	PUNCT
fcis-2971	4	10	network	network	NOUN
fcis-2971	4	11	intrusion	intrusion	PROPN
fcis-2971	4	12	data	datum	NOUN
fcis-2971	4	13	has	have	VERB
fcis-2971	4	14	the	the	DET
fcis-2971	4	15	characteristics	characteristic	NOUN
fcis-2971	4	16	of	of	ADP
fcis-2971	4	17	high	high	ADJ
fcis-2971	4	18	dimension	dimension	NOUN
fcis-2971	4	19	,	,	PUNCT
fcis-2971	4	20	nonlinearity	nonlinearity	NOUN
fcis-2971	4	21	and	and	CCONJ
fcis-2971	4	22	redundancy	redundancy	NOUN
fcis-2971	4	23	.	.	PUNCT
fcis-2971	5	1	to	to	PART
fcis-2971	5	2	solve	solve	VERB
fcis-2971	5	3	the	the	DET
fcis-2971	5	4	problem	problem	NOUN
fcis-2971	5	5	of	of	ADP
fcis-2971	5	6	low	low	ADJ
fcis-2971	5	7	detection	detection	NOUN
fcis-2971	5	8	rate	rate	NOUN
fcis-2971	5	9	of	of	ADP
fcis-2971	5	10	traditional	traditional	ADJ
fcis-2971	5	11	dimensionality	dimensionality	NOUN
fcis-2971	5	12	reduction	reduction	NOUN
fcis-2971	5	13	and	and	CCONJ
fcis-2971	5	14	detection	detection	NOUN
fcis-2971	5	15	methods	method	NOUN
fcis-2971	5	16	,	,	PUNCT
fcis-2971	5	17	a	a	DET
fcis-2971	5	18	network	network	NOUN
fcis-2971	5	19	intrusion	intrusion	NOUN
fcis-2971	5	20	detection	detection	NOUN
fcis-2971	5	21	method	method	NOUN
fcis-2971	5	22	based	base	VERB
fcis-2971	5	23	on	on	ADP
fcis-2971	5	24	principal	principal	ADJ
fcis-2971	5	25	component	component	NOUN
fcis-2971	5	26	analysis	analysis	NOUN
fcis-2971	5	27	combined	combine	VERB
fcis-2971	5	28	with	with	ADP
fcis-2971	5	29	random	random	ADJ
fcis-2971	5	30	forest	forest	NOUN
fcis-2971	5	31	is	be	AUX
fcis-2971	5	32	proposed	propose	VERB
fcis-2971	5	33	.	.	PUNCT
fcis-2971	6	1	firstly	firstly	ADV
fcis-2971	6	2	,	,	PUNCT
fcis-2971	6	3	the	the	DET
fcis-2971	6	4	data	data	NOUN
fcis-2971	6	5	dimension	dimension	NOUN
fcis-2971	6	6	of	of	ADP
fcis-2971	6	7	network	network	NOUN
fcis-2971	6	8	intrusion	intrusion	NOUN
fcis-2971	6	9	is	be	AUX
fcis-2971	6	10	reduced	reduce	VERB
fcis-2971	6	11	by	by	ADP
fcis-2971	6	12	principal	principal	ADJ
fcis-2971	6	13	component	component	NOUN
fcis-2971	6	14	analysis	analysis	NOUN
fcis-2971	6	15	to	to	PART
fcis-2971	6	16	eliminate	eliminate	VERB
fcis-2971	6	17	redundant	redundant	ADJ
fcis-2971	6	18	information	information	NOUN
fcis-2971	6	19	between	between	ADP
fcis-2971	6	20	data	datum	NOUN
fcis-2971	6	21	,	,	PUNCT
fcis-2971	6	22	and	and	CCONJ
fcis-2971	6	23	then	then	ADV
fcis-2971	6	24	the	the	DET
fcis-2971	6	25	processed	process	VERB
fcis-2971	6	26	data	data	NOUN
fcis-2971	6	27	is	be	AUX
fcis-2971	6	28	classified	classify	VERB
fcis-2971	6	29	and	and	CCONJ
fcis-2971	6	30	trained	train	VERB
fcis-2971	6	31	by	by	ADP
fcis-2971	6	32	using	use	VERB
fcis-2971	6	33	random	random	ADJ
fcis-2971	6	34	forest	forest	NOUN
fcis-2971	6	35	classifier	classifier	NOUN
fcis-2971	6	36	.	.	PUNCT
fcis-2971	7	1	the	the	DET
fcis-2971	7	2	algorithm	algorithm	NOUN
fcis-2971	7	3	is	be	AUX
fcis-2971	7	4	verified	verify	VERB
fcis-2971	7	5	by	by	ADP
fcis-2971	7	6	the	the	DET
fcis-2971	7	7	network	network	NOUN
fcis-2971	7	8	intrusion	intrusion	NOUN
fcis-2971	7	9	nsl_kdd	nsl_kdd	PART
fcis-2971	7	10	dataset	dataset	NOUN
fcis-2971	7	11	.	.	PUNCT
fcis-2971	8	1	the	the	DET
fcis-2971	8	2	experimental	experimental	ADJ
fcis-2971	8	3	results	result	NOUN
fcis-2971	8	4	show	show	VERB
fcis-2971	8	5	that	that	SCONJ
fcis-2971	8	6	compared	compare	VERB
fcis-2971	8	7	with	with	ADP
fcis-2971	8	8	other	other	ADJ
fcis-2971	8	9	network	network	NOUN
fcis-2971	8	10	intrusion	intrusion	NOUN
fcis-2971	8	11	detection	detection	NOUN
fcis-2971	8	12	methods	method	NOUN
fcis-2971	8	13	,	,	PUNCT
fcis-2971	8	14	the	the	DET
fcis-2971	8	15	method	method	NOUN
fcis-2971	8	16	has	have	AUX
fcis-2971	8	17	fast	fast	ADJ
fcis-2971	8	18	learning	learn	VERB
fcis-2971	8	19	speed	speed	NOUN
fcis-2971	8	20	,	,	PUNCT
fcis-2971	8	21	high	high	ADJ
fcis-2971	8	22	detection	detection	NOUN
fcis-2971	8	23	accuracy	accuracy	NOUN
fcis-2971	8	24	,	,	PUNCT
fcis-2971	8	25	low	low	ADJ
fcis-2971	8	26	false	false	ADJ
fcis-2971	8	27	negative	negative	ADJ
fcis-2971	8	28	rate	rate	NOUN
fcis-2971	8	29	and	and	CCONJ
fcis-2971	8	30	low	low	ADJ
fcis-2971	8	31	false	false	ADJ
fcis-2971	8	32	positive	positive	ADJ
fcis-2971	8	33	rate	rate	NOUN
fcis-2971	8	34	.	.	PUNCT
fcis-2971	9	1	an	an	DET
fcis-2971	9	2	efficient	efficient	ADJ
fcis-2971	9	3	,	,	PUNCT
fcis-2971	9	4	real	real	ADJ
fcis-2971	9	5	-	-	PUNCT
fcis-2971	9	6	time	time	NOUN
fcis-2971	9	7	and	and	CCONJ
fcis-2971	9	8	good	good	ADJ
fcis-2971	9	9	network	network	NOUN
fcis-2971	9	10	intrusion	intrusion	NOUN
fcis-2971	9	11	detection	detection	NOUN
fcis-2971	9	12	method	method	NOUN
fcis-2971	9	13	.	.	PUNCT
fcis-2971	10	1	keywords	keyword	NOUN
fcis-2971	10	2	:	:	PUNCT
fcis-2971	10	3	network	network	NOUN
fcis-2971	10	4	security	security	NOUN
fcis-2971	10	5	;	;	PUNCT
fcis-2971	10	6	intrusion	intrusion	NOUN
fcis-2971	10	7	detection	detection	NOUN
fcis-2971	10	8	;	;	PUNCT
fcis-2971	10	9	principal	principal	ADJ
fcis-2971	10	10	component	component	NOUN
fcis-2971	10	11	analysis	analysis	NOUN
fcis-2971	10	12	;	;	PUNCT
fcis-2971	10	13	random	random	ADJ
fcis-2971	10	14	forest	forest	NOUN
fcis-2971	10	15	.	.	PUNCT
fcis-2971	11	1	1	1	X
fcis-2971	11	2	.	.	X
fcis-2971	11	3	introduction	introduction	NOUN
fcis-2971	11	4	with	with	ADP
fcis-2971	11	5	the	the	DET
fcis-2971	11	6	development	development	NOUN
fcis-2971	11	7	and	and	CCONJ
fcis-2971	11	8	application	application	NOUN
fcis-2971	11	9	of	of	ADP
fcis-2971	11	10	information	information	NOUN
fcis-2971	11	11	technologies	technology	NOUN
fcis-2971	11	12	such	such	ADJ
fcis-2971	11	13	as	as	ADP
fcis-2971	11	14	cloud	cloud	NOUN
fcis-2971	11	15	computing	computing	NOUN
fcis-2971	11	16	and	and	CCONJ
fcis-2971	11	17	big	big	ADJ
fcis-2971	11	18	data	datum	NOUN
fcis-2971	11	19	,	,	PUNCT
fcis-2971	11	20	the	the	DET
fcis-2971	11	21	scale	scale	NOUN
fcis-2971	11	22	of	of	ADP
fcis-2971	11	23	various	various	ADJ
fcis-2971	11	24	server	server	NOUN
fcis-2971	11	25	cluster	cluster	NOUN
fcis-2971	11	26	networks	network	NOUN
fcis-2971	11	27	is	be	AUX
fcis-2971	11	28	becoming	become	VERB
fcis-2971	11	29	more	more	ADV
fcis-2971	11	30	and	and	CCONJ
fcis-2971	11	31	more	more	ADV
fcis-2971	11	32	large	large	ADJ
fcis-2971	11	33	and	and	CCONJ
fcis-2971	11	34	complex	complex	ADJ
fcis-2971	11	35	,	,	PUNCT
fcis-2971	11	36	and	and	CCONJ
fcis-2971	11	37	at	at	ADP
fcis-2971	11	38	the	the	DET
fcis-2971	11	39	same	same	ADJ
fcis-2971	11	40	time	time	NOUN
fcis-2971	11	41	,	,	PUNCT
fcis-2971	11	42	it	it	PRON
fcis-2971	11	43	is	be	AUX
fcis-2971	11	44	also	also	ADV
fcis-2971	11	45	facing	face	VERB
fcis-2971	11	46	more	more	ADJ
fcis-2971	11	47	and	and	CCONJ
fcis-2971	11	48	more	more	ADJ
fcis-2971	11	49	network	network	NOUN
fcis-2971	11	50	security	security	NOUN
fcis-2971	11	51	threats	threat	NOUN
fcis-2971	11	52	[	[	X
fcis-2971	11	53	1	1	NUM
fcis-2971	11	54	]	]	PUNCT
fcis-2971	11	55	.	.	PUNCT
fcis-2971	12	1	network	network	NOUN
fcis-2971	12	2	intrusion	intrusion	NOUN
fcis-2971	12	3	detection	detection	NOUN
fcis-2971	12	4	is	be	AUX
fcis-2971	12	5	one	one	NUM
fcis-2971	12	6	of	of	ADP
fcis-2971	12	7	the	the	DET
fcis-2971	12	8	effective	effective	ADJ
fcis-2971	12	9	means	mean	NOUN
fcis-2971	12	10	to	to	PART
fcis-2971	12	11	improve	improve	VERB
fcis-2971	12	12	network	network	NOUN
fcis-2971	12	13	security	security	NOUN
fcis-2971	12	14	.	.	PUNCT
fcis-2971	13	1	it	it	PRON
fcis-2971	13	2	judges	judge	VERB
fcis-2971	13	3	normal	normal	ADJ
fcis-2971	13	4	behavior	behavior	NOUN
fcis-2971	13	5	or	or	CCONJ
fcis-2971	13	6	abnormal	abnormal	ADJ
fcis-2971	13	7	behavior	behavior	NOUN
fcis-2971	13	8	according	accord	VERB
fcis-2971	13	9	to	to	ADP
fcis-2971	13	10	network	network	NOUN
fcis-2971	13	11	traffic	traffic	NOUN
fcis-2971	13	12	data	datum	NOUN
fcis-2971	13	13	or	or	CCONJ
fcis-2971	13	14	host	host	NOUN
fcis-2971	13	15	data	datum	NOUN
fcis-2971	13	16	,	,	PUNCT
fcis-2971	13	17	which	which	PRON
fcis-2971	13	18	can	can	AUX
fcis-2971	13	19	be	be	AUX
fcis-2971	13	20	abstracted	abstract	VERB
fcis-2971	13	21	into	into	ADP
fcis-2971	13	22	classification	classification	NOUN
fcis-2971	13	23	behavior	behavior	NOUN
fcis-2971	13	24	,	,	PUNCT
fcis-2971	13	25	and	and	CCONJ
fcis-2971	13	26	machine	machine	NOUN
fcis-2971	13	27	learning	learning	NOUN
fcis-2971	13	28	has	have	VERB
fcis-2971	13	29	a	a	DET
fcis-2971	13	30	powerful	powerful	ADJ
fcis-2971	13	31	ability	ability	NOUN
fcis-2971	13	32	to	to	PART
fcis-2971	13	33	solve	solve	VERB
fcis-2971	13	34	classification	classification	NOUN
fcis-2971	13	35	problems	problem	NOUN
fcis-2971	13	36	,	,	PUNCT
fcis-2971	13	37	so	so	ADV
fcis-2971	13	38	many	many	ADJ
fcis-2971	13	39	research	research	NOUN
fcis-2971	13	40	attempts	attempt	NOUN
fcis-2971	13	41	to	to	PART
fcis-2971	13	42	use	use	VERB
fcis-2971	13	43	machine	machine	NOUN
fcis-2971	13	44	learning	learn	VERB
fcis-2971	13	45	algorithms	algorithm	NOUN
fcis-2971	13	46	in	in	ADP
fcis-2971	13	47	intrusion	intrusion	NOUN
fcis-2971	13	48	detection	detection	NOUN
fcis-2971	13	49	models	model	NOUN
fcis-2971	13	50	[	[	X
fcis-2971	13	51	2	2	NUM
fcis-2971	13	52	]	]	PUNCT
fcis-2971	13	53	.	.	PUNCT
fcis-2971	14	1	among	among	ADP
fcis-2971	14	2	various	various	ADJ
fcis-2971	14	3	intrusion	intrusion	NOUN
fcis-2971	14	4	detection	detection	NOUN
fcis-2971	14	5	methods	method	NOUN
fcis-2971	14	6	based	base	VERB
fcis-2971	14	7	on	on	ADP
fcis-2971	14	8	machine	machine	NOUN
fcis-2971	14	9	learning	learning	NOUN
fcis-2971	14	10	models	model	NOUN
fcis-2971	14	11	,	,	PUNCT
fcis-2971	14	12	the	the	DET
fcis-2971	14	13	method	method	NOUN
fcis-2971	14	14	based	base	VERB
fcis-2971	14	15	on	on	ADP
fcis-2971	14	16	random	random	ADJ
fcis-2971	14	17	forest	forest	NOUN
fcis-2971	14	18	model	model	NOUN
fcis-2971	14	19	has	have	VERB
fcis-2971	14	20	good	good	ADJ
fcis-2971	14	21	results	result	NOUN
fcis-2971	14	22	in	in	ADP
fcis-2971	14	23	terms	term	NOUN
fcis-2971	14	24	of	of	ADP
fcis-2971	14	25	training	training	NOUN
fcis-2971	14	26	time	time	NOUN
fcis-2971	14	27	,	,	PUNCT
fcis-2971	14	28	false	false	ADJ
fcis-2971	14	29	alarm	alarm	NOUN
fcis-2971	14	30	rate	rate	NOUN
fcis-2971	14	31	,	,	PUNCT
fcis-2971	14	32	and	and	CCONJ
fcis-2971	14	33	detection	detection	NOUN
fcis-2971	14	34	ability	ability	NOUN
fcis-2971	14	35	of	of	ADP
fcis-2971	14	36	unknown	unknown	ADJ
fcis-2971	14	37	attacks	attack	NOUN
fcis-2971	14	38	.	.	PUNCT
fcis-2971	15	1	reference	reference	NOUN
fcis-2971	15	2	[	[	X
fcis-2971	15	3	3	3	NUM
fcis-2971	15	4	]	]	PUNCT
fcis-2971	15	5	uses	use	VERB
fcis-2971	15	6	one	one	NUM
fcis-2971	15	7	-	-	PUNCT
fcis-2971	15	8	r	r	NOUN
fcis-2971	15	9	fast	fast	ADJ
fcis-2971	15	10	attribute	attribute	NOUN
fcis-2971	15	11	to	to	PART
fcis-2971	15	12	solve	solve	VERB
fcis-2971	15	13	the	the	DET
fcis-2971	15	14	problem	problem	NOUN
fcis-2971	15	15	of	of	ADP
fcis-2971	15	16	inefficiency	inefficiency	NOUN
fcis-2971	15	17	caused	cause	VERB
fcis-2971	15	18	by	by	ADP
fcis-2971	15	19	excessive	excessive	ADJ
fcis-2971	15	20	randomness	randomness	NOUN
fcis-2971	15	21	in	in	ADP
fcis-2971	15	22	the	the	DET
fcis-2971	15	23	selection	selection	NOUN
fcis-2971	15	24	of	of	ADP
fcis-2971	15	25	attributes	attribute	NOUN
fcis-2971	15	26	of	of	ADP
fcis-2971	15	27	the	the	DET
fcis-2971	15	28	random	random	ADJ
fcis-2971	15	29	forest	forest	NOUN
fcis-2971	15	30	model	model	NOUN
fcis-2971	15	31	when	when	SCONJ
fcis-2971	15	32	encountering	encounter	VERB
fcis-2971	15	33	highdimensional	highdimensional	ADJ
fcis-2971	15	34	data	datum	NOUN
fcis-2971	15	35	,	,	PUNCT
fcis-2971	15	36	and	and	CCONJ
fcis-2971	15	37	achieved	achieve	VERB
fcis-2971	15	38	good	good	ADJ
fcis-2971	15	39	spatiotemporal	spatiotemporal	ADJ
fcis-2971	15	40	performance	performance	NOUN
fcis-2971	15	41	and	and	CCONJ
fcis-2971	15	42	low	low	ADJ
fcis-2971	15	43	false	false	ADJ
fcis-2971	15	44	positive	positive	ADJ
fcis-2971	15	45	and	and	CCONJ
fcis-2971	15	46	false	false	ADJ
fcis-2971	15	47	negative	negative	ADJ
fcis-2971	15	48	rates	rate	NOUN
fcis-2971	15	49	in	in	ADP
fcis-2971	15	50	the	the	DET
fcis-2971	15	51	experiment	experiment	NOUN
fcis-2971	15	52	.	.	PUNCT
fcis-2971	16	1	literature	literature	NOUN
fcis-2971	17	1	[	[	X
fcis-2971	17	2	4	4	X
fcis-2971	17	3	]	]	PUNCT
fcis-2971	17	4	proposes	propose	VERB
fcis-2971	17	5	a	a	DET
fcis-2971	17	6	method	method	NOUN
fcis-2971	17	7	of	of	ADP
fcis-2971	17	8	using	use	VERB
fcis-2971	17	9	knn	knn	PROPN
fcis-2971	17	10	to	to	ADP
fcis-2971	17	11	delete	delete	ADJ
fcis-2971	17	12	outlier	outlier	NOUN
fcis-2971	17	13	data	datum	NOUN
fcis-2971	17	14	and	and	CCONJ
fcis-2971	17	15	then	then	ADV
fcis-2971	17	16	combining	combine	VERB
fcis-2971	17	17	with	with	ADP
fcis-2971	17	18	multilevel	multilevel	ADJ
fcis-2971	17	19	random	random	ADJ
fcis-2971	17	20	forest	forest	NOUN
fcis-2971	17	21	to	to	PART
fcis-2971	17	22	detect	detect	VERB
fcis-2971	17	23	network	network	NOUN
fcis-2971	17	24	attacks	attack	NOUN
fcis-2971	17	25	,	,	PUNCT
fcis-2971	17	26	which	which	PRON
fcis-2971	17	27	can	can	AUX
fcis-2971	17	28	effectively	effectively	ADV
fcis-2971	17	29	detect	detect	VERB
fcis-2971	17	30	probe	probe	NOUN
fcis-2971	17	31	,	,	PUNCT
fcis-2971	17	32	u2r	u2r	NOUN
fcis-2971	17	33	,	,	PUNCT
fcis-2971	17	34	r2l	r2l	NOUN
fcis-2971	17	35	and	and	CCONJ
fcis-2971	17	36	other	other	ADJ
fcis-2971	17	37	attacks	attack	NOUN
fcis-2971	17	38	on	on	ADP
fcis-2971	17	39	the	the	DET
fcis-2971	17	40	kdd	kdd	PROPN
fcis-2971	17	41	cup99	cup99	PROPN
fcis-2971	17	42	dataset	dataset	NOUN
fcis-2971	17	43	.	.	PUNCT
fcis-2971	18	1	some	some	DET
fcis-2971	18	2	other	other	ADJ
fcis-2971	18	3	methods	method	NOUN
fcis-2971	18	4	combined	combine	VERB
fcis-2971	18	5	with	with	ADP
fcis-2971	18	6	artificial	artificial	ADJ
fcis-2971	18	7	intelligence	intelligence	NOUN
fcis-2971	18	8	are	be	AUX
fcis-2971	18	9	also	also	ADV
fcis-2971	18	10	used	use	VERB
fcis-2971	18	11	in	in	ADP
fcis-2971	18	12	network	network	NOUN
fcis-2971	18	13	intrusion	intrusion	NOUN
fcis-2971	18	14	detection	detection	NOUN
fcis-2971	18	15	.	.	PUNCT
fcis-2971	19	1	reference	reference	NOUN
fcis-2971	19	2	[	[	X
fcis-2971	19	3	5	5	NUM
fcis-2971	19	4	]	]	PUNCT
fcis-2971	19	5	proposes	propose	VERB
fcis-2971	19	6	a	a	DET
fcis-2971	19	7	pca	pca	PROPN
fcis-2971	19	8	-	-	PUNCT
fcis-2971	19	9	bp	bp	PROPN
fcis-2971	19	10	neural	neural	ADJ
fcis-2971	19	11	network	network	PROPN
fcis-2971	19	12	intrusion	intrusion	PROPN
fcis-2971	19	13	detection	detection	NOUN
fcis-2971	19	14	method	method	NOUN
fcis-2971	19	15	.	.	PUNCT
fcis-2971	20	1	by	by	ADP
fcis-2971	20	2	reducing	reduce	VERB
fcis-2971	20	3	the	the	DET
fcis-2971	20	4	dimension	dimension	NOUN
fcis-2971	20	5	of	of	ADP
fcis-2971	20	6	data	datum	NOUN
fcis-2971	20	7	features	feature	NOUN
fcis-2971	20	8	and	and	CCONJ
fcis-2971	20	9	correcting	correct	VERB
fcis-2971	20	10	weights	weight	NOUN
fcis-2971	20	11	,	,	PUNCT
fcis-2971	20	12	the	the	DET
fcis-2971	20	13	shortcomings	shortcoming	NOUN
fcis-2971	20	14	of	of	ADP
fcis-2971	20	15	slow	slow	ADJ
fcis-2971	20	16	convergence	convergence	NOUN
fcis-2971	20	17	of	of	ADP
fcis-2971	20	18	bp	bp	PROPN
fcis-2971	20	19	neural	neural	ADJ
fcis-2971	20	20	network	network	NOUN
fcis-2971	20	21	are	be	AUX
fcis-2971	20	22	improved	improve	VERB
fcis-2971	20	23	and	and	CCONJ
fcis-2971	20	24	the	the	DET
fcis-2971	20	25	detection	detection	NOUN
fcis-2971	20	26	performance	performance	NOUN
fcis-2971	20	27	is	be	AUX
fcis-2971	20	28	improved	improve	VERB
fcis-2971	20	29	.	.	PUNCT
fcis-2971	21	1	literature	literature	NOUN
fcis-2971	22	1	[	[	X
fcis-2971	22	2	6	6	NUM
fcis-2971	22	3	]	]	PUNCT
fcis-2971	22	4	proposes	propose	VERB
fcis-2971	22	5	an	an	DET
fcis-2971	22	6	intrusion	intrusion	NOUN
fcis-2971	22	7	detection	detection	NOUN
fcis-2971	22	8	algorithm	algorithm	NOUN
fcis-2971	22	9	based	base	VERB
fcis-2971	22	10	on	on	ADP
fcis-2971	22	11	pca	pca	PROPN
fcis-2971	22	12	and	and	CCONJ
fcis-2971	22	13	svm	svm	PROPN
fcis-2971	22	14	(	(	PUNCT
fcis-2971	22	15	support	support	VERB
fcis-2971	22	16	vector	vector	NOUN
fcis-2971	22	17	machines	machine	NOUN
fcis-2971	22	18	)	)	PUNCT
fcis-2971	22	19	,	,	PUNCT
fcis-2971	22	20	but	but	CCONJ
fcis-2971	22	21	the	the	DET
fcis-2971	22	22	accuracy	accuracy	NOUN
fcis-2971	22	23	is	be	AUX
fcis-2971	22	24	low	low	ADJ
fcis-2971	22	25	for	for	ADP
fcis-2971	22	26	individual	individual	ADJ
fcis-2971	22	27	attack	attack	NOUN
fcis-2971	22	28	types	type	NOUN
fcis-2971	22	29	.	.	PUNCT
fcis-2971	23	1	this	this	DET
fcis-2971	23	2	paper	paper	NOUN
fcis-2971	23	3	proposes	propose	VERB
fcis-2971	23	4	an	an	DET
fcis-2971	23	5	intrusion	intrusion	NOUN
fcis-2971	23	6	detection	detection	NOUN
fcis-2971	23	7	algorithm	algorithm	NOUN
fcis-2971	23	8	based	base	VERB
fcis-2971	23	9	on	on	ADP
fcis-2971	23	10	pca	pca	PROPN
fcis-2971	23	11	and	and	CCONJ
fcis-2971	23	12	random	random	ADJ
fcis-2971	23	13	forest	forest	NOUN
fcis-2971	23	14	classification	classification	NOUN
fcis-2971	23	15	.	.	PUNCT
fcis-2971	24	1	the	the	DET
fcis-2971	24	2	data	data	NOUN
fcis-2971	24	3	is	be	AUX
fcis-2971	24	4	first	first	ADV
fcis-2971	24	5	reduced	reduce	VERB
fcis-2971	24	6	by	by	ADP
fcis-2971	24	7	pca	pca	PROPN
fcis-2971	24	8	and	and	CCONJ
fcis-2971	24	9	then	then	ADV
fcis-2971	24	10	classified	classify	VERB
fcis-2971	24	11	by	by	ADP
fcis-2971	24	12	random	random	ADJ
fcis-2971	24	13	forest	forest	NOUN
fcis-2971	24	14	,	,	PUNCT
fcis-2971	24	15	and	and	CCONJ
fcis-2971	24	16	the	the	DET
fcis-2971	24	17	effectiveness	effectiveness	NOUN
fcis-2971	24	18	of	of	ADP
fcis-2971	24	19	the	the	DET
fcis-2971	24	20	algorithm	algorithm	NOUN
fcis-2971	24	21	is	be	AUX
fcis-2971	24	22	verified	verify	VERB
fcis-2971	24	23	by	by	ADP
fcis-2971	24	24	experiments	experiment	NOUN
fcis-2971	24	25	.	.	PUNCT
fcis-2971	25	1	2	2	X
fcis-2971	25	2	.	.	X
fcis-2971	25	3	decision	decision	NOUN
fcis-2971	25	4	tree	tree	NOUN
fcis-2971	25	5	and	and	CCONJ
fcis-2971	25	6	random	random	ADJ
fcis-2971	25	7	forest	forest	NOUN
fcis-2971	25	8	2.1	2.1	NUM
fcis-2971	25	9	.	.	PUNCT
fcis-2971	26	1	decision	decision	NOUN
fcis-2971	26	2	tree	tree	NOUN
fcis-2971	26	3	a	a	DET
fcis-2971	26	4	decision	decision	NOUN
fcis-2971	26	5	tree	tree	NOUN
fcis-2971	26	6	is	be	AUX
fcis-2971	26	7	a	a	DET
fcis-2971	26	8	classifier	classifier	NOUN
fcis-2971	26	9	model	model	NOUN
fcis-2971	26	10	and	and	CCONJ
fcis-2971	26	11	the	the	DET
fcis-2971	26	12	basis	basis	NOUN
fcis-2971	26	13	for	for	ADP
fcis-2971	26	14	forming	form	VERB
fcis-2971	26	15	random	random	ADJ
fcis-2971	26	16	forests	forest	NOUN
fcis-2971	26	17	.	.	PUNCT
fcis-2971	27	1	common	common	ADJ
fcis-2971	27	2	decision	decision	NOUN
fcis-2971	27	3	tree	tree	NOUN
fcis-2971	27	4	generation	generation	NOUN
fcis-2971	27	5	algorithms	algorithm	NOUN
fcis-2971	27	6	mainly	mainly	ADV
fcis-2971	27	7	include	include	VERB
fcis-2971	27	8	id3	id3	NOUN
fcis-2971	27	9	[	[	X
fcis-2971	27	10	7	7	NUM
fcis-2971	27	11	]	]	PUNCT
fcis-2971	27	12	,	,	PUNCT
fcis-2971	27	13	c4.5	c4.5	PROPN
fcis-2971	28	1	[	[	X
fcis-2971	28	2	8	8	NUM
fcis-2971	28	3	]	]	PUNCT
fcis-2971	28	4	,	,	PUNCT
fcis-2971	28	5	cart	cart	NOUN
fcis-2971	29	1	[	[	X
fcis-2971	29	2	9	9	NUM
fcis-2971	29	3	]	]	PUNCT
fcis-2971	29	4	,	,	PUNCT
fcis-2971	29	5	etc	etc	X
fcis-2971	29	6	.	.	X
fcis-2971	29	7	among	among	ADP
fcis-2971	29	8	them	they	PRON
fcis-2971	29	9	,	,	PUNCT
fcis-2971	29	10	id3	id3	NOUN
fcis-2971	29	11	algorithm	algorithm	NOUN
fcis-2971	29	12	is	be	AUX
fcis-2971	29	13	the	the	DET
fcis-2971	29	14	first	first	ADJ
fcis-2971	29	15	influential	influential	ADJ
fcis-2971	29	16	decision	decision	NOUN
fcis-2971	29	17	tree	tree	NOUN
fcis-2971	29	18	generation	generation	NOUN
fcis-2971	29	19	algorithm	algorithm	NOUN
fcis-2971	29	20	,	,	PUNCT
fcis-2971	29	21	and	and	CCONJ
fcis-2971	29	22	the	the	DET
fcis-2971	29	23	latter	latter	ADJ
fcis-2971	29	24	two	two	NUM
fcis-2971	29	25	algorithms	algorithm	NOUN
fcis-2971	29	26	are	be	AUX
fcis-2971	29	27	in	in	ADP
fcis-2971	29	28	its	its	PRON
fcis-2971	29	29	on	on	ADP
fcis-2971	29	30	the	the	DET
fcis-2971	29	31	basis	basis	NOUN
fcis-2971	29	32	of	of	ADP
fcis-2971	29	33	optimization	optimization	NOUN
fcis-2971	29	34	or	or	CCONJ
fcis-2971	29	35	borrowing	borrow	VERB
fcis-2971	29	36	its	its	PRON
fcis-2971	29	37	idea	idea	NOUN
fcis-2971	29	38	.	.	PUNCT
fcis-2971	30	1	the	the	DET
fcis-2971	30	2	id3	id3	NOUN
fcis-2971	30	3	algorithm	algorithm	NOUN
fcis-2971	30	4	introduces	introduce	VERB
fcis-2971	30	5	the	the	DET
fcis-2971	30	6	concept	concept	NOUN
fcis-2971	30	7	of	of	ADP
fcis-2971	30	8	information	information	NOUN
fcis-2971	30	9	entropy	entropy	NOUN
fcis-2971	30	10	in	in	ADP
fcis-2971	30	11	information	information	NOUN
fcis-2971	30	12	theory	theory	NOUN
fcis-2971	30	13	and	and	CCONJ
fcis-2971	30	14	defines	define	VERB
fcis-2971	30	15	the	the	DET
fcis-2971	30	16	information	information	NOUN
fcis-2971	30	17	gain	gain	NOUN
fcis-2971	30	18	of	of	ADP
fcis-2971	30	19	feature	feature	NOUN
fcis-2971	30	20	attributes	attribute	NOUN
fcis-2971	30	21	.	.	PUNCT
fcis-2971	31	1	assuming	assume	VERB
fcis-2971	31	2	that	that	SCONJ
fcis-2971	31	3	a	a	DET
fcis-2971	31	4	total	total	ADJ
fcis-2971	31	5	sample	sample	NOUN
fcis-2971	31	6	set	set	NOUN
fcis-2971	31	7	d	d	NOUN
fcis-2971	31	8	can	can	AUX
fcis-2971	31	9	be	be	AUX
fcis-2971	31	10	divided	divide	VERB
fcis-2971	31	11	into	into	ADP
fcis-2971	31	12	m	m	PROPN
fcis-2971	31	13	different	different	ADJ
fcis-2971	31	14	categories	category	NOUN
fcis-2971	31	15	according	accord	VERB
fcis-2971	31	16	to	to	ADP
fcis-2971	31	17	the	the	DET
fcis-2971	31	18	target	target	NOUN
fcis-2971	31	19	attribute	attribute	NOUN
fcis-2971	31	20	,	,	PUNCT
fcis-2971	31	21	then	then	ADV
fcis-2971	31	22	the	the	DET
fcis-2971	31	23	information	information	NOUN
fcis-2971	31	24	entropy	entropy	NOUN
fcis-2971	31	25	of	of	ADP
fcis-2971	31	26	this	this	DET
fcis-2971	31	27	sample	sample	NOUN
fcis-2971	31	28	is	be	AUX
fcis-2971	31	29	:	:	PUNCT
fcis-2971	31	30			X
fcis-2971	31	31	=	=	PUNCT
fcis-2971	31	32	−=	−=	VERB
fcis-2971	31	33	m	m	VERB
fcis-2971	31	34	i	i	PRON
fcis-2971	32	1	i	i	PRON
fcis-2971	32	2	p	p	VERB
fcis-2971	33	1	i	i	PRON
fcis-2971	33	2	pdh	pdh	VERB
fcis-2971	33	3	1	1	NUM
fcis-2971	33	4	2	2	NUM
fcis-2971	33	5	log	log	NOUN
fcis-2971	33	6	)	)	PUNCT
fcis-2971	33	7	(	(	PUNCT
fcis-2971	33	8	(	(	PUNCT
fcis-2971	33	9	1	1	X
fcis-2971	33	10	)	)	PUNCT
fcis-2971	33	11	where	where	SCONJ
fcis-2971	33	12	ip	ip	NOUN
fcis-2971	33	13	is	be	AUX
fcis-2971	33	14	the	the	DET
fcis-2971	33	15	proportion	proportion	NOUN
fcis-2971	33	16	of	of	ADP
fcis-2971	33	17	the	the	DET
fcis-2971	33	18	ith	ith	PROPN
fcis-2971	33	19	type	type	NOUN
fcis-2971	33	20	of	of	ADP
fcis-2971	33	21	subsample	subsample	NOUN
fcis-2971	33	22	set	set	VERB
fcis-2971	33	23	i	i	PROPN
fcis-2971	33	24	d	d	PROPN
fcis-2971	33	25	in	in	ADP
fcis-2971	33	26	the	the	DET
fcis-2971	33	27	total	total	ADJ
fcis-2971	33	28	sample	sample	NOUN
fcis-2971	33	29	set	set	VERB
fcis-2971	33	30	d	d	NOUN
fcis-2971	33	31	,	,	PUNCT
fcis-2971	33	32	or	or	CCONJ
fcis-2971	33	33	the	the	DET
fcis-2971	33	34	probability	probability	NOUN
fcis-2971	33	35	that	that	SCONJ
fcis-2971	33	36	i	i	PROPN
fcis-2971	33	37	d	d	PROPN
fcis-2971	33	38	appears	appear	VERB
fcis-2971	33	39	.	.	PUNCT
fcis-2971	34	1	the	the	DET
fcis-2971	34	2	information	information	NOUN
fcis-2971	34	3	gain	gain	NOUN
fcis-2971	34	4	brought	bring	VERB
fcis-2971	34	5	by	by	ADP
fcis-2971	34	6	an	an	DET
fcis-2971	34	7	attribute	attribute	NOUN
fcis-2971	34	8	to	to	ADP
fcis-2971	34	9	the	the	DET
fcis-2971	34	10	whole	whole	NOUN
fcis-2971	34	11	is	be	AUX
fcis-2971	34	12	equal	equal	ADJ
fcis-2971	34	13	to	to	ADP
fcis-2971	34	14	the	the	DET
fcis-2971	34	15	difference	difference	NOUN
fcis-2971	34	16	between	between	ADP
fcis-2971	34	17	the	the	DET
fcis-2971	34	18	total	total	ADJ
fcis-2971	34	19	information	information	NOUN
fcis-2971	34	20	entropy	entropy	NOUN
fcis-2971	34	21	and	and	CCONJ
fcis-2971	34	22	the	the	DET
fcis-2971	34	23	residual	residual	ADJ
fcis-2971	34	24	information	information	NOUN
fcis-2971	34	25	entropy	entropy	NOUN
fcis-2971	34	26	after	after	SCONJ
fcis-2971	34	27	the	the	DET
fcis-2971	34	28	attribute	attribute	NOUN
fcis-2971	34	29	is	be	AUX
fcis-2971	34	30	selected	select	VERB
fcis-2971	34	31	.	.	PUNCT
fcis-2971	35	1	assuming	assume	VERB
fcis-2971	35	2	that	that	SCONJ
fcis-2971	35	3	the	the	DET
fcis-2971	35	4	information	information	NOUN
fcis-2971	35	5	gain	gain	NOUN
fcis-2971	35	6	of	of	ADP
fcis-2971	35	7	attribute	attribute	NOUN
fcis-2971	35	8	a	a	PRON
fcis-2971	35	9	is	be	AUX
fcis-2971	35	10	to	to	PART
fcis-2971	35	11	be	be	AUX
fcis-2971	35	12	obtained	obtain	VERB
fcis-2971	35	13	,	,	PUNCT
fcis-2971	35	14	and	and	CCONJ
fcis-2971	35	15	attribute	attribute	VERB
fcis-2971	35	16	a	a	PRON
fcis-2971	35	17	has	have	VERB
fcis-2971	35	18	k	k	PROPN
fcis-2971	35	19	different	different	ADJ
fcis-2971	35	20	values	value	NOUN
fcis-2971	35	21	,	,	PUNCT
fcis-2971	35	22	the	the	DET
fcis-2971	35	23	total	total	ADJ
fcis-2971	35	24	sample	sample	NOUN
fcis-2971	35	25	should	should	AUX
fcis-2971	35	26	be	be	AUX
fcis-2971	35	27	divided	divide	VERB
fcis-2971	35	28	into	into	ADP
fcis-2971	35	29	several	several	ADJ
fcis-2971	35	30	sub	sub	NOUN
fcis-2971	35	31	samples	sample	NOUN
fcis-2971	35	32	)	)	PUNCT
fcis-2971	35	33	,	,	PUNCT
fcis-2971	35	34	,	,	PUNCT
fcis-2971	35	35	1	1	NUM
fcis-2971	35	36	,	,	PUNCT
fcis-2971	35	37	1	1	NUM
fcis-2971	35	38	(	(	PUNCT
fcis-2971	35	39	k	k	PROPN
fcis-2971	35	40	ddd	ddd	PROPN
fcis-2971	35	41			PUNCT
fcis-2971	35	42	according	accord	VERB
fcis-2971	35	43	to	to	ADP
fcis-2971	35	44	the	the	DET
fcis-2971	35	45	value	value	NOUN
fcis-2971	35	46	of	of	ADP
fcis-2971	35	47	attribute	attribute	NOUN
fcis-2971	35	48	a	a	PRON
fcis-2971	35	49	,	,	PUNCT
fcis-2971	35	50	and	and	CCONJ
fcis-2971	35	51	the	the	DET
fcis-2971	35	52	entropy	entropy	NOUN
fcis-2971	35	53	of	of	ADP
fcis-2971	35	54	each	each	DET
fcis-2971	35	55	sub	sub	NOUN
fcis-2971	35	56	sample	sample	NOUN
fcis-2971	35	57	should	should	AUX
fcis-2971	35	58	be	be	AUX
fcis-2971	35	59	calculated	calculate	VERB
fcis-2971	35	60	respectively	respectively	ADV
fcis-2971	35	61	,	,	PUNCT
fcis-2971	35	62	and	and	CCONJ
fcis-2971	35	63	then	then	ADV
fcis-2971	35	64	the	the	DET
fcis-2971	35	65	residual	residual	ADJ
fcis-2971	35	66	information	information	NOUN
fcis-2971	35	67	entropy	entropy	NOUN
fcis-2971	35	68	can	can	AUX
fcis-2971	35	69	be	be	AUX
fcis-2971	35	70	obtained	obtain	VERB
fcis-2971	35	71	by	by	ADP
fcis-2971	35	72	weighted	weighted	ADJ
fcis-2971	35	73	summation	summation	NOUN
fcis-2971	35	74	.	.	PUNCT
fcis-2971	36	1			X
fcis-2971	37	1	=	=	PUNCT
fcis-2971	38	1	=	=	PUNCT
fcis-2971	38	2	k	k	NOUN
fcis-2971	39	1	i	i	PRON
fcis-2971	39	2	i	i	PRON
fcis-2971	39	3	dh	dh	INTJ
fcis-2971	39	4	dlen	dlen	VERB
fcis-2971	39	5	i	i	PRON
fcis-2971	39	6	dlen	dlen	VERB
fcis-2971	39	7	ar	ar	PROPN
fcis-2971	39	8	1	1	NUM
fcis-2971	39	9	)	)	PUNCT
fcis-2971	39	10	(	(	PUNCT
fcis-2971	39	11	)	)	PUNCT
fcis-2971	39	12	(	(	PUNCT
fcis-2971	39	13	)	)	PUNCT
fcis-2971	39	14	(	(	PUNCT
fcis-2971	39	15	)	)	PUNCT
fcis-2971	39	16	(	(	PUNCT
fcis-2971	39	17	(	(	PUNCT
fcis-2971	39	18	2	2	X
fcis-2971	39	19	)	)	PUNCT
fcis-2971	39	20	77	77	NUM
fcis-2971	39	21	where	where	SCONJ
fcis-2971	39	22	len	len	PROPN
fcis-2971	39	23	(	(	PUNCT
fcis-2971	39	24	)	)	PUNCT
fcis-2971	39	25	represents	represent	VERB
fcis-2971	39	26	the	the	DET
fcis-2971	39	27	size	size	NOUN
fcis-2971	39	28	of	of	ADP
fcis-2971	39	29	the	the	DET
fcis-2971	39	30	sample	sample	NOUN
fcis-2971	39	31	set	set	VERB
fcis-2971	39	32	in	in	ADP
fcis-2971	39	33	parentheses	parenthesis	NOUN
fcis-2971	39	34	.	.	PUNCT
fcis-2971	40	1	according	accord	VERB
fcis-2971	40	2	to	to	ADP
fcis-2971	40	3	the	the	DET
fcis-2971	40	4	total	total	ADJ
fcis-2971	40	5	information	information	NOUN
fcis-2971	40	6	entropy	entropy	NOUN
fcis-2971	40	7	and	and	CCONJ
fcis-2971	40	8	residual	residual	ADJ
fcis-2971	40	9	information	information	NOUN
fcis-2971	40	10	entropy	entropy	PROPN
fcis-2971	40	11	,	,	PUNCT
fcis-2971	40	12	the	the	DET
fcis-2971	40	13	information	information	NOUN
fcis-2971	40	14	gain	gain	NOUN
fcis-2971	40	15	of	of	ADP
fcis-2971	40	16	attribute	attribute	NOUN
fcis-2971	40	17	a	a	PRON
fcis-2971	40	18	is	be	AUX
fcis-2971	40	19	:	:	PUNCT
fcis-2971	40	20	)	)	PUNCT
fcis-2971	40	21	(	(	PUNCT
fcis-2971	40	22	)	)	PUNCT
fcis-2971	40	23	(	(	PUNCT
fcis-2971	40	24	)	)	PUNCT
fcis-2971	40	25	(	(	PUNCT
fcis-2971	40	26	ardhag	ardhag	PROPN
fcis-2971	40	27	−=	−=	NOUN
fcis-2971	40	28	(	(	PUNCT
fcis-2971	40	29	3	3	NUM
fcis-2971	40	30	)	)	PUNCT
fcis-2971	40	31	according	accord	VERB
fcis-2971	40	32	to	to	ADP
fcis-2971	40	33	the	the	DET
fcis-2971	40	34	information	information	NOUN
fcis-2971	40	35	entropy	entropy	NOUN
fcis-2971	40	36	theory	theory	NOUN
fcis-2971	40	37	,	,	PUNCT
fcis-2971	40	38	when	when	SCONJ
fcis-2971	40	39	the	the	DET
fcis-2971	40	40	probability	probability	NOUN
fcis-2971	40	41	of	of	ADP
fcis-2971	40	42	occurrence	occurrence	NOUN
fcis-2971	40	43	of	of	ADP
fcis-2971	40	44	each	each	DET
fcis-2971	40	45	type	type	NOUN
fcis-2971	40	46	of	of	ADP
fcis-2971	40	47	sub	sub	NOUN
fcis-2971	40	48	-	-	NOUN
fcis-2971	40	49	sample	sample	NOUN
fcis-2971	40	50	in	in	ADP
fcis-2971	40	51	the	the	DET
fcis-2971	40	52	total	total	ADJ
fcis-2971	40	53	sample	sample	NOUN
fcis-2971	40	54	is	be	AUX
fcis-2971	40	55	the	the	DET
fcis-2971	40	56	same	same	ADJ
fcis-2971	40	57	,	,	PUNCT
fcis-2971	40	58	that	that	ADV
fcis-2971	40	59	is	is	ADV
fcis-2971	40	60	,	,	PUNCT
fcis-2971	40	61	for	for	ADP
fcis-2971	40	62	any	any	DET
fcis-2971	40	63	i	i	PRON
fcis-2971	40	64	,	,	PUNCT
fcis-2971	40	65	there	there	PRON
fcis-2971	40	66	is	be	VERB
fcis-2971	40	67	mpi	mpi	PROPN
fcis-2971	40	68	1/=	1/=	NUM
fcis-2971	40	69	,	,	PUNCT
fcis-2971	40	70	the	the	DET
fcis-2971	40	71	maximum	maximum	ADJ
fcis-2971	40	72	information	information	NOUN
fcis-2971	40	73	entropy	entropy	NOUN
fcis-2971	40	74	is	be	AUX
fcis-2971	40	75	m	m	PROPN
fcis-2971	40	76	2log	2log	NUM
fcis-2971	40	77	.	.	PUNCT
fcis-2971	41	1	however	however	ADV
fcis-2971	41	2	,	,	PUNCT
fcis-2971	41	3	if	if	SCONJ
fcis-2971	41	4	the	the	DET
fcis-2971	41	5	probability	probability	NOUN
fcis-2971	41	6	of	of	ADP
fcis-2971	41	7	occurrence	occurrence	NOUN
fcis-2971	41	8	of	of	ADP
fcis-2971	41	9	one	one	NUM
fcis-2971	41	10	type	type	NOUN
fcis-2971	41	11	of	of	ADP
fcis-2971	41	12	subsample	subsample	NOUN
fcis-2971	41	13	is	be	AUX
fcis-2971	41	14	much	much	ADV
fcis-2971	41	15	higher	high	ADJ
fcis-2971	41	16	than	than	ADP
fcis-2971	41	17	that	that	PRON
fcis-2971	41	18	of	of	ADP
fcis-2971	41	19	other	other	ADJ
fcis-2971	41	20	types	type	NOUN
fcis-2971	41	21	of	of	ADP
fcis-2971	41	22	subsample	subsample	NOUN
fcis-2971	41	23	,	,	PUNCT
fcis-2971	41	24	the	the	PRON
fcis-2971	41	25	smaller	small	ADJ
fcis-2971	41	26	the	the	DET
fcis-2971	41	27	information	information	NOUN
fcis-2971	41	28	entropy	entropy	NOUN
fcis-2971	41	29	is	be	AUX
fcis-2971	41	30	,	,	PUNCT
fcis-2971	41	31	and	and	CCONJ
fcis-2971	41	32	when	when	SCONJ
fcis-2971	41	33	there	there	PRON
fcis-2971	41	34	is	be	VERB
fcis-2971	41	35	only	only	ADV
fcis-2971	41	36	one	one	NUM
fcis-2971	41	37	type	type	NOUN
fcis-2971	41	38	of	of	ADP
fcis-2971	41	39	subsample	subsample	NOUN
fcis-2971	41	40	,	,	PUNCT
fcis-2971	41	41	the	the	DET
fcis-2971	41	42	minimum	minimum	ADJ
fcis-2971	41	43	information	information	NOUN
fcis-2971	41	44	entropy	entropy	NOUN
fcis-2971	41	45	is	be	AUX
fcis-2971	41	46	0	0	NUM
fcis-2971	41	47	.	.	PUNCT
fcis-2971	42	1	therefore	therefore	ADV
fcis-2971	42	2	,	,	PUNCT
fcis-2971	42	3	the	the	DET
fcis-2971	42	4	key	key	ADJ
fcis-2971	42	5	idea	idea	NOUN
fcis-2971	42	6	of	of	ADP
fcis-2971	42	7	id3	id3	NOUN
fcis-2971	42	8	algorithm	algorithm	NOUN
fcis-2971	42	9	is	be	AUX
fcis-2971	42	10	to	to	PART
fcis-2971	42	11	minimize	minimize	VERB
fcis-2971	42	12	the	the	DET
fcis-2971	42	13	residual	residual	ADJ
fcis-2971	42	14	information	information	NOUN
fcis-2971	42	15	entropy	entropy	NOUN
fcis-2971	42	16	after	after	ADP
fcis-2971	42	17	classification	classification	NOUN
fcis-2971	42	18	according	accord	VERB
fcis-2971	42	19	to	to	ADP
fcis-2971	42	20	certain	certain	ADJ
fcis-2971	42	21	attributes	attribute	NOUN
fcis-2971	42	22	,	,	PUNCT
fcis-2971	42	23	so	so	SCONJ
fcis-2971	42	24	that	that	SCONJ
fcis-2971	42	25	the	the	DET
fcis-2971	42	26	purity	purity	NOUN
fcis-2971	42	27	of	of	ADP
fcis-2971	42	28	each	each	DET
fcis-2971	42	29	subsample	subsample	NOUN
fcis-2971	42	30	after	after	ADP
fcis-2971	42	31	division	division	NOUN
fcis-2971	42	32	is	be	AUX
fcis-2971	42	33	higher	high	ADJ
fcis-2971	42	34	.	.	PUNCT
fcis-2971	43	1	every	every	DET
fcis-2971	43	2	time	time	NOUN
fcis-2971	43	3	id3	id3	NOUN
fcis-2971	43	4	algorithm	algorithm	NOUN
fcis-2971	43	5	selects	select	VERB
fcis-2971	43	6	the	the	DET
fcis-2971	43	7	feature	feature	NOUN
fcis-2971	43	8	attributes	attribute	NOUN
fcis-2971	43	9	used	use	VERB
fcis-2971	43	10	for	for	ADP
fcis-2971	43	11	splitting	splitting	NOUN
fcis-2971	43	12	,	,	PUNCT
fcis-2971	43	13	it	it	PRON
fcis-2971	43	14	needs	need	VERB
fcis-2971	43	15	to	to	PART
fcis-2971	43	16	calculate	calculate	VERB
fcis-2971	43	17	the	the	DET
fcis-2971	43	18	information	information	NOUN
fcis-2971	43	19	gain	gain	NOUN
fcis-2971	43	20	of	of	ADP
fcis-2971	43	21	all	all	DET
fcis-2971	43	22	the	the	DET
fcis-2971	43	23	feature	feature	NOUN
fcis-2971	43	24	attributes	attribute	VERB
fcis-2971	43	25	,	,	PUNCT
fcis-2971	43	26	select	select	VERB
fcis-2971	43	27	the	the	DET
fcis-2971	43	28	feature	feature	NOUN
fcis-2971	43	29	attribute	attribute	NOUN
fcis-2971	43	30	with	with	ADP
fcis-2971	43	31	the	the	DET
fcis-2971	43	32	largest	large	ADJ
fcis-2971	43	33	information	information	NOUN
fcis-2971	43	34	gain	gain	NOUN
fcis-2971	43	35	,	,	PUNCT
fcis-2971	43	36	divide	divide	VERB
fcis-2971	43	37	the	the	DET
fcis-2971	43	38	sample	sample	NOUN
fcis-2971	43	39	into	into	ADP
fcis-2971	43	40	several	several	ADJ
fcis-2971	43	41	sub	sub	NOUN
fcis-2971	43	42	samples	sample	NOUN
fcis-2971	43	43	according	accord	VERB
fcis-2971	43	44	to	to	ADP
fcis-2971	43	45	this	this	DET
fcis-2971	43	46	feature	feature	NOUN
fcis-2971	43	47	attribute	attribute	NOUN
fcis-2971	43	48	,	,	PUNCT
fcis-2971	43	49	and	and	CCONJ
fcis-2971	43	50	then	then	ADV
fcis-2971	43	51	repeat	repeat	VERB
fcis-2971	43	52	the	the	DET
fcis-2971	43	53	process	process	NOUN
fcis-2971	43	54	until	until	SCONJ
fcis-2971	43	55	all	all	DET
fcis-2971	43	56	the	the	DET
fcis-2971	43	57	samples	sample	NOUN
fcis-2971	43	58	belong	belong	VERB
fcis-2971	43	59	to	to	ADP
fcis-2971	43	60	the	the	DET
fcis-2971	43	61	same	same	ADJ
fcis-2971	43	62	category	category	NOUN
fcis-2971	43	63	,	,	PUNCT
fcis-2971	43	64	or	or	CCONJ
fcis-2971	43	65	most	most	ADJ
fcis-2971	43	66	of	of	ADP
fcis-2971	43	67	the	the	DET
fcis-2971	43	68	samples	sample	NOUN
fcis-2971	43	69	belong	belong	VERB
fcis-2971	43	70	to	to	ADP
fcis-2971	43	71	the	the	DET
fcis-2971	43	72	same	same	ADJ
fcis-2971	43	73	category	category	NOUN
fcis-2971	43	74	.	.	PUNCT
fcis-2971	44	1	2.2	2.2	NUM
fcis-2971	44	2	.	.	PUNCT
fcis-2971	44	3	random	random	ADJ
fcis-2971	44	4	forest	forest	NOUN
fcis-2971	44	5	because	because	SCONJ
fcis-2971	44	6	decision	decision	NOUN
fcis-2971	44	7	trees	tree	NOUN
fcis-2971	44	8	are	be	AUX
fcis-2971	44	9	easy	easy	ADJ
fcis-2971	44	10	to	to	PART
fcis-2971	44	11	over	over	ADP
fcis-2971	44	12	fit	fit	ADJ
fcis-2971	44	13	when	when	SCONJ
fcis-2971	44	14	facing	face	VERB
fcis-2971	44	15	samples	sample	NOUN
fcis-2971	44	16	with	with	ADP
fcis-2971	44	17	higher	high	ADJ
fcis-2971	44	18	dimensions	dimension	NOUN
fcis-2971	44	19	,	,	PUNCT
fcis-2971	44	20	most	most	ADV
fcis-2971	44	21	practical	practical	ADJ
fcis-2971	44	22	applications	application	NOUN
fcis-2971	44	23	use	use	VERB
fcis-2971	44	24	integrated	integrate	VERB
fcis-2971	44	25	models	model	NOUN
fcis-2971	44	26	based	base	VERB
fcis-2971	44	27	on	on	ADP
fcis-2971	44	28	decision	decision	NOUN
fcis-2971	44	29	trees	tree	NOUN
fcis-2971	44	30	,	,	PUNCT
fcis-2971	44	31	in	in	ADP
fcis-2971	44	32	which	which	PRON
fcis-2971	44	33	random	random	ADJ
fcis-2971	44	34	forest	forest	NOUN
fcis-2971	44	35	is	be	AUX
fcis-2971	44	36	a	a	DET
fcis-2971	44	37	classifier	classifier	NOUN
fcis-2971	44	38	composed	compose	VERB
fcis-2971	44	39	of	of	ADP
fcis-2971	44	40	multiple	multiple	ADJ
fcis-2971	44	41	independent	independent	ADJ
fcis-2971	44	42	decision	decision	NOUN
fcis-2971	44	43	trees	tree	NOUN
fcis-2971	44	44	.	.	PUNCT
fcis-2971	45	1	random	random	ADJ
fcis-2971	45	2	forest	forest	NOUN
fcis-2971	45	3	has	have	VERB
fcis-2971	45	4	the	the	DET
fcis-2971	45	5	advantages	advantage	NOUN
fcis-2971	45	6	of	of	ADP
fcis-2971	45	7	strong	strong	ADJ
fcis-2971	45	8	generalization	generalization	NOUN
fcis-2971	45	9	ability	ability	NOUN
fcis-2971	45	10	,	,	PUNCT
fcis-2971	45	11	fast	fast	ADJ
fcis-2971	45	12	training	training	NOUN
fcis-2971	45	13	speed	speed	NOUN
fcis-2971	45	14	,	,	PUNCT
fcis-2971	45	15	can	can	AUX
fcis-2971	45	16	deal	deal	VERB
fcis-2971	45	17	with	with	ADP
fcis-2971	45	18	high	high	ADJ
fcis-2971	45	19	-	-	PUNCT
fcis-2971	45	20	dimensional	dimensional	ADJ
fcis-2971	45	21	data	datum	NOUN
fcis-2971	45	22	and	and	CCONJ
fcis-2971	45	23	does	do	AUX
fcis-2971	45	24	not	not	PART
fcis-2971	45	25	need	need	VERB
fcis-2971	45	26	feature	feature	NOUN
fcis-2971	45	27	selection	selection	NOUN
fcis-2971	45	28	.	.	PUNCT
fcis-2971	46	1	it	it	PRON
fcis-2971	46	2	has	have	AUX
fcis-2971	46	3	been	be	AUX
fcis-2971	46	4	widely	widely	ADV
fcis-2971	46	5	used	use	VERB
fcis-2971	46	6	in	in	ADP
fcis-2971	46	7	many	many	ADJ
fcis-2971	46	8	classification	classification	NOUN
fcis-2971	46	9	problems	problem	NOUN
fcis-2971	46	10	.	.	PUNCT
fcis-2971	47	1	the	the	DET
fcis-2971	47	2	training	training	NOUN
fcis-2971	47	3	process	process	NOUN
fcis-2971	47	4	of	of	ADP
fcis-2971	47	5	random	random	ADJ
fcis-2971	47	6	forest	forest	NOUN
fcis-2971	47	7	is	be	AUX
fcis-2971	47	8	actually	actually	ADV
fcis-2971	47	9	to	to	PART
fcis-2971	47	10	build	build	VERB
fcis-2971	47	11	several	several	ADJ
fcis-2971	47	12	decision	decision	NOUN
fcis-2971	47	13	trees	tree	NOUN
fcis-2971	47	14	.	.	PUNCT
fcis-2971	48	1	until	until	SCONJ
fcis-2971	48	2	each	each	DET
fcis-2971	48	3	decision	decision	NOUN
fcis-2971	48	4	tree	tree	NOUN
fcis-2971	48	5	is	be	AUX
fcis-2971	48	6	built	build	VERB
fcis-2971	48	7	,	,	PUNCT
fcis-2971	48	8	the	the	DET
fcis-2971	48	9	random	random	ADJ
fcis-2971	48	10	forest	forest	NOUN
fcis-2971	48	11	will	will	AUX
fcis-2971	48	12	be	be	AUX
fcis-2971	48	13	trained	train	VERB
fcis-2971	48	14	.	.	PUNCT
fcis-2971	49	1	the	the	DET
fcis-2971	49	2	training	training	NOUN
fcis-2971	49	3	samples	sample	NOUN
fcis-2971	49	4	of	of	ADP
fcis-2971	49	5	each	each	DET
fcis-2971	49	6	decision	decision	NOUN
fcis-2971	49	7	tree	tree	NOUN
fcis-2971	49	8	are	be	AUX
fcis-2971	49	9	sampled	sample	VERB
fcis-2971	49	10	from	from	ADP
fcis-2971	49	11	the	the	DET
fcis-2971	49	12	whole	whole	ADJ
fcis-2971	49	13	training	training	NOUN
fcis-2971	49	14	samples	sample	NOUN
fcis-2971	49	15	by	by	ADP
fcis-2971	49	16	bootstrap	bootstrap	NOUN
fcis-2971	49	17	method	method	NOUN
fcis-2971	49	18	,	,	PUNCT
fcis-2971	49	19	and	and	CCONJ
fcis-2971	49	20	the	the	DET
fcis-2971	49	21	feature	feature	NOUN
fcis-2971	49	22	attributes	attribute	NOUN
fcis-2971	49	23	used	use	VERB
fcis-2971	49	24	for	for	ADP
fcis-2971	49	25	classification	classification	NOUN
fcis-2971	49	26	of	of	ADP
fcis-2971	49	27	each	each	DET
fcis-2971	49	28	decision	decision	NOUN
fcis-2971	49	29	tree	tree	NOUN
fcis-2971	49	30	are	be	AUX
fcis-2971	49	31	randomly	randomly	ADV
fcis-2971	49	32	selected	select	VERB
fcis-2971	49	33	from	from	ADP
fcis-2971	49	34	all	all	DET
fcis-2971	49	35	feature	feature	NOUN
fcis-2971	49	36	attributes	attribute	NOUN
fcis-2971	49	37	,	,	PUNCT
fcis-2971	49	38	usually	usually	ADV
fcis-2971	49	39	the	the	DET
fcis-2971	49	40	number	number	NOUN
fcis-2971	49	41	of	of	ADP
fcis-2971	49	42	selected	select	VERB
fcis-2971	49	43	attributes	attribute	NOUN
fcis-2971	49	44	is	be	AUX
fcis-2971	49	45	less	less	ADJ
fcis-2971	49	46	than	than	ADP
fcis-2971	49	47	the	the	DET
fcis-2971	49	48	total	total	ADJ
fcis-2971	49	49	number	number	NOUN
fcis-2971	49	50	of	of	ADP
fcis-2971	49	51	feature	feature	NOUN
fcis-2971	49	52	attributes	attribute	NOUN
fcis-2971	49	53	.	.	PUNCT
fcis-2971	50	1	during	during	ADP
fcis-2971	50	2	the	the	DET
fcis-2971	50	3	test	test	NOUN
fcis-2971	50	4	of	of	ADP
fcis-2971	50	5	random	random	ADJ
fcis-2971	50	6	forest	forest	NOUN
fcis-2971	50	7	,	,	PUNCT
fcis-2971	50	8	for	for	ADP
fcis-2971	50	9	any	any	DET
fcis-2971	50	10	test	test	NOUN
fcis-2971	50	11	sample	sample	NOUN
fcis-2971	50	12	,	,	PUNCT
fcis-2971	50	13	each	each	DET
fcis-2971	50	14	decision	decision	NOUN
fcis-2971	50	15	tree	tree	NOUN
fcis-2971	50	16	will	will	AUX
fcis-2971	50	17	independently	independently	ADV
fcis-2971	50	18	judge	judge	VERB
fcis-2971	50	19	the	the	DET
fcis-2971	50	20	category	category	NOUN
fcis-2971	50	21	of	of	ADP
fcis-2971	50	22	the	the	DET
fcis-2971	50	23	sample	sample	NOUN
fcis-2971	50	24	,	,	PUNCT
fcis-2971	50	25	and	and	CCONJ
fcis-2971	50	26	finally	finally	ADV
fcis-2971	50	27	the	the	DET
fcis-2971	50	28	classification	classification	NOUN
fcis-2971	50	29	results	result	NOUN
fcis-2971	50	30	of	of	ADP
fcis-2971	50	31	the	the	DET
fcis-2971	50	32	entire	entire	ADJ
fcis-2971	50	33	random	random	ADJ
fcis-2971	50	34	forest	forest	NOUN
fcis-2971	50	35	will	will	AUX
fcis-2971	50	36	be	be	AUX
fcis-2971	50	37	obtained	obtain	VERB
fcis-2971	50	38	by	by	ADP
fcis-2971	50	39	voting	vote	VERB
fcis-2971	50	40	the	the	DET
fcis-2971	50	41	classification	classification	NOUN
fcis-2971	50	42	results	result	NOUN
fcis-2971	50	43	of	of	ADP
fcis-2971	50	44	all	all	DET
fcis-2971	50	45	decision	decision	NOUN
fcis-2971	50	46	trees	tree	NOUN
fcis-2971	50	47	.	.	PUNCT
fcis-2971	51	1	suppose	suppose	VERB
fcis-2971	51	2	that	that	SCONJ
fcis-2971	51	3	for	for	ADP
fcis-2971	51	4	a	a	DET
fcis-2971	51	5	test	test	NOUN
fcis-2971	51	6	sample	sample	NOUN
fcis-2971	51	7	x	x	NOUN
fcis-2971	51	8	,	,	PUNCT
fcis-2971	51	9	the	the	DET
fcis-2971	51	10	output	output	NOUN
fcis-2971	51	11	of	of	ADP
fcis-2971	51	12	the	the	DET
fcis-2971	51	13	kthdecision	kthdecision	NOUN
fcis-2971	51	14	tree	tree	NOUN
fcis-2971	51	15	is	be	AUX
fcis-2971	51	16	ixfk	ixfk	PROPN
fcis-2971	51	17	=)	=)	INTJ
fcis-2971	51	18	(	(	PUNCT
fcis-2971	51	19	(	(	PUNCT
fcis-2971	51	20	4	4	NUM
fcis-2971	51	21	)	)	PUNCT
fcis-2971	51	22	where	where	SCONJ
fcis-2971	51	23	i	i	PRON
fcis-2971	51	24	represents	represent	VERB
fcis-2971	51	25	the	the	DET
fcis-2971	51	26	serial	serial	ADJ
fcis-2971	51	27	number	number	NOUN
fcis-2971	51	28	of	of	ADP
fcis-2971	51	29	a	a	DET
fcis-2971	51	30	category	category	NOUN
fcis-2971	51	31	,	,	PUNCT
fcis-2971	51	32	the	the	DET
fcis-2971	51	33	set	set	NOUN
fcis-2971	51	34	of	of	ADP
fcis-2971	51	35	serial	serial	ADJ
fcis-2971	51	36	numbers	number	NOUN
fcis-2971	51	37	of	of	ADP
fcis-2971	51	38	the	the	DET
fcis-2971	51	39	decision	decision	NOUN
fcis-2971	51	40	tree	tree	NOUN
fcis-2971	51	41	output	output	NOUN
fcis-2971	51	42	as	as	SCONJ
fcis-2971	51	43	i	i	PRON
fcis-2971	51	44	is	be	AUX
fcis-2971	51	45	:	:	PUNCT
fcis-2971	52	1			PROPN
fcis-2971	52	2	kkix	kkix	PROPN
fcis-2971	53	1	k	k	INTJ
fcis-2971	53	2	fk	fk	INTJ
fcis-2971	54	1	i	i	PRON
fcis-2971	54	2	s	s	PRON
fcis-2971	54	3	,	,	PUNCT
fcis-2971	54	4	,	,	PUNCT
fcis-2971	55	1	2,1,)(|	2,1,)(|	NUM
fcis-2971	55	2	===	===	NOUN
fcis-2971	55	3	(	(	PUNCT
fcis-2971	55	4	5	5	NUM
fcis-2971	55	5	)	)	PUNCT
fcis-2971	55	6	where	where	SCONJ
fcis-2971	55	7	k	k	PROPN
fcis-2971	55	8	represents	represent	VERB
fcis-2971	55	9	the	the	DET
fcis-2971	55	10	number	number	NOUN
fcis-2971	55	11	of	of	ADP
fcis-2971	55	12	decision	decision	NOUN
fcis-2971	55	13	trees	tree	NOUN
fcis-2971	55	14	.	.	PUNCT
fcis-2971	56	1	so	so	ADV
fcis-2971	56	2	,	,	PUNCT
fcis-2971	56	3	the	the	DET
fcis-2971	56	4	output	output	NOUN
fcis-2971	56	5	of	of	ADP
fcis-2971	56	6	the	the	DET
fcis-2971	56	7	whole	whole	ADJ
fcis-2971	56	8	random	random	ADJ
fcis-2971	56	9	forest	forest	NOUN
fcis-2971	56	10	is	be	AUX
fcis-2971	56	11	:	:	PUNCT
fcis-2971	56	12	mi	mi	PROPN
fcis-2971	57	1	i	i	PRON
fcis-2971	57	2	slenx	slenx	VERB
fcis-2971	57	3	k	k	PROPN
fcis-2971	57	4	f	f	PROPN
fcis-2971	57	5	,	,	PUNCT
fcis-2971	57	6	,	,	PUNCT
fcis-2971	57	7	2,1	2,1	NUM
fcis-2971	57	8	)	)	PUNCT
fcis-2971	57	9	)	)	PUNCT
fcis-2971	57	10	(	(	PUNCT
fcis-2971	57	11	max(arg	max(arg	NOUN
fcis-2971	57	12	)	)	PUNCT
fcis-2971	57	13	(	(	PUNCT
fcis-2971	57	14	=	=	NOUN
fcis-2971	57	15	=	=	SYM
fcis-2971	57	16	(	(	PUNCT
fcis-2971	57	17	6	6	NUM
fcis-2971	57	18	)	)	PUNCT
fcis-2971	57	19	where	where	SCONJ
fcis-2971	57	20	i	i	PRON
fcis-2971	57	21	max()arg	max()arg	NOUN
fcis-2971	57	22	represents	represent	VERB
fcis-2971	57	23	the	the	DET
fcis-2971	57	24	largest	large	ADJ
fcis-2971	57	25	expression	expression	NOUN
fcis-2971	57	26	value	value	NOUN
fcis-2971	57	27	in	in	ADP
fcis-2971	57	28	the	the	DET
fcis-2971	57	29	parentheses	parenthesis	NOUN
fcis-2971	57	30	in	in	ADP
fcis-2971	57	31	all	all	DET
fcis-2971	57	32	i	i	PRON
fcis-2971	57	33	,	,	PUNCT
fcis-2971	57	34	and	and	CCONJ
fcis-2971	57	35	(	(	PUNCT
fcis-2971	57	36	)	)	PUNCT
fcis-2971	57	37	len	len	NOUN
fcis-2971	57	38	represents	represent	VERB
fcis-2971	57	39	the	the	DET
fcis-2971	57	40	size	size	NOUN
fcis-2971	57	41	of	of	ADP
fcis-2971	57	42	the	the	DET
fcis-2971	57	43	set	set	NOUN
fcis-2971	57	44	in	in	ADP
fcis-2971	57	45	the	the	DET
fcis-2971	57	46	parentheses	parenthesis	NOUN
fcis-2971	57	47	.	.	PUNCT
fcis-2971	58	1	2.3	2.3	NUM
fcis-2971	58	2	.	.	PUNCT
fcis-2971	59	1	principal	principal	ADJ
fcis-2971	59	2	component	component	NOUN
fcis-2971	59	3	analysis	analysis	NOUN
fcis-2971	59	4	principal	principal	ADJ
fcis-2971	59	5	component	component	NOUN
fcis-2971	59	6	analysis	analysis	NOUN
fcis-2971	59	7	is	be	AUX
fcis-2971	59	8	a	a	DET
fcis-2971	59	9	data	data	NOUN
fcis-2971	59	10	analysis	analysis	NOUN
fcis-2971	59	11	method	method	NOUN
fcis-2971	59	12	proposed	propose	VERB
fcis-2971	59	13	by	by	ADP
fcis-2971	59	14	k.	k.	PROPN
fcis-2971	59	15	pearson	pearson	PROPN
fcis-2971	59	16	more	more	ADJ
fcis-2971	59	17	than	than	ADP
fcis-2971	59	18	a	a	DET
fcis-2971	59	19	century	century	NOUN
fcis-2971	59	20	ago	ago	ADV
fcis-2971	59	21	.	.	PUNCT
fcis-2971	60	1	principal	principal	ADJ
fcis-2971	60	2	component	component	NOUN
fcis-2971	60	3	analysis	analysis	NOUN
fcis-2971	60	4	is	be	AUX
fcis-2971	60	5	also	also	ADV
fcis-2971	60	6	a	a	DET
fcis-2971	60	7	common	common	ADJ
fcis-2971	60	8	multi	multi	ADJ
fcis-2971	60	9	-	-	ADJ
fcis-2971	60	10	attribute	attribute	NOUN
fcis-2971	60	11	analysis	analysis	NOUN
fcis-2971	60	12	method	method	NOUN
fcis-2971	60	13	.	.	PUNCT
fcis-2971	61	1	its	its	PRON
fcis-2971	61	2	core	core	ADJ
fcis-2971	61	3	idea	idea	NOUN
fcis-2971	61	4	is	be	AUX
fcis-2971	61	5	data	datum	NOUN
fcis-2971	61	6	dimension	dimension	NOUN
fcis-2971	61	7	reduction	reduction	NOUN
fcis-2971	61	8	.	.	PUNCT
fcis-2971	62	1	the	the	DET
fcis-2971	62	2	feature	feature	NOUN
fcis-2971	62	3	of	of	ADP
fcis-2971	62	4	principal	principal	ADJ
fcis-2971	62	5	component	component	NOUN
fcis-2971	62	6	analysis	analysis	NOUN
fcis-2971	62	7	is	be	AUX
fcis-2971	62	8	very	very	ADV
fcis-2971	62	9	meaningful	meaningful	ADJ
fcis-2971	62	10	.	.	PUNCT
fcis-2971	63	1	when	when	SCONJ
fcis-2971	63	2	there	there	PRON
fcis-2971	63	3	are	be	VERB
fcis-2971	63	4	many	many	ADJ
fcis-2971	63	5	analysis	analysis	NOUN
fcis-2971	63	6	indicators	indicator	NOUN
fcis-2971	63	7	and	and	CCONJ
fcis-2971	63	8	the	the	DET
fcis-2971	63	9	process	process	NOUN
fcis-2971	63	10	is	be	AUX
fcis-2971	63	11	complex	complex	ADJ
fcis-2971	63	12	,	,	PUNCT
fcis-2971	63	13	principal	principal	ADJ
fcis-2971	63	14	component	component	NOUN
fcis-2971	63	15	analysis	analysis	NOUN
fcis-2971	63	16	can	can	AUX
fcis-2971	63	17	simplify	simplify	VERB
fcis-2971	63	18	the	the	DET
fcis-2971	63	19	analysis	analysis	NOUN
fcis-2971	63	20	process	process	NOUN
fcis-2971	63	21	and	and	CCONJ
fcis-2971	63	22	reduce	reduce	VERB
fcis-2971	63	23	the	the	DET
fcis-2971	63	24	amount	amount	NOUN
fcis-2971	63	25	of	of	ADP
fcis-2971	63	26	calculation	calculation	NOUN
fcis-2971	63	27	.	.	PUNCT
fcis-2971	64	1	because	because	SCONJ
fcis-2971	64	2	pca	pca	PROPN
fcis-2971	64	3	has	have	VERB
fcis-2971	64	4	the	the	DET
fcis-2971	64	5	advantages	advantage	NOUN
fcis-2971	64	6	of	of	ADP
fcis-2971	64	7	objectivity	objectivity	NOUN
fcis-2971	64	8	,	,	PUNCT
fcis-2971	64	9	simple	simple	ADJ
fcis-2971	64	10	calculation	calculation	NOUN
fcis-2971	64	11	and	and	CCONJ
fcis-2971	64	12	convenient	convenient	ADJ
fcis-2971	64	13	application	application	NOUN
fcis-2971	64	14	,	,	PUNCT
fcis-2971	64	15	it	it	PRON
fcis-2971	64	16	has	have	AUX
fcis-2971	64	17	been	be	AUX
fcis-2971	64	18	widely	widely	ADV
fcis-2971	64	19	used	use	VERB
fcis-2971	64	20	in	in	ADP
fcis-2971	64	21	mathematical	mathematical	ADJ
fcis-2971	64	22	modeling	modeling	NOUN
fcis-2971	64	23	,	,	PUNCT
fcis-2971	64	24	mathematical	mathematical	ADJ
fcis-2971	64	25	analysis	analysis	NOUN
fcis-2971	64	26	and	and	CCONJ
fcis-2971	64	27	other	other	ADJ
fcis-2971	64	28	disciplines	discipline	NOUN
fcis-2971	64	29	the	the	DET
fcis-2971	64	30	detailed	detailed	ADJ
fcis-2971	64	31	steps	step	NOUN
fcis-2971	64	32	of	of	ADP
fcis-2971	64	33	principal	principal	ADJ
fcis-2971	64	34	component	component	NOUN
fcis-2971	64	35	analysis	analysis	NOUN
fcis-2971	64	36	are	be	AUX
fcis-2971	64	37	as	as	SCONJ
fcis-2971	64	38	follows	follow	VERB
fcis-2971	64	39	:	:	PUNCT
fcis-2971	64	40	establish	establish	VERB
fcis-2971	64	41	evaluation	evaluation	NOUN
fcis-2971	64	42	matrix	matrix	NOUN
fcis-2971	64	43	suppose	suppose	VERB
fcis-2971	64	44	the	the	DET
fcis-2971	64	45	number	number	NOUN
fcis-2971	64	46	of	of	ADP
fcis-2971	64	47	network	network	NOUN
fcis-2971	64	48	attack	attack	NOUN
fcis-2971	64	49	sample	sample	NOUN
fcis-2971	64	50	is	be	AUX
fcis-2971	64	51	m	m	PRON
fcis-2971	64	52	,	,	PUNCT
fcis-2971	64	53	and	and	CCONJ
fcis-2971	64	54	the	the	DET
fcis-2971	64	55	number	number	NOUN
fcis-2971	64	56	of	of	ADP
fcis-2971	64	57	data	datum	NOUN
fcis-2971	64	58	characteristic	characteristic	ADJ
fcis-2971	64	59	parameters	parameter	NOUN
fcis-2971	64	60	(	(	PUNCT
fcis-2971	64	61	evaluation	evaluation	NOUN
fcis-2971	64	62	parameters	parameter	NOUN
fcis-2971	64	63	)	)	PUNCT
fcis-2971	64	64	is	be	AUX
fcis-2971	64	65	n	n	PRON
fcis-2971	64	66	,	,	PUNCT
fcis-2971	64	67	then	then	ADV
fcis-2971	64	68	the	the	DET
fcis-2971	64	69	evaluation	evaluation	NOUN
fcis-2971	64	70	matrix	matrix	NOUN
fcis-2971	64	71	is	be	AUX
fcis-2971	64	72	:	:	PUNCT
fcis-2971	64	73	(	(	PUNCT
fcis-2971	64	74	)	)	PUNCT
fcis-2971	64	75	11	11	NUM
fcis-2971	64	76	1	1	NUM
fcis-2971	64	77	1	1	NUM
fcis-2971	64	78	x=	x=	X
fcis-2971	65	1	=	=	SYM
fcis-2971	65	2	1,2	1,2	NUM
fcis-2971	65	3	,	,	PUNCT
fcis-2971	65	4	...	...	PUNCT
fcis-2971	65	5	,	,	PUNCT
fcis-2971	65	6	;	;	PUNCT
fcis-2971	65	7	1	1	NUM
fcis-2971	65	8	,	,	PUNCT
fcis-2971	65	9	2	2	NUM
fcis-2971	65	10	,	,	PUNCT
fcis-2971	65	11	...	...	PUNCT
fcis-2971	65	12	,	,	PUNCT
fcis-2971	65	13	n	n	CCONJ
fcis-2971	66	1	ij	ij	INTJ
fcis-2971	66	2	m	m	VERB
fcis-2971	66	3	n	n	PRON
fcis-2971	66	4	m	m	NOUN
fcis-2971	66	5	mn	mn	NOUN
fcis-2971	66	6	x	x	PUNCT
fcis-2971	67	1	x	x	PUNCT
fcis-2971	67	2	x	x	PUNCT
fcis-2971	67	3	x	x	PUNCT
fcis-2971	67	4	x	x	VERB
fcis-2971	67	5	i	i	PRON
fcis-2971	67	6	m	m	VERB
fcis-2971	67	7	j	j	VERB
fcis-2971	67	8	n	n	ADP
fcis-2971	67	9			VERB
fcis-2971	67	10			NOUN
fcis-2971	67	11			PROPN
fcis-2971	67	12			PROPN
fcis-2971	67	13			INTJ
fcis-2971	68	1			PROPN
fcis-2971	68	2			PROPN
fcis-2971	68	3			PROPN
fcis-2971	68	4			PROPN
fcis-2971	68	5			PROPN
fcis-2971	68	6			PROPN
fcis-2971	68	7			ADJ
fcis-2971	68	8			NOUN
fcis-2971	68	9	=	=	SYM
fcis-2971	68	10	=	=	SYM
fcis-2971	68	11	(	(	PUNCT
fcis-2971	68	12	7	7	NUM
fcis-2971	68	13	)	)	PUNCT
fcis-2971	68	14	where	where	SCONJ
fcis-2971	68	15	represents	represent	VERB
fcis-2971	68	16	the	the	DET
fcis-2971	68	17	evaluation	evaluation	NOUN
fcis-2971	68	18	value	value	NOUN
fcis-2971	68	19	of	of	ADP
fcis-2971	68	20	the	the	DET
fcis-2971	68	21	parameter	parameter	NOUN
fcis-2971	68	22	of	of	ADP
fcis-2971	68	23	the	the	DET
fcis-2971	68	24	sample	sample	NOUN
fcis-2971	68	25	.	.	PUNCT
fcis-2971	69	1	normalize	normalize	VERB
fcis-2971	69	2	evaluation	evaluation	NOUN
fcis-2971	69	3	matrix	matrix	NOUN
fcis-2971	69	4	the	the	DET
fcis-2971	69	5	purpose	purpose	NOUN
fcis-2971	69	6	of	of	ADP
fcis-2971	69	7	normalization	normalization	NOUN
fcis-2971	69	8	is	be	AUX
fcis-2971	69	9	to	to	PART
fcis-2971	69	10	eliminate	eliminate	VERB
fcis-2971	69	11	the	the	DET
fcis-2971	69	12	dimensional	dimensional	ADJ
fcis-2971	69	13	influence	influence	NOUN
fcis-2971	69	14	between	between	ADP
fcis-2971	69	15	parameters	parameter	NOUN
fcis-2971	69	16	,	,	PUNCT
fcis-2971	69	17	so	so	SCONJ
fcis-2971	69	18	as	as	SCONJ
fcis-2971	69	19	to	to	PART
fcis-2971	69	20	solve	solve	VERB
fcis-2971	69	21	the	the	DET
fcis-2971	69	22	comparability	comparability	NOUN
fcis-2971	69	23	between	between	ADP
fcis-2971	69	24	data	datum	NOUN
fcis-2971	69	25	parameter	parameter	NOUN
fcis-2971	69	26	indicators	indicator	NOUN
fcis-2971	69	27	.	.	PUNCT
fcis-2971	70	1	after	after	ADP
fcis-2971	70	2	data	data	NOUN
fcis-2971	70	3	normalization	normalization	NOUN
fcis-2971	70	4	,	,	PUNCT
fcis-2971	70	5	the	the	DET
fcis-2971	70	6	original	original	ADJ
fcis-2971	70	7	data	datum	NOUN
fcis-2971	70	8	are	be	AUX
fcis-2971	70	9	in	in	ADP
fcis-2971	70	10	the	the	DET
fcis-2971	70	11	same	same	ADJ
fcis-2971	70	12	order	order	NOUN
fcis-2971	70	13	of	of	ADP
fcis-2971	70	14	magnitude	magnitude	NOUN
fcis-2971	70	15	,	,	PUNCT
fcis-2971	70	16	which	which	PRON
fcis-2971	70	17	is	be	AUX
fcis-2971	70	18	suitable	suitable	ADJ
fcis-2971	70	19	for	for	ADP
fcis-2971	70	20	comprehensive	comprehensive	ADJ
fcis-2971	70	21	comparison	comparison	NOUN
fcis-2971	70	22	and	and	CCONJ
fcis-2971	70	23	evaluation	evaluation	NOUN
fcis-2971	70	24	.	.	PUNCT
fcis-2971	71	1	the	the	DET
fcis-2971	71	2	standardized	standardized	ADJ
fcis-2971	71	3	formula	formula	NOUN
fcis-2971	71	4	is	be	AUX
fcis-2971	71	5	as	as	SCONJ
fcis-2971	71	6	follows	follow	VERB
fcis-2971	71	7	:	:	PUNCT
fcis-2971	71	8	ij	ij	INTJ
fcis-2971	71	9	j	j	PROPN
fcis-2971	72	1	ij	ij	INTJ
fcis-2971	72	2	j	j	PROPN
fcis-2971	72	3	x	x	X
fcis-2971	72	4	x	x	X
fcis-2971	72	5	z	z	NOUN
fcis-2971	72	6			PROPN
fcis-2971	72	7	−	−	NOUN
fcis-2971	73	1	=	=	SYM
fcis-2971	73	2	(	(	PUNCT
fcis-2971	73	3	8)	8)	NUM
fcis-2971	73	4	1	1	NUM
fcis-2971	73	5	1	1	NUM
fcis-2971	73	6	=	=	SYM
fcis-2971	73	7	m	m	VERB
fcis-2971	73	8	j	j	NOUN
fcis-2971	73	9	ij	ij	INTJ
fcis-2971	74	1	i	i	NOUN
fcis-2971	74	2	x	x	X
fcis-2971	74	3	x	x	VERB
fcis-2971	74	4	m	m	VERB
fcis-2971	74	5	=	=	VERB
fcis-2971	74	6			X
fcis-2971	74	7	(	(	PUNCT
fcis-2971	74	8	9	9	NUM
fcis-2971	74	9	)	)	PUNCT
fcis-2971	74	10	(	(	PUNCT
fcis-2971	74	11	)	)	PUNCT
fcis-2971	74	12	2	2	NUM
fcis-2971	74	13	1	1	NUM
fcis-2971	74	14	1	1	NUM
fcis-2971	74	15	m	m	NOUN
fcis-2971	74	16	j	j	NOUN
fcis-2971	74	17	ij	ij	INTJ
fcis-2971	74	18	j	j	PROPN
fcis-2971	75	1	i	i	NOUN
fcis-2971	75	2	x	x	X
fcis-2971	76	1	x	x	VERB
fcis-2971	76	2	m	m	VERB
fcis-2971	76	3			PROPN
fcis-2971	76	4	=	=	SYM
fcis-2971	76	5	=	=	SYM
fcis-2971	76	6	−	−	NOUN
fcis-2971	76	7	(	(	PUNCT
fcis-2971	76	8	10	10	NUM
fcis-2971	76	9	)	)	PUNCT
fcis-2971	76	10	where	where	SCONJ
fcis-2971	76	11	jx	jx	PROPN
fcis-2971	76	12	is	be	AUX
fcis-2971	76	13	the	the	DET
fcis-2971	76	14	average	average	ADJ
fcis-2971	76	15	value	value	NOUN
fcis-2971	76	16	of	of	ADP
fcis-2971	76	17	the	the	DET
fcis-2971	76	18	parameter	parameter	NOUN
fcis-2971	76	19	and	and	CCONJ
fcis-2971	76	20	j	j	PROPN
fcis-2971	76	21	is	be	AUX
fcis-2971	76	22	the	the	DET
fcis-2971	76	23	variance	variance	NOUN
fcis-2971	76	24	of	of	ADP
fcis-2971	76	25	the	the	DET
fcis-2971	76	26	parameter	parameter	NOUN
fcis-2971	76	27	?	?	PUNCT
fcis-2971	77	1	the	the	DET
fcis-2971	77	2	standardized	standardized	ADJ
fcis-2971	77	3	matrix	matrix	NOUN
fcis-2971	77	4	is	be	AUX
fcis-2971	77	5	:	:	PUNCT
fcis-2971	77	6	(	(	PUNCT
fcis-2971	77	7	1,2	1,2	NUM
fcis-2971	77	8	,	,	PUNCT
fcis-2971	77	9	...	...	PUNCT
fcis-2971	77	10	,	,	PUNCT
fcis-2971	77	11	;	;	PUNCT
fcis-2971	77	12	1,2	1,2	NUM
fcis-2971	77	13	,	,	PUNCT
fcis-2971	77	14	...	...	PUNCT
fcis-2971	77	15	,	,	PUNCT
fcis-2971	77	16	)	)	PUNCT
fcis-2971	78	1	ij	ij	INTJ
fcis-2971	78	2	m	m	PROPN
fcis-2971	78	3	n	n	PROPN
fcis-2971	78	4	z	z	NOUN
fcis-2971	78	5	z	z	NOUN
fcis-2971	79	1	i	i	PRON
fcis-2971	79	2	m	m	VERB
fcis-2971	79	3	j	j	VERB
fcis-2971	79	4	n	n	ADP
fcis-2971	79	5			VERB
fcis-2971	79	6			PROPN
fcis-2971	79	7	=	=	NOUN
fcis-2971	79	8	=	=	SYM
fcis-2971	79	9	=	=	NOUN
fcis-2971	79	10			PROPN
fcis-2971	79	11			PROPN
fcis-2971	79	12	(	(	PUNCT
fcis-2971	79	13	11	11	NUM
fcis-2971	79	14	)	)	PUNCT
fcis-2971	79	15	calculate	calculate	VERB
fcis-2971	79	16	the	the	DET
fcis-2971	79	17	correlation	correlation	NOUN
fcis-2971	79	18	coefficient	coefficient	NOUN
fcis-2971	79	19	matrix	matrix	NOUN
fcis-2971	79	20	r.	r.	PROPN
fcis-2971	79	21	(	(	PUNCT
fcis-2971	79	22	)	)	PUNCT
fcis-2971	79	23	,	,	PUNCT
fcis-2971	79	24	1,2,	1,2,	NUM
fcis-2971	79	25	...	...	PUNCT
fcis-2971	79	26	,kj	,kj	PUNCT
fcis-2971	79	27	n	n	CCONJ
fcis-2971	79	28	n	n	NOUN
fcis-2971	79	29	r	r	NOUN
fcis-2971	79	30	r	r	NOUN
fcis-2971	79	31	k	k	PROPN
fcis-2971	79	32	j	j	PROPN
fcis-2971	80	1	n	n	ADP
fcis-2971	80	2			VERB
fcis-2971	80	3			PROPN
fcis-2971	80	4	=	=	NOUN
fcis-2971	80	5	=	=	NOUN
fcis-2971	80	6			NOUN
fcis-2971	80	7			PROPN
fcis-2971	80	8	(	(	PUNCT
fcis-2971	80	9	12	12	NUM
fcis-2971	80	10	)	)	PUNCT
fcis-2971	80	11	solve	solve	VERB
fcis-2971	80	12	the	the	DET
fcis-2971	80	13	eigenvalues	eigenvalue	NOUN
fcis-2971	80	14	and	and	CCONJ
fcis-2971	80	15	eigenvectors	eigenvector	NOUN
fcis-2971	80	16	of	of	ADP
fcis-2971	80	17	the	the	DET
fcis-2971	80	18	correlation	correlation	NOUN
fcis-2971	80	19	coefficient	coefficient	NOUN
fcis-2971	80	20	matrix	matrix	NOUN
fcis-2971	80	21	,	,	PUNCT
fcis-2971	80	22	and	and	CCONJ
fcis-2971	80	23	determine	determine	VERB
fcis-2971	80	24	the	the	DET
fcis-2971	80	25	principal	principal	ADJ
fcis-2971	80	26	components	component	NOUN
fcis-2971	80	27	.	.	PUNCT
fcis-2971	81	1	suppose	suppose	VERB
fcis-2971	81	2	0n21	0n21	X
fcis-2971	81	3			PROPN
fcis-2971	81	4			PROPN
fcis-2971	81	5	are	be	AUX
fcis-2971	81	6	the	the	DET
fcis-2971	81	7	eigenvalues	eigenvalue	NOUN
fcis-2971	81	8	of	of	ADP
fcis-2971	81	9	the	the	DET
fcis-2971	81	10	ij	ij	NOUN
fcis-2971	81	11	x	x	PROPN
fcis-2971	81	12	jth	jth	PROPN
fcis-2971	81	13	ith	ith	PROPN
fcis-2971	81	14	jth	jth	PROPN
fcis-2971	81	15	jth	jth	PROPN
fcis-2971	81	16	78	78	NUM
fcis-2971	81	17	correlation	correlation	NOUN
fcis-2971	81	18	coefficient	coefficient	NOUN
fcis-2971	81	19	matrix	matrix	NOUN
fcis-2971	81	20	and	and	CCONJ
fcis-2971	81	21	gigg	gigg	PROPN
fcis-2971	81	22	lll	lll	PROPN
fcis-2971	81	23	,	,	PUNCT
fcis-2971	81	24	,	,	PUNCT
fcis-2971	81	25	,	,	PUNCT
fcis-2971	81	26	21	21	NUM
fcis-2971	81	27			NOUN
fcis-2971	81	28	are	be	AUX
fcis-2971	81	29	the	the	DET
fcis-2971	81	30	corresponding	corresponding	ADJ
fcis-2971	81	31	eigenvectors	eigenvector	NOUN
fcis-2971	81	32	.	.	PUNCT
fcis-2971	82	1	then	then	ADV
fcis-2971	82	2	the	the	DET
fcis-2971	82	3	solution	solution	NOUN
fcis-2971	82	4	of	of	ADP
fcis-2971	82	5	the	the	DET
fcis-2971	82	6	principal	principal	ADJ
fcis-2971	82	7	component	component	NOUN
fcis-2971	82	8	is	be	AUX
fcis-2971	82	9	as	as	SCONJ
fcis-2971	82	10	follows	follow	VERB
fcis-2971	82	11	:	:	PUNCT
fcis-2971	82	12	1	1	NUM
fcis-2971	82	13	(	(	PUNCT
fcis-2971	82	14	1,2	1,2	NUM
fcis-2971	82	15	,	,	PUNCT
fcis-2971	82	16	,	,	PUNCT
fcis-2971	82	17	)	)	PUNCT
fcis-2971	83	1	n	n	CCONJ
fcis-2971	83	2	i	i	PRON
fcis-2971	83	3	gi	gi	VERB
fcis-2971	83	4	ij	ij	INTJ
fcis-2971	84	1	j	j	PROPN
fcis-2971	84	2	f	f	PROPN
fcis-2971	84	3	l	l	PROPN
fcis-2971	84	4	z	z	VERB
fcis-2971	85	1	i	i	PRON
fcis-2971	85	2	n	n	NOUN
fcis-2971	85	3	=	=	PUNCT
fcis-2971	85	4	=	=	SYM
fcis-2971	85	5	=	=	NOUN
fcis-2971	85	6			X
fcis-2971	85	7	(	(	PUNCT
fcis-2971	85	8	13	13	NUM
fcis-2971	85	9	)	)	PUNCT
fcis-2971	85	10	determine	determine	VERB
fcis-2971	85	11	the	the	DET
fcis-2971	85	12	number	number	NOUN
fcis-2971	85	13	of	of	ADP
fcis-2971	85	14	principal	principal	ADJ
fcis-2971	85	15	components	component	NOUN
fcis-2971	85	16	.	.	PUNCT
fcis-2971	86	1	in	in	ADP
fcis-2971	86	2	order	order	NOUN
fcis-2971	86	3	to	to	PART
fcis-2971	86	4	minimize	minimize	VERB
fcis-2971	86	5	workload	workload	NOUN
fcis-2971	86	6	and	and	CCONJ
fcis-2971	86	7	information	information	NOUN
fcis-2971	86	8	loss	loss	NOUN
fcis-2971	86	9	,	,	PUNCT
fcis-2971	86	10	only	only	ADV
fcis-2971	86	11	the	the	DET
fcis-2971	86	12	first	first	ADJ
fcis-2971	86	13	principal	principal	ADJ
fcis-2971	86	14	components	component	NOUN
fcis-2971	86	15	are	be	AUX
fcis-2971	86	16	retained	retain	VERB
fcis-2971	86	17	.	.	PUNCT
fcis-2971	87	1	the	the	DET
fcis-2971	87	2	value	value	NOUN
fcis-2971	87	3	of	of	ADP
fcis-2971	87	4	can	can	AUX
fcis-2971	87	5	be	be	AUX
fcis-2971	87	6	determined	determine	VERB
fcis-2971	87	7	by	by	ADP
fcis-2971	87	8	the	the	DET
fcis-2971	87	9	cumulative	cumulative	ADJ
fcis-2971	87	10	contribution	contribution	NOUN
fcis-2971	87	11	rate	rate	NOUN
fcis-2971	87	12	(	(	PUNCT
fcis-2971	87	13	)	)	PUNCT
fcis-2971	87	14	k	k	PROPN
fcis-2971	87	15	,	,	PUNCT
fcis-2971	87	16	and	and	CCONJ
fcis-2971	87	17	the	the	DET
fcis-2971	87	18	criteria	criterion	NOUN
fcis-2971	87	19	are	be	AUX
fcis-2971	87	20	as	as	SCONJ
fcis-2971	87	21	follows	follow	VERB
fcis-2971	87	22	:	:	PUNCT
fcis-2971	87	23	(	(	PUNCT
fcis-2971	87	24	)	)	PUNCT
fcis-2971	87	25	1	1	NUM
fcis-2971	87	26	/	/	SYM
fcis-2971	87	27	n	n	CCONJ
fcis-2971	87	28	g	g	NOUN
fcis-2971	87	29	g	g	PROPN
fcis-2971	87	30	g	g	PROPN
fcis-2971	87	31	g	g	PROPN
fcis-2971	87	32			ADJ
fcis-2971	87	33			ADJ
fcis-2971	87	34	=	=	SYM
fcis-2971	87	35	=	=	SYM
fcis-2971	87	36			X
fcis-2971	87	37	(	(	PUNCT
fcis-2971	87	38	14	14	NUM
fcis-2971	87	39	)	)	PUNCT
fcis-2971	87	40	(	(	PUNCT
fcis-2971	87	41	)	)	PUNCT
fcis-2971	87	42	1	1	NUM
fcis-2971	87	43	1	1	NUM
fcis-2971	87	44	1	1	NUM
fcis-2971	87	45	(	(	PUNCT
fcis-2971	87	46	/	/	SYM
fcis-2971	87	47	)	)	PUNCT
fcis-2971	87	48	k	k	PROPN
fcis-2971	88	1	k	k	PROPN
fcis-2971	88	2	n	n	CCONJ
fcis-2971	88	3	g	g	NOUN
fcis-2971	88	4	g	g	PROPN
fcis-2971	88	5	g	g	PROPN
fcis-2971	88	6	g	g	PROPN
fcis-2971	88	7	g	g	PROPN
fcis-2971	88	8	g	g	PROPN
fcis-2971	88	9	k	k	PROPN
fcis-2971	88	10			X
fcis-2971	88	11			X
fcis-2971	88	12			ADJ
fcis-2971	88	13	=	=	NOUN
fcis-2971	88	14	=	=	SYM
fcis-2971	88	15	=	=	PUNCT
fcis-2971	88	16	=	=	PUNCT
fcis-2971	88	17	=	=	NOUN
fcis-2971	88	18			X
fcis-2971	88	19			X
fcis-2971	88	20			X
fcis-2971	88	21	(	(	PUNCT
fcis-2971	88	22	15	15	NUM
fcis-2971	88	23	)	)	PUNCT
fcis-2971	88	24	in	in	ADP
fcis-2971	88	25	equation	equation	NOUN
fcis-2971	88	26	(	(	PUNCT
fcis-2971	88	27	14	14	NUM
fcis-2971	88	28	)	)	PUNCT
fcis-2971	88	29	,	,	PUNCT
fcis-2971	88	30	(	(	PUNCT
fcis-2971	88	31	)	)	PUNCT
fcis-2971	88	32	g	g	NOUN
fcis-2971	88	33	refers	refer	VERB
fcis-2971	88	34	to	to	ADP
fcis-2971	88	35	the	the	DET
fcis-2971	88	36	contribution	contribution	NOUN
fcis-2971	88	37	rate	rate	NOUN
fcis-2971	88	38	of	of	ADP
fcis-2971	88	39	each	each	DET
fcis-2971	88	40	principal	principal	ADJ
fcis-2971	88	41	component	component	NOUN
fcis-2971	88	42	.	.	PUNCT
fcis-2971	89	1	in	in	ADP
fcis-2971	89	2	general	general	ADJ
fcis-2971	89	3	,	,	PUNCT
fcis-2971	89	4	the	the	PRON
fcis-2971	89	5	greater	great	ADJ
fcis-2971	89	6	the	the	DET
fcis-2971	89	7	value	value	NOUN
fcis-2971	89	8	of	of	ADP
fcis-2971	89	9	principal	principal	ADJ
fcis-2971	89	10	component	component	NOUN
fcis-2971	89	11	contribution	contribution	NOUN
fcis-2971	89	12	rate	rate	NOUN
fcis-2971	89	13	(	(	PUNCT
fcis-2971	89	14	)	)	PUNCT
fcis-2971	89	15	g	g	NOUN
fcis-2971	89	16	,	,	PUNCT
fcis-2971	89	17	the	the	PRON
fcis-2971	89	18	richer	rich	ADJ
fcis-2971	89	19	the	the	DET
fcis-2971	89	20	information	information	NOUN
fcis-2971	89	21	of	of	ADP
fcis-2971	89	22	the	the	DET
fcis-2971	89	23	original	original	ADJ
fcis-2971	89	24	variables	variable	NOUN
fcis-2971	89	25	contained	contain	VERB
fcis-2971	89	26	in	in	ADP
fcis-2971	89	27	the	the	DET
fcis-2971	89	28	principal	principal	ADJ
fcis-2971	89	29	component	component	NOUN
fcis-2971	89	30	.	.	PUNCT
fcis-2971	90	1	generally	generally	ADV
fcis-2971	90	2	,	,	PUNCT
fcis-2971	90	3	principal	principal	ADJ
fcis-2971	90	4	components	component	NOUN
fcis-2971	90	5	with	with	ADP
fcis-2971	90	6	cumulative	cumulative	ADJ
fcis-2971	90	7	contribution	contribution	NOUN
fcis-2971	90	8	rate	rate	NOUN
fcis-2971	90	9	higher	high	ADJ
fcis-2971	90	10	than	than	ADP
fcis-2971	90	11	80	80	NUM
fcis-2971	90	12	%	%	NOUN
fcis-2971	90	13	and	and	CCONJ
fcis-2971	90	14	eigenvalue	eigenvalue	VERB
fcis-2971	90	15	greater	great	ADJ
fcis-2971	90	16	than	than	ADP
fcis-2971	90	17	1	1	NUM
fcis-2971	90	18	are	be	AUX
fcis-2971	90	19	taken	take	VERB
fcis-2971	90	20	as	as	ADP
fcis-2971	90	21	the	the	DET
fcis-2971	90	22	final	final	ADJ
fcis-2971	90	23	selected	select	VERB
fcis-2971	90	24	principal	principal	ADJ
fcis-2971	90	25	components	component	NOUN
fcis-2971	90	26	.	.	PUNCT
fcis-2971	91	1	after	after	ADP
fcis-2971	91	2	determining	determine	VERB
fcis-2971	91	3	the	the	DET
fcis-2971	91	4	principal	principal	ADJ
fcis-2971	91	5	component	component	NOUN
fcis-2971	91	6	fraction	fraction	NOUN
fcis-2971	91	7	,	,	PUNCT
fcis-2971	91	8	the	the	DET
fcis-2971	91	9	eigenvector	eigenvector	NOUN
fcis-2971	91	10	corresponding	correspond	VERB
fcis-2971	91	11	to	to	ADP
fcis-2971	91	12	the	the	DET
fcis-2971	91	13	eigenvalues	eigenvalue	NOUN
fcis-2971	91	14	of	of	ADP
fcis-2971	91	15	each	each	DET
fcis-2971	91	16	principal	principal	ADJ
fcis-2971	91	17	component	component	NOUN
fcis-2971	91	18	is	be	AUX
fcis-2971	91	19	calculated	calculate	VERB
fcis-2971	91	20	according	accord	VERB
fcis-2971	91	21	to	to	ADP
fcis-2971	91	22	the	the	DET
fcis-2971	91	23	eigenvalues	eigenvalue	NOUN
fcis-2971	91	24	of	of	ADP
fcis-2971	91	25	the	the	DET
fcis-2971	91	26	correlation	correlation	NOUN
fcis-2971	91	27	coefficient	coefficient	NOUN
fcis-2971	91	28	matrix	matrix	NOUN
fcis-2971	91	29	,	,	PUNCT
fcis-2971	91	30	and	and	CCONJ
fcis-2971	91	31	the	the	DET
fcis-2971	91	32	impact	impact	NOUN
fcis-2971	91	33	of	of	ADP
fcis-2971	91	34	each	each	DET
fcis-2971	91	35	index	index	NOUN
fcis-2971	91	36	on	on	ADP
fcis-2971	91	37	the	the	DET
fcis-2971	91	38	principal	principal	ADJ
fcis-2971	91	39	component	component	NOUN
fcis-2971	91	40	can	can	AUX
fcis-2971	91	41	be	be	AUX
fcis-2971	91	42	known	know	VERB
fcis-2971	91	43	according	accord	VERB
fcis-2971	91	44	to	to	ADP
fcis-2971	91	45	the	the	DET
fcis-2971	91	46	descending	descend	VERB
fcis-2971	91	47	order	order	NOUN
fcis-2971	91	48	of	of	ADP
fcis-2971	91	49	the	the	DET
fcis-2971	91	50	eigenvector	eigenvector	NOUN
fcis-2971	91	51	coefficients	coefficient	NOUN
fcis-2971	91	52	.	.	PUNCT
fcis-2971	92	1	the	the	PRON
fcis-2971	92	2	larger	large	ADJ
fcis-2971	92	3	the	the	DET
fcis-2971	92	4	value	value	NOUN
fcis-2971	92	5	of	of	ADP
fcis-2971	92	6	the	the	DET
fcis-2971	92	7	coefficient	coefficient	NOUN
fcis-2971	92	8	,	,	PUNCT
fcis-2971	92	9	the	the	PRON
fcis-2971	92	10	greater	great	ADJ
fcis-2971	92	11	the	the	DET
fcis-2971	92	12	influence	influence	NOUN
fcis-2971	92	13	of	of	ADP
fcis-2971	92	14	the	the	DET
fcis-2971	92	15	index	index	NOUN
fcis-2971	92	16	on	on	ADP
fcis-2971	92	17	the	the	DET
fcis-2971	92	18	principal	principal	ADJ
fcis-2971	92	19	component	component	NOUN
fcis-2971	92	20	.	.	PUNCT
fcis-2971	93	1	if	if	SCONJ
fcis-2971	93	2	the	the	DET
fcis-2971	93	3	coefficient	coefficient	NOUN
fcis-2971	93	4	value	value	NOUN
fcis-2971	93	5	is	be	AUX
fcis-2971	93	6	less	less	ADJ
fcis-2971	93	7	than	than	ADP
fcis-2971	93	8	0.1	0.1	NUM
fcis-2971	93	9	,	,	PUNCT
fcis-2971	93	10	the	the	DET
fcis-2971	93	11	influence	influence	NOUN
fcis-2971	93	12	on	on	ADP
fcis-2971	93	13	the	the	DET
fcis-2971	93	14	principal	principal	ADJ
fcis-2971	93	15	component	component	NOUN
fcis-2971	93	16	is	be	AUX
fcis-2971	93	17	negligible	negligible	ADJ
fcis-2971	93	18	.	.	PUNCT
fcis-2971	94	1	according	accord	VERB
fcis-2971	94	2	to	to	ADP
fcis-2971	94	3	the	the	DET
fcis-2971	94	4	above	above	ADJ
fcis-2971	94	5	steps	step	NOUN
fcis-2971	94	6	,	,	PUNCT
fcis-2971	94	7	a	a	DET
fcis-2971	94	8	complete	complete	ADJ
fcis-2971	94	9	indicator	indicator	NOUN
fcis-2971	94	10	parameter	parameter	NOUN
fcis-2971	94	11	system	system	NOUN
fcis-2971	94	12	can	can	AUX
fcis-2971	94	13	be	be	AUX
fcis-2971	94	14	established	establish	VERB
fcis-2971	94	15	,	,	PUNCT
fcis-2971	94	16	which	which	PRON
fcis-2971	94	17	can	can	AUX
fcis-2971	94	18	fully	fully	ADV
fcis-2971	94	19	reflect	reflect	VERB
fcis-2971	94	20	the	the	DET
fcis-2971	94	21	information	information	NOUN
fcis-2971	94	22	contained	contain	VERB
fcis-2971	94	23	in	in	ADP
fcis-2971	94	24	each	each	DET
fcis-2971	94	25	indicator	indicator	NOUN
fcis-2971	94	26	parameter	parameter	NOUN
fcis-2971	94	27	,	,	PUNCT
fcis-2971	94	28	eliminate	eliminate	VERB
fcis-2971	94	29	the	the	DET
fcis-2971	94	30	correlation	correlation	NOUN
fcis-2971	94	31	between	between	ADP
fcis-2971	94	32	each	each	DET
fcis-2971	94	33	indicator	indicator	NOUN
fcis-2971	94	34	parameter	parameter	NOUN
fcis-2971	94	35	,	,	PUNCT
fcis-2971	94	36	and	and	CCONJ
fcis-2971	94	37	facilitate	facilitate	VERB
fcis-2971	94	38	further	further	ADJ
fcis-2971	94	39	data	datum	NOUN
fcis-2971	94	40	analysis	analysis	NOUN
fcis-2971	94	41	.	.	PUNCT
fcis-2971	95	1	2.4	2.4	NUM
fcis-2971	95	2	.	.	X
fcis-2971	95	3	intrusion	intrusion	NOUN
fcis-2971	95	4	detection	detection	NOUN
fcis-2971	95	5	algorithm	algorithm	NOUN
fcis-2971	95	6	based	base	VERB
fcis-2971	95	7	on	on	ADP
fcis-2971	95	8	pca	pca	PROPN
fcis-2971	95	9	and	and	CCONJ
fcis-2971	95	10	random	random	ADJ
fcis-2971	95	11	forest	forest	NOUN
fcis-2971	95	12	classification	classification	NOUN
fcis-2971	95	13	pca	pca	NOUN
fcis-2971	95	14	algorithm	algorithm	NOUN
fcis-2971	95	15	has	have	VERB
fcis-2971	95	16	good	good	ADJ
fcis-2971	95	17	data	datum	NOUN
fcis-2971	95	18	processing	processing	NOUN
fcis-2971	95	19	ability	ability	NOUN
fcis-2971	95	20	.	.	PUNCT
fcis-2971	96	1	it	it	PRON
fcis-2971	96	2	can	can	AUX
fcis-2971	96	3	reduce	reduce	VERB
fcis-2971	96	4	the	the	DET
fcis-2971	96	5	dimension	dimension	NOUN
fcis-2971	96	6	of	of	ADP
fcis-2971	96	7	a	a	DET
fcis-2971	96	8	large	large	ADJ
fcis-2971	96	9	number	number	NOUN
fcis-2971	96	10	of	of	ADP
fcis-2971	96	11	data	datum	NOUN
fcis-2971	96	12	to	to	PART
fcis-2971	96	13	reduce	reduce	VERB
fcis-2971	96	14	the	the	DET
fcis-2971	96	15	amount	amount	NOUN
fcis-2971	96	16	of	of	ADP
fcis-2971	96	17	data	datum	NOUN
fcis-2971	96	18	while	while	SCONJ
fcis-2971	96	19	retaining	retain	VERB
fcis-2971	96	20	the	the	DET
fcis-2971	96	21	main	main	ADJ
fcis-2971	96	22	information	information	NOUN
fcis-2971	96	23	in	in	ADP
fcis-2971	96	24	the	the	DET
fcis-2971	96	25	data	datum	NOUN
fcis-2971	96	26	,	,	PUNCT
fcis-2971	96	27	and	and	CCONJ
fcis-2971	96	28	can	can	AUX
fcis-2971	96	29	remove	remove	VERB
fcis-2971	96	30	noise	noise	NOUN
fcis-2971	96	31	such	such	ADJ
fcis-2971	96	32	as	as	ADP
fcis-2971	96	33	outliers	outlier	NOUN
fcis-2971	96	34	in	in	ADP
fcis-2971	96	35	the	the	DET
fcis-2971	96	36	data	datum	NOUN
fcis-2971	96	37	.	.	PUNCT
fcis-2971	97	1	but	but	CCONJ
fcis-2971	97	2	for	for	ADP
fcis-2971	97	3	a	a	DET
fcis-2971	97	4	group	group	NOUN
fcis-2971	97	5	of	of	ADP
fcis-2971	97	6	data	datum	NOUN
fcis-2971	97	7	,	,	PUNCT
fcis-2971	97	8	the	the	DET
fcis-2971	97	9	algorithm	algorithm	NOUN
fcis-2971	97	10	itself	itself	PRON
fcis-2971	97	11	can	can	AUX
fcis-2971	97	12	not	not	PART
fcis-2971	97	13	give	give	VERB
fcis-2971	97	14	any	any	DET
fcis-2971	97	15	useful	useful	ADJ
fcis-2971	97	16	information	information	NOUN
fcis-2971	97	17	,	,	PUNCT
fcis-2971	97	18	and	and	CCONJ
fcis-2971	97	19	the	the	DET
fcis-2971	97	20	random	random	ADJ
fcis-2971	97	21	forest	forest	NOUN
fcis-2971	97	22	classifier	classifier	NOUN
fcis-2971	97	23	can	can	AUX
fcis-2971	97	24	effectively	effectively	ADV
fcis-2971	97	25	classify	classify	VERB
fcis-2971	97	26	the	the	DET
fcis-2971	97	27	data	datum	NOUN
fcis-2971	97	28	and	and	CCONJ
fcis-2971	97	29	can	can	AUX
fcis-2971	97	30	effectively	effectively	ADV
fcis-2971	97	31	prevent	prevent	VERB
fcis-2971	97	32	the	the	DET
fcis-2971	97	33	data	datum	NOUN
fcis-2971	97	34	from	from	ADP
fcis-2971	97	35	over	over	ADP
fcis-2971	97	36	fitting	fitting	ADJ
fcis-2971	97	37	.	.	PUNCT
fcis-2971	98	1	therefore	therefore	ADV
fcis-2971	98	2	,	,	PUNCT
fcis-2971	98	3	according	accord	VERB
fcis-2971	98	4	to	to	ADP
fcis-2971	98	5	the	the	DET
fcis-2971	98	6	characteristics	characteristic	NOUN
fcis-2971	98	7	of	of	ADP
fcis-2971	98	8	pca	pca	NOUN
fcis-2971	98	9	and	and	CCONJ
fcis-2971	98	10	random	random	ADJ
fcis-2971	98	11	forest	forest	NOUN
fcis-2971	98	12	classification	classification	NOUN
fcis-2971	98	13	,	,	PUNCT
fcis-2971	98	14	this	this	DET
fcis-2971	98	15	paper	paper	NOUN
fcis-2971	98	16	proposes	propose	VERB
fcis-2971	98	17	an	an	DET
fcis-2971	98	18	intrusion	intrusion	NOUN
fcis-2971	98	19	detection	detection	NOUN
fcis-2971	98	20	algorithm	algorithm	NOUN
fcis-2971	98	21	based	base	VERB
fcis-2971	98	22	on	on	ADP
fcis-2971	98	23	pca	pca	PROPN
fcis-2971	98	24	and	and	CCONJ
fcis-2971	98	25	random	random	ADJ
fcis-2971	98	26	forest	forest	NOUN
fcis-2971	98	27	classification	classification	NOUN
fcis-2971	98	28	,	,	PUNCT
fcis-2971	98	29	which	which	PRON
fcis-2971	98	30	combines	combine	VERB
fcis-2971	98	31	pca	pca	NOUN
fcis-2971	98	32	and	and	CCONJ
fcis-2971	98	33	random	random	ADJ
fcis-2971	98	34	forest	forest	NOUN
fcis-2971	98	35	classification	classification	NOUN
fcis-2971	98	36	to	to	PART
fcis-2971	98	37	improve	improve	VERB
fcis-2971	98	38	the	the	DET
fcis-2971	98	39	accuracy	accuracy	NOUN
fcis-2971	98	40	of	of	ADP
fcis-2971	98	41	classification	classification	NOUN
fcis-2971	98	42	and	and	CCONJ
fcis-2971	98	43	reduce	reduce	VERB
fcis-2971	98	44	the	the	DET
fcis-2971	98	45	false	false	ADJ
fcis-2971	98	46	alarm	alarm	NOUN
fcis-2971	98	47	rate	rate	NOUN
fcis-2971	98	48	of	of	ADP
fcis-2971	98	49	the	the	DET
fcis-2971	98	50	algorithm	algorithm	NOUN
fcis-2971	98	51	.	.	PUNCT
fcis-2971	99	1	the	the	DET
fcis-2971	99	2	training	training	NOUN
fcis-2971	99	3	steps	step	NOUN
fcis-2971	99	4	of	of	ADP
fcis-2971	99	5	intrusion	intrusion	NOUN
fcis-2971	99	6	detection	detection	NOUN
fcis-2971	99	7	algorithm	algorithm	NOUN
fcis-2971	99	8	are	be	AUX
fcis-2971	99	9	as	as	SCONJ
fcis-2971	99	10	follows	follow	VERB
fcis-2971	99	11	:	:	PUNCT
fcis-2971	99	12	select	select	VERB
fcis-2971	99	13	the	the	DET
fcis-2971	99	14	number	number	NOUN
fcis-2971	99	15	of	of	ADP
fcis-2971	99	16	maximum	maximum	ADJ
fcis-2971	99	17	characteristic	characteristic	ADJ
fcis-2971	99	18	values	value	NOUN
fcis-2971	99	19	(	(	PUNCT
fcis-2971	99	20	k	k	NOUN
fcis-2971	99	21	value	value	NOUN
fcis-2971	99	22	)	)	PUNCT
fcis-2971	99	23	to	to	PART
fcis-2971	99	24	be	be	AUX
fcis-2971	99	25	retained	retain	VERB
fcis-2971	99	26	according	accord	VERB
fcis-2971	99	27	to	to	ADP
fcis-2971	99	28	the	the	DET
fcis-2971	99	29	properties	property	NOUN
fcis-2971	99	30	of	of	ADP
fcis-2971	99	31	the	the	DET
fcis-2971	99	32	original	original	ADJ
fcis-2971	99	33	data	datum	NOUN
fcis-2971	99	34	.	.	PUNCT
fcis-2971	100	1	pca	pca	PROPN
fcis-2971	100	2	is	be	AUX
fcis-2971	100	3	used	use	VERB
fcis-2971	100	4	to	to	PART
fcis-2971	100	5	reduce	reduce	VERB
fcis-2971	100	6	the	the	DET
fcis-2971	100	7	dimension	dimension	NOUN
fcis-2971	100	8	of	of	ADP
fcis-2971	100	9	the	the	DET
fcis-2971	100	10	data	datum	NOUN
fcis-2971	100	11	(	(	PUNCT
fcis-2971	100	12	retain	retain	VERB
fcis-2971	100	13	the	the	DET
fcis-2971	100	14	first	first	ADJ
fcis-2971	100	15	k	k	PROPN
fcis-2971	100	16	features	feature	NOUN
fcis-2971	100	17	with	with	ADP
fcis-2971	100	18	the	the	DET
fcis-2971	100	19	largest	large	ADJ
fcis-2971	100	20	eigenvalue	eigenvalue	NOUN
fcis-2971	100	21	)	)	PUNCT
fcis-2971	100	22	,	,	PUNCT
fcis-2971	100	23	and	and	CCONJ
fcis-2971	100	24	remove	remove	VERB
fcis-2971	100	25	the	the	DET
fcis-2971	100	26	noise	noise	NOUN
fcis-2971	100	27	.	.	PUNCT
fcis-2971	101	1	according	accord	VERB
fcis-2971	101	2	to	to	ADP
fcis-2971	101	3	the	the	DET
fcis-2971	101	4	data	data	NOUN
fcis-2971	101	5	characteristics	characteristic	NOUN
fcis-2971	101	6	after	after	ADP
fcis-2971	101	7	preprocessing	preprocesse	VERB
fcis-2971	101	8	,	,	PUNCT
fcis-2971	101	9	the	the	DET
fcis-2971	101	10	forest	forest	NOUN
fcis-2971	101	11	density	density	NOUN
fcis-2971	101	12	(	(	PUNCT
fcis-2971	101	13	the	the	DET
fcis-2971	101	14	number	number	NOUN
fcis-2971	101	15	of	of	ADP
fcis-2971	101	16	decision	decision	NOUN
fcis-2971	101	17	trees	tree	NOUN
fcis-2971	101	18	)	)	PUNCT
fcis-2971	101	19	of	of	ADP
fcis-2971	101	20	the	the	DET
fcis-2971	101	21	random	random	ADJ
fcis-2971	101	22	forest	forest	NOUN
fcis-2971	101	23	classifier	classifier	NOUN
fcis-2971	101	24	is	be	AUX
fcis-2971	101	25	selected	select	VERB
fcis-2971	101	26	.	.	PUNCT
fcis-2971	102	1	the	the	DET
fcis-2971	102	2	random	random	ADJ
fcis-2971	102	3	forest	forest	NOUN
fcis-2971	102	4	classifier	classifier	NOUN
fcis-2971	102	5	is	be	AUX
fcis-2971	102	6	used	use	VERB
fcis-2971	102	7	to	to	PART
fcis-2971	102	8	train	train	VERB
fcis-2971	102	9	the	the	DET
fcis-2971	102	10	data	datum	NOUN
fcis-2971	102	11	to	to	PART
fcis-2971	102	12	get	get	VERB
fcis-2971	102	13	a	a	DET
fcis-2971	102	14	classifier	classifier	NOUN
fcis-2971	102	15	that	that	PRON
fcis-2971	102	16	can	can	AUX
fcis-2971	102	17	be	be	AUX
fcis-2971	102	18	used	use	VERB
fcis-2971	102	19	for	for	ADP
fcis-2971	102	20	intrusion	intrusion	NOUN
fcis-2971	102	21	detection	detection	NOUN
fcis-2971	102	22	.	.	PUNCT
fcis-2971	103	1	select	select	VERB
fcis-2971	103	2	different	different	ADJ
fcis-2971	103	3	k	k	PROPN
fcis-2971	103	4	values	value	NOUN
fcis-2971	103	5	and	and	CCONJ
fcis-2971	103	6	forest	forest	NOUN
fcis-2971	103	7	densities	density	NOUN
fcis-2971	103	8	to	to	PART
fcis-2971	103	9	repeat	repeat	VERB
fcis-2971	103	10	the	the	DET
fcis-2971	103	11	above	above	ADJ
fcis-2971	103	12	steps	step	NOUN
fcis-2971	103	13	,	,	PUNCT
fcis-2971	103	14	compare	compare	VERB
fcis-2971	103	15	the	the	DET
fcis-2971	103	16	impact	impact	NOUN
fcis-2971	103	17	of	of	ADP
fcis-2971	103	18	different	different	ADJ
fcis-2971	103	19	k	k	PROPN
fcis-2971	103	20	values	value	NOUN
fcis-2971	103	21	and	and	CCONJ
fcis-2971	103	22	forest	forest	NOUN
fcis-2971	103	23	densities	density	NOUN
fcis-2971	103	24	on	on	ADP
fcis-2971	103	25	the	the	DET
fcis-2971	103	26	classifier	classifier	NOUN
fcis-2971	103	27	,	,	PUNCT
fcis-2971	103	28	and	and	CCONJ
fcis-2971	103	29	select	select	VERB
fcis-2971	103	30	the	the	DET
fcis-2971	103	31	best	good	ADJ
fcis-2971	103	32	classification	classification	NOUN
fcis-2971	103	33	result	result	NOUN
fcis-2971	103	34	as	as	ADP
fcis-2971	103	35	the	the	DET
fcis-2971	103	36	final	final	ADJ
fcis-2971	103	37	classifier	classifier	NOUN
fcis-2971	103	38	.	.	PUNCT
fcis-2971	104	1	the	the	DET
fcis-2971	104	2	training	training	NOUN
fcis-2971	104	3	flow	flow	NOUN
fcis-2971	104	4	chart	chart	NOUN
fcis-2971	104	5	of	of	ADP
fcis-2971	104	6	the	the	DET
fcis-2971	104	7	intrusion	intrusion	NOUN
fcis-2971	104	8	detection	detection	NOUN
fcis-2971	104	9	algorithm	algorithm	NOUN
fcis-2971	104	10	proposed	propose	VERB
fcis-2971	104	11	in	in	ADP
fcis-2971	104	12	this	this	DET
fcis-2971	104	13	paper	paper	NOUN
fcis-2971	104	14	is	be	AUX
fcis-2971	104	15	shown	show	VERB
fcis-2971	104	16	in	in	ADP
fcis-2971	104	17	figure	figure	NOUN
fcis-2971	104	18	1	1	NUM
fcis-2971	104	19	.	.	PUNCT
fcis-2971	104	20	point	point	NOUN
fcis-2971	104	21	value	value	NOUN
fcis-2971	104	22	pca	pca	NOUN
fcis-2971	104	23	classifier	classifier	NOUN
fcis-2971	104	24	is	be	AUX
fcis-2971	104	25	selected	select	VERB
fcis-2971	104	26	for	for	ADP
fcis-2971	104	27	initial	initial	ADJ
fcis-2971	104	28	data	datum	NOUN
fcis-2971	104	29	,	,	PUNCT
fcis-2971	104	30	and	and	CCONJ
fcis-2971	104	31	forest	forest	NOUN
fcis-2971	104	32	density	density	NOUN
fcis-2971	104	33	is	be	AUX
fcis-2971	104	34	selected	select	VERB
fcis-2971	104	35	for	for	ADP
fcis-2971	104	36	random	random	ADJ
fcis-2971	104	37	forest	forest	NOUN
fcis-2971	104	38	classification	classification	NOUN
fcis-2971	104	39	.	.	PUNCT
fcis-2971	105	1	it	it	PRON
fcis-2971	105	2	is	be	AUX
fcis-2971	105	3	worth	worth	ADJ
fcis-2971	105	4	mentioning	mention	VERB
fcis-2971	105	5	that	that	SCONJ
fcis-2971	105	6	,	,	PUNCT
fcis-2971	105	7	because	because	SCONJ
fcis-2971	105	8	the	the	DET
fcis-2971	105	9	characteristics	characteristic	NOUN
fcis-2971	105	10	of	of	ADP
fcis-2971	105	11	different	different	ADJ
fcis-2971	105	12	data	datum	NOUN
fcis-2971	105	13	sets	set	NOUN
fcis-2971	105	14	are	be	AUX
fcis-2971	105	15	very	very	ADV
fcis-2971	105	16	different	different	ADJ
fcis-2971	105	17	,	,	PUNCT
fcis-2971	105	18	the	the	DET
fcis-2971	105	19	ideal	ideal	ADJ
fcis-2971	105	20	k	k	PROPN
fcis-2971	105	21	values	value	NOUN
fcis-2971	105	22	and	and	CCONJ
fcis-2971	105	23	forest	forest	NOUN
fcis-2971	105	24	density	density	NOUN
fcis-2971	105	25	values	value	NOUN
fcis-2971	105	26	under	under	ADP
fcis-2971	105	27	different	different	ADJ
fcis-2971	105	28	data	data	NOUN
fcis-2971	105	29	sets	set	NOUN
fcis-2971	105	30	are	be	AUX
fcis-2971	105	31	often	often	ADV
fcis-2971	105	32	very	very	ADV
fcis-2971	105	33	different	different	ADJ
fcis-2971	105	34	,	,	PUNCT
fcis-2971	105	35	and	and	CCONJ
fcis-2971	105	36	there	there	PRON
fcis-2971	105	37	is	be	VERB
fcis-2971	105	38	no	no	DET
fcis-2971	105	39	universally	universally	ADV
fcis-2971	105	40	applicable	applicable	ADJ
fcis-2971	105	41	optimal	optimal	ADJ
fcis-2971	105	42	solution	solution	NOUN
fcis-2971	105	43	.	.	PUNCT
fcis-2971	106	1	for	for	ADP
fcis-2971	106	2	general	general	ADJ
fcis-2971	106	3	data	datum	NOUN
fcis-2971	106	4	sets	set	NOUN
fcis-2971	106	5	,	,	PUNCT
fcis-2971	106	6	if	if	SCONJ
fcis-2971	106	7	the	the	DET
fcis-2971	106	8	k	k	PROPN
fcis-2971	106	9	value	value	NOUN
fcis-2971	106	10	is	be	AUX
fcis-2971	106	11	too	too	ADV
fcis-2971	106	12	large	large	ADJ
fcis-2971	106	13	,	,	PUNCT
fcis-2971	106	14	the	the	DET
fcis-2971	106	15	pca	pca	NOUN
fcis-2971	106	16	feature	feature	NOUN
fcis-2971	106	17	reduction	reduction	NOUN
fcis-2971	106	18	and	and	CCONJ
fcis-2971	106	19	denoising	denoising	NOUN
fcis-2971	106	20	effect	effect	NOUN
fcis-2971	106	21	can	can	AUX
fcis-2971	106	22	not	not	PART
fcis-2971	106	23	be	be	AUX
fcis-2971	106	24	well	well	ADV
fcis-2971	106	25	exerted	exert	VERB
fcis-2971	106	26	;	;	PUNCT
fcis-2971	106	27	if	if	SCONJ
fcis-2971	106	28	the	the	DET
fcis-2971	106	29	value	value	NOUN
fcis-2971	106	30	of	of	ADP
fcis-2971	106	31	k	k	PROPN
fcis-2971	106	32	is	be	AUX
fcis-2971	106	33	too	too	ADV
fcis-2971	106	34	small	small	ADJ
fcis-2971	106	35	,	,	PUNCT
fcis-2971	106	36	the	the	DET
fcis-2971	106	37	data	datum	NOUN
fcis-2971	106	38	will	will	AUX
fcis-2971	106	39	lose	lose	VERB
fcis-2971	106	40	most	most	ADJ
fcis-2971	106	41	of	of	ADP
fcis-2971	106	42	its	its	PRON
fcis-2971	106	43	original	original	ADJ
fcis-2971	106	44	information	information	NOUN
fcis-2971	106	45	.	.	PUNCT
fcis-2971	107	1	in	in	ADP
fcis-2971	107	2	addition	addition	NOUN
fcis-2971	107	3	,	,	PUNCT
fcis-2971	107	4	the	the	DET
fcis-2971	107	5	data	data	NOUN
fcis-2971	107	6	distribution	distribution	NOUN
fcis-2971	107	7	of	of	ADP
fcis-2971	107	8	some	some	DET
fcis-2971	107	9	datasets	dataset	NOUN
fcis-2971	107	10	is	be	AUX
fcis-2971	107	11	too	too	ADV
fcis-2971	107	12	sparse	sparse	ADJ
fcis-2971	107	13	.	.	PUNCT
fcis-2971	108	1	for	for	ADP
fcis-2971	108	2	these	these	DET
fcis-2971	108	3	datasets	dataset	NOUN
fcis-2971	108	4	,	,	PUNCT
fcis-2971	108	5	the	the	DET
fcis-2971	108	6	ideal	ideal	ADJ
fcis-2971	108	7	k	k	PROPN
fcis-2971	108	8	value	value	NOUN
fcis-2971	108	9	is	be	AUX
fcis-2971	108	10	often	often	ADV
fcis-2971	108	11	small	small	ADJ
fcis-2971	108	12	;	;	PUNCT
fcis-2971	108	13	for	for	ADP
fcis-2971	108	14	data	data	NOUN
fcis-2971	108	15	sets	set	NOUN
fcis-2971	108	16	with	with	ADP
fcis-2971	108	17	very	very	ADV
fcis-2971	108	18	dense	dense	ADJ
fcis-2971	108	19	data	datum	NOUN
fcis-2971	108	20	distribution	distribution	NOUN
fcis-2971	108	21	,	,	PUNCT
fcis-2971	108	22	the	the	DET
fcis-2971	108	23	ideal	ideal	ADJ
fcis-2971	108	24	k	k	PROPN
fcis-2971	108	25	value	value	NOUN
fcis-2971	108	26	is	be	AUX
fcis-2971	108	27	often	often	ADV
fcis-2971	108	28	large	large	ADJ
fcis-2971	108	29	,	,	PUNCT
fcis-2971	108	30	or	or	CCONJ
fcis-2971	108	31	even	even	ADV
fcis-2971	108	32	nearly	nearly	ADV
fcis-2971	108	33	the	the	DET
fcis-2971	108	34	same	same	ADJ
fcis-2971	108	35	dimension	dimension	NOUN
fcis-2971	108	36	as	as	ADP
fcis-2971	108	37	the	the	DET
fcis-2971	108	38	original	original	ADJ
fcis-2971	108	39	data	datum	NOUN
fcis-2971	108	40	set	set	VERB
fcis-2971	108	41	.	.	PUNCT
fcis-2971	109	1	figure	figure	NOUN
fcis-2971	109	2	1	1	NUM
fcis-2971	109	3	.	.	PUNCT
fcis-2971	110	1	flow	flow	VERB
fcis-2971	110	2	chart	chart	NOUN
fcis-2971	110	3	of	of	ADP
fcis-2971	110	4	intrusion	intrusion	NOUN
fcis-2971	110	5	detection	detection	NOUN
fcis-2971	110	6	algorithm	algorithm	NOUN
fcis-2971	110	7	there	there	PRON
fcis-2971	110	8	are	be	VERB
fcis-2971	110	9	also	also	ADV
fcis-2971	110	10	corresponding	correspond	VERB
fcis-2971	110	11	judgments	judgment	NOUN
fcis-2971	110	12	on	on	ADP
fcis-2971	110	13	the	the	DET
fcis-2971	110	14	forest	forest	NOUN
fcis-2971	110	15	density	density	NOUN
fcis-2971	110	16	of	of	ADP
fcis-2971	110	17	random	random	ADJ
fcis-2971	110	18	forests	forest	NOUN
fcis-2971	110	19	.	.	PUNCT
fcis-2971	111	1	if	if	SCONJ
fcis-2971	111	2	the	the	DET
fcis-2971	111	3	number	number	NOUN
fcis-2971	111	4	of	of	ADP
fcis-2971	111	5	decision	decision	NOUN
fcis-2971	111	6	trees	tree	NOUN
fcis-2971	111	7	in	in	ADP
fcis-2971	111	8	the	the	DET
fcis-2971	111	9	forest	forest	NOUN
fcis-2971	111	10	is	be	AUX
fcis-2971	111	11	too	too	ADV
fcis-2971	111	12	small	small	ADJ
fcis-2971	111	13	,	,	PUNCT
fcis-2971	111	14	the	the	PRON
fcis-2971	111	15	over	over	ADP
fcis-2971	111	16	fitting	fitting	ADJ
fcis-2971	111	17	phenomenon	phenomenon	NOUN
fcis-2971	111	18	of	of	ADP
fcis-2971	111	19	data	datum	NOUN
fcis-2971	111	20	can	can	AUX
fcis-2971	111	21	not	not	PART
fcis-2971	111	22	be	be	AUX
fcis-2971	111	23	eliminated	eliminate	VERB
fcis-2971	111	24	well	well	ADV
fcis-2971	111	25	,	,	PUNCT
fcis-2971	111	26	and	and	CCONJ
fcis-2971	111	27	the	the	DET
fcis-2971	111	28	advantages	advantage	NOUN
fcis-2971	111	29	of	of	ADP
fcis-2971	111	30	the	the	DET
fcis-2971	111	31	random	random	ADJ
fcis-2971	111	32	forest	forest	NOUN
fcis-2971	111	33	method	method	NOUN
fcis-2971	111	34	can	can	AUX
fcis-2971	111	35	not	not	PART
fcis-2971	111	36	be	be	AUX
fcis-2971	111	37	reflected	reflect	VERB
fcis-2971	111	38	.	.	PUNCT
fcis-2971	112	1	if	if	SCONJ
fcis-2971	112	2	the	the	DET
fcis-2971	112	3	number	number	NOUN
fcis-2971	112	4	of	of	ADP
fcis-2971	112	5	decision	decision	NOUN
fcis-2971	112	6	trees	tree	NOUN
fcis-2971	112	7	in	in	ADP
fcis-2971	112	8	the	the	DET
fcis-2971	112	9	forest	forest	NOUN
fcis-2971	112	10	exceeds	exceed	VERB
fcis-2971	112	11	a	a	DET
fcis-2971	112	12	certain	certain	ADJ
fcis-2971	112	13	limit	limit	NOUN
fcis-2971	112	14	,	,	PUNCT
fcis-2971	112	15	the	the	DET
fcis-2971	112	16	new	new	ADJ
fcis-2971	112	17	decision	decision	NOUN
fcis-2971	112	18	trees	tree	NOUN
fcis-2971	112	19	will	will	AUX
fcis-2971	112	20	not	not	PART
fcis-2971	112	21	improve	improve	VERB
fcis-2971	112	22	the	the	DET
fcis-2971	112	23	classification	classification	NOUN
fcis-2971	112	24	effect	effect	NOUN
fcis-2971	112	25	of	of	ADP
fcis-2971	112	26	the	the	DET
fcis-2971	112	27	random	random	ADJ
fcis-2971	112	28	forest	forest	NOUN
fcis-2971	112	29	classifier	classifier	NOUN
fcis-2971	112	30	,	,	PUNCT
fcis-2971	112	31	but	but	CCONJ
fcis-2971	112	32	will	will	AUX
fcis-2971	112	33	drag	drag	VERB
fcis-2971	112	34	down	down	ADP
fcis-2971	112	35	the	the	DET
fcis-2971	112	36	overall	overall	ADJ
fcis-2971	112	37	classification	classification	NOUN
fcis-2971	112	38	speed	speed	NOUN
fcis-2971	112	39	due	due	ADP
fcis-2971	112	40	to	to	ADP
fcis-2971	112	41	the	the	DET
fcis-2971	112	42	increased	increase	VERB
fcis-2971	112	43	amount	amount	NOUN
fcis-2971	112	44	of	of	ADP
fcis-2971	112	45	calculation	calculation	NOUN
fcis-2971	112	46	.	.	PUNCT
fcis-2971	113	1	therefore	therefore	ADV
fcis-2971	113	2	,	,	PUNCT
fcis-2971	113	3	this	this	DET
fcis-2971	113	4	paper	paper	NOUN
fcis-2971	113	5	adopts	adopt	VERB
fcis-2971	113	6	a	a	DET
fcis-2971	113	7	trial	trial	NOUN
fcis-2971	113	8	and	and	CCONJ
fcis-2971	113	9	error	error	NOUN
fcis-2971	113	10	method	method	NOUN
fcis-2971	113	11	for	for	ADP
fcis-2971	113	12	the	the	DET
fcis-2971	113	13	selection	selection	NOUN
fcis-2971	113	14	of	of	ADP
fcis-2971	113	15	k	k	PROPN
fcis-2971	113	16	value	value	NOUN
fcis-2971	113	17	and	and	CCONJ
fcis-2971	113	18	forest	forest	NOUN
fcis-2971	113	19	density	density	NOUN
fcis-2971	113	20	,	,	PUNCT
fcis-2971	113	21	constantly	constantly	ADV
fcis-2971	113	22	enumerates	enumerate	VERB
fcis-2971	113	23	possible	possible	ADJ
fcis-2971	113	24	k	k	PROPN
fcis-2971	113	25	values	value	NOUN
fcis-2971	113	26	and	and	CCONJ
fcis-2971	113	27	forest	forest	NOUN
fcis-2971	113	28	density	density	NOUN
fcis-2971	113	29	for	for	ADP
fcis-2971	113	30	experiments	experiment	NOUN
fcis-2971	113	31	and	and	CCONJ
fcis-2971	113	32	comparison	comparison	NOUN
fcis-2971	113	33	,	,	PUNCT
fcis-2971	113	34	and	and	CCONJ
fcis-2971	113	35	finally	finally	ADV
fcis-2971	113	36	selects	select	VERB
fcis-2971	113	37	the	the	DET
fcis-2971	113	38	value	value	NOUN
fcis-2971	113	39	that	that	PRON
fcis-2971	113	40	conforms	conform	VERB
fcis-2971	113	41	to	to	ADP
fcis-2971	113	42	the	the	DET
fcis-2971	113	43	dataset	dataset	NOUN
fcis-2971	113	44	used	use	VERB
fcis-2971	113	45	in	in	ADP
fcis-2971	113	46	this	this	DET
fcis-2971	113	47	paper	paper	NOUN
fcis-2971	113	48	.	.	PUNCT
fcis-2971	114	1	3	3	X
fcis-2971	114	2	.	.	X
fcis-2971	114	3	experiments	experiment	NOUN
fcis-2971	114	4	in	in	ADP
fcis-2971	114	5	the	the	DET
fcis-2971	114	6	experimental	experimental	ADJ
fcis-2971	114	7	part	part	NOUN
fcis-2971	114	8	,	,	PUNCT
fcis-2971	114	9	nsl_kdd	nsl_kdd	CCONJ
fcis-2971	114	10	dataset	dataset	NOUN
fcis-2971	114	11	is	be	AUX
fcis-2971	114	12	used	use	VERB
fcis-2971	114	13	,	,	PUNCT
fcis-2971	114	14	which	which	PRON
fcis-2971	114	15	is	be	AUX
fcis-2971	114	16	derived	derive	VERB
fcis-2971	114	17	from	from	ADP
fcis-2971	114	18	kdd99	kdd99	NOUN
fcis-2971	114	19	dataset	dataset	NOUN
fcis-2971	114	20	.	.	PUNCT
fcis-2971	115	1	although	although	SCONJ
fcis-2971	115	2	the	the	DET
fcis-2971	115	3	nsl_kdd	nsl_kdd	ADJ
fcis-2971	115	4	dataset	dataset	NOUN
fcis-2971	115	5	is	be	AUX
fcis-2971	115	6	still	still	ADV
fcis-2971	115	7	not	not	PART
fcis-2971	115	8	a	a	DET
fcis-2971	115	9	perfect	perfect	ADJ
fcis-2971	115	10	dataset	dataset	NOUN
fcis-2971	115	11	compared	compare	VERB
fcis-2971	115	12	with	with	ADP
fcis-2971	115	13	the	the	DET
fcis-2971	115	14	real	real	ADJ
fcis-2971	115	15	network	network	NOUN
fcis-2971	115	16	environment	environment	NOUN
fcis-2971	115	17	,	,	PUNCT
fcis-2971	115	18	it	it	PRON
fcis-2971	115	19	is	be	AUX
fcis-2971	115	20	an	an	DET
fcis-2971	115	21	effective	effective	ADJ
fcis-2971	115	22	benchmark	benchmark	NOUN
fcis-2971	115	23	dataset	dataset	NOUN
fcis-2971	115	24	for	for	ADP
fcis-2971	115	25	intrusion	intrusion	NOUN
fcis-2971	115	26	detection	detection	NOUN
fcis-2971	115	27	researchers	researcher	NOUN
fcis-2971	115	28	.	.	PUNCT
fcis-2971	116	1	it	it	PRON
fcis-2971	116	2	solves	solve	VERB
fcis-2971	116	3	some	some	DET
fcis-2971	116	4	inherent	inherent	ADJ
fcis-2971	116	5	problems	problem	NOUN
fcis-2971	116	6	of	of	ADP
fcis-2971	116	7	kdd99	kdd99	PROPN
fcis-2971	116	8	dataset	dataset	NOUN
fcis-2971	116	9	,	,	PUNCT
fcis-2971	116	10	such	such	ADJ
fcis-2971	116	11	as	as	ADP
fcis-2971	116	12	redundant	redundant	ADJ
fcis-2971	116	13	records	record	NOUN
fcis-2971	116	14	in	in	ADP
fcis-2971	116	15	the	the	DET
fcis-2971	116	16	training	training	NOUN
fcis-2971	116	17	set	set	NOUN
fcis-2971	116	18	,	,	PUNCT
fcis-2971	116	19	so	so	CCONJ
fcis-2971	116	20	the	the	DET
fcis-2971	116	21	classifier	classifier	NOUN
fcis-2971	116	22	will	will	AUX
fcis-2971	116	23	not	not	PART
fcis-2971	116	24	favor	favor	VERB
fcis-2971	116	25	records	record	NOUN
fcis-2971	116	26	with	with	ADP
fcis-2971	116	27	high	high	ADJ
fcis-2971	116	28	frequency	frequency	NOUN
fcis-2971	116	29	,	,	PUNCT
fcis-2971	116	30	and	and	CCONJ
fcis-2971	116	31	the	the	DET
fcis-2971	116	32	number	number	NOUN
fcis-2971	116	33	of	of	ADP
fcis-2971	116	34	records	record	NOUN
fcis-2971	116	35	in	in	ADP
fcis-2971	116	36	the	the	DET
fcis-2971	116	37	training	training	NOUN
fcis-2971	116	38	set	set	NOUN
fcis-2971	116	39	and	and	CCONJ
fcis-2971	116	40	test	test	NOUN
fcis-2971	116	41	set	set	VERB
fcis-2971	116	42	is	be	AUX
fcis-2971	116	43	reasonable	reasonable	ADJ
fcis-2971	116	44	.	.	PUNCT
fcis-2971	117	1	nsl_kdd	nsl_kdd	NOUN
fcis-2971	117	2	dataset	dataset	NOUN
fcis-2971	117	3	is	be	AUX
fcis-2971	117	4	widely	widely	ADV
fcis-2971	117	5	used	use	VERB
fcis-2971	117	6	in	in	ADP
fcis-2971	117	7	intrusion	intrusion	NOUN
fcis-2971	117	8	detection	detection	NOUN
fcis-2971	117	9	field	field	NOUN
fcis-2971	117	10	.	.	PUNCT
fcis-2971	118	1	the	the	DET
fcis-2971	118	2	nsl_kdd	nsl_kdd	NUM
fcis-2971	118	3	dataset	dataset	NOUN
fcis-2971	118	4	contains	contain	VERB
fcis-2971	118	5	normal	normal	ADJ
fcis-2971	118	6	and	and	CCONJ
fcis-2971	118	7	abnormal	abnormal	ADJ
fcis-2971	118	8	data	datum	NOUN
fcis-2971	118	9	.	.	PUNCT
fcis-2971	119	1	the	the	DET
fcis-2971	119	2	data	datum	NOUN
fcis-2971	119	3	can	can	AUX
fcis-2971	119	4	be	be	AUX
fcis-2971	119	5	divided	divide	VERB
fcis-2971	119	6	into	into	ADP
fcis-2971	119	7	23	23	NUM
fcis-2971	119	8	categories	category	NOUN
fcis-2971	119	9	according	accord	VERB
fcis-2971	119	10	to	to	ADP
fcis-2971	119	11	different	different	ADJ
fcis-2971	119	12	attack	attack	NOUN
fcis-2971	119	13	modes	mode	NOUN
fcis-2971	119	14	,	,	PUNCT
fcis-2971	119	15	which	which	PRON
fcis-2971	119	16	can	can	AUX
fcis-2971	119	17	be	be	AUX
fcis-2971	119	18	further	far	ADV
fcis-2971	119	19	classified	classify	VERB
fcis-2971	119	20	into	into	ADP
fcis-2971	119	21	5	5	NUM
fcis-2971	119	22	categories	category	NOUN
fcis-2971	119	23	.	.	PUNCT
fcis-2971	120	1	in	in	ADP
fcis-2971	120	2	this	this	DET
fcis-2971	120	3	experiment	experiment	NOUN
fcis-2971	120	4	,	,	PUNCT
fcis-2971	120	5	30	30	NUM
fcis-2971	120	6	%	%	NOUN
fcis-2971	120	7	of	of	ADP
fcis-2971	120	8	k	k	PROPN
fcis-2971	120	9	k	k	PROPN
fcis-2971	120	10	k	k	PROPN
fcis-2971	120	11	79	79	NUM
fcis-2971	120	12	nslkdd	nslkdd	ADJ
fcis-2971	120	13	data	datum	NOUN
fcis-2971	120	14	set	set	VERB
fcis-2971	120	15	is	be	AUX
fcis-2971	120	16	used	use	VERB
fcis-2971	120	17	,	,	PUNCT
fcis-2971	120	18	of	of	ADP
fcis-2971	120	19	which	which	PRON
fcis-2971	120	20	15	15	NUM
fcis-2971	120	21	%	%	NOUN
fcis-2971	120	22	is	be	AUX
fcis-2971	120	23	used	use	VERB
fcis-2971	120	24	as	as	ADP
fcis-2971	120	25	the	the	DET
fcis-2971	120	26	training	training	NOUN
fcis-2971	120	27	set	set	NOUN
fcis-2971	120	28	and	and	CCONJ
fcis-2971	120	29	the	the	DET
fcis-2971	120	30	other	other	ADJ
fcis-2971	120	31	15	15	NUM
fcis-2971	120	32	%	%	NOUN
fcis-2971	120	33	as	as	ADP
fcis-2971	120	34	the	the	DET
fcis-2971	120	35	test	test	NOUN
fcis-2971	120	36	set	set	VERB
fcis-2971	120	37	.	.	PUNCT
fcis-2971	121	1	in	in	ADP
fcis-2971	121	2	this	this	DET
fcis-2971	121	3	paper	paper	NOUN
fcis-2971	121	4	,	,	PUNCT
fcis-2971	121	5	cart	cart	NOUN
fcis-2971	121	6	decision	decision	NOUN
fcis-2971	121	7	tree	tree	NOUN
fcis-2971	121	8	based	base	VERB
fcis-2971	121	9	on	on	ADP
fcis-2971	121	10	gini	gini	PROPN
fcis-2971	121	11	index	index	NOUN
fcis-2971	121	12	is	be	AUX
fcis-2971	121	13	used	use	VERB
fcis-2971	121	14	to	to	PART
fcis-2971	121	15	construct	construct	VERB
fcis-2971	121	16	multiple	multiple	ADJ
fcis-2971	121	17	random	random	ADJ
fcis-2971	121	18	forests	forest	NOUN
fcis-2971	121	19	with	with	ADP
fcis-2971	121	20	forest	forest	NOUN
fcis-2971	121	21	density	density	NOUN
fcis-2971	121	22	ranging	range	VERB
fcis-2971	121	23	from	from	ADP
fcis-2971	121	24	1	1	NUM
fcis-2971	121	25	to	to	PART
fcis-2971	121	26	20	20	NUM
fcis-2971	121	27	.	.	PUNCT
fcis-2971	122	1	the	the	DET
fcis-2971	122	2	experimental	experimental	ADJ
fcis-2971	122	3	results	result	NOUN
fcis-2971	122	4	show	show	VERB
fcis-2971	122	5	that	that	SCONJ
fcis-2971	122	6	,	,	PUNCT
fcis-2971	122	7	with	with	ADP
fcis-2971	122	8	the	the	DET
fcis-2971	122	9	increase	increase	NOUN
fcis-2971	122	10	of	of	ADP
fcis-2971	122	11	the	the	DET
fcis-2971	122	12	number	number	NOUN
fcis-2971	122	13	of	of	ADP
fcis-2971	122	14	decision	decision	NOUN
fcis-2971	122	15	trees	tree	NOUN
fcis-2971	122	16	in	in	ADP
fcis-2971	122	17	the	the	DET
fcis-2971	122	18	random	random	ADJ
fcis-2971	122	19	forest	forest	NOUN
fcis-2971	122	20	,	,	PUNCT
fcis-2971	122	21	the	the	DET
fcis-2971	122	22	growth	growth	NOUN
fcis-2971	122	23	rate	rate	NOUN
fcis-2971	122	24	of	of	ADP
fcis-2971	122	25	experimental	experimental	ADJ
fcis-2971	122	26	accuracy	accuracy	NOUN
fcis-2971	122	27	is	be	AUX
fcis-2971	122	28	decreasing	decrease	VERB
fcis-2971	122	29	.	.	PUNCT
fcis-2971	123	1	when	when	SCONJ
fcis-2971	123	2	the	the	DET
fcis-2971	123	3	number	number	NOUN
fcis-2971	123	4	of	of	ADP
fcis-2971	123	5	decision	decision	NOUN
fcis-2971	123	6	trees	tree	NOUN
fcis-2971	123	7	exceeds	exceed	VERB
fcis-2971	123	8	20	20	NUM
fcis-2971	123	9	,	,	PUNCT
fcis-2971	123	10	the	the	DET
fcis-2971	123	11	growth	growth	NOUN
fcis-2971	123	12	tends	tend	VERB
fcis-2971	123	13	to	to	PART
fcis-2971	123	14	be	be	AUX
fcis-2971	123	15	flat	flat	ADJ
fcis-2971	123	16	slow	slow	ADJ
fcis-2971	123	17	.	.	PUNCT
fcis-2971	124	1	therefore	therefore	ADV
fcis-2971	124	2	,	,	PUNCT
fcis-2971	124	3	this	this	DET
fcis-2971	124	4	paper	paper	NOUN
fcis-2971	124	5	divides	divide	VERB
fcis-2971	124	6	the	the	DET
fcis-2971	124	7	[	[	X
fcis-2971	124	8	1,20	1,20	NUM
fcis-2971	124	9	]	]	X
fcis-2971	124	10	interval	interval	NOUN
fcis-2971	124	11	and	and	CCONJ
fcis-2971	124	12	lists	list	VERB
fcis-2971	124	13	the	the	DET
fcis-2971	124	14	accuracy	accuracy	NOUN
fcis-2971	124	15	of	of	ADP
fcis-2971	124	16	using	use	VERB
fcis-2971	124	17	pca	pca	PROPN
fcis-2971	124	18	to	to	PART
fcis-2971	124	19	reduce	reduce	VERB
fcis-2971	124	20	data	datum	NOUN
fcis-2971	124	21	to	to	ADP
fcis-2971	124	22	different	different	ADJ
fcis-2971	124	23	dimensions	dimension	NOUN
fcis-2971	124	24	(	(	PUNCT
fcis-2971	124	25	5	5	NUM
fcis-2971	124	26	dimensions	dimension	NOUN
fcis-2971	124	27	,	,	PUNCT
fcis-2971	124	28	10	10	NUM
fcis-2971	124	29	dimensions	dimension	NOUN
fcis-2971	124	30	,	,	PUNCT
fcis-2971	124	31	20	20	NUM
fcis-2971	124	32	dimensions	dimension	NOUN
fcis-2971	124	33	)	)	PUNCT
fcis-2971	124	34	when	when	SCONJ
fcis-2971	124	35	the	the	DET
fcis-2971	124	36	number	number	NOUN
fcis-2971	124	37	of	of	ADP
fcis-2971	124	38	decision	decision	NOUN
fcis-2971	124	39	trees	tree	NOUN
fcis-2971	124	40	in	in	ADP
fcis-2971	124	41	the	the	DET
fcis-2971	124	42	random	random	ADJ
fcis-2971	124	43	forest	forest	NOUN
fcis-2971	124	44	is	be	AUX
fcis-2971	124	45	5	5	NUM
fcis-2971	124	46	,	,	PUNCT
fcis-2971	124	47	10	10	NUM
fcis-2971	124	48	,	,	PUNCT
fcis-2971	124	49	20	20	NUM
fcis-2971	124	50	and	and	CCONJ
fcis-2971	124	51	more	more	ADJ
fcis-2971	124	52	.	.	PUNCT
fcis-2971	125	1	the	the	DET
fcis-2971	125	2	data	data	NOUN
fcis-2971	125	3	is	be	AUX
fcis-2971	125	4	divided	divide	VERB
fcis-2971	125	5	into	into	ADP
fcis-2971	125	6	5	5	NUM
fcis-2971	125	7	categories	category	NOUN
fcis-2971	125	8	(	(	PUNCT
fcis-2971	125	9	normal	normal	ADJ
fcis-2971	125	10	,	,	PUNCT
fcis-2971	125	11	dos	do	NOUN
fcis-2971	125	12	,	,	PUNCT
fcis-2971	125	13	u2r	u2r	NOUN
fcis-2971	125	14	,	,	PUNCT
fcis-2971	125	15	r2l	r2l	NOUN
fcis-2971	125	16	,	,	PUNCT
fcis-2971	125	17	probe	probe	NOUN
fcis-2971	125	18	)	)	PUNCT
fcis-2971	125	19	and	and	CCONJ
fcis-2971	125	20	2	2	NUM
fcis-2971	125	21	categories	category	NOUN
fcis-2971	125	22	(	(	PUNCT
fcis-2971	125	23	normal	normal	ADJ
fcis-2971	125	24	,	,	PUNCT
fcis-2971	125	25	abnormal	abnormal	ADJ
fcis-2971	125	26	)	)	PUNCT
fcis-2971	125	27	,	,	PUNCT
fcis-2971	125	28	that	that	ADV
fcis-2971	125	29	is	is	ADV
fcis-2971	125	30	,	,	PUNCT
fcis-2971	125	31	the	the	DET
fcis-2971	125	32	classification	classification	NOUN
fcis-2971	125	33	accuracy	accuracy	NOUN
fcis-2971	125	34	without	without	ADP
fcis-2971	125	35	pca	pca	NOUN
fcis-2971	125	36	preprocessing	preprocessing	NOUN
fcis-2971	125	37	is	be	AUX
fcis-2971	125	38	compared	compare	VERB
fcis-2971	125	39	when	when	SCONJ
fcis-2971	125	40	there	there	PRON
fcis-2971	125	41	is	be	VERB
fcis-2971	125	42	only	only	ADV
fcis-2971	125	43	a	a	DET
fcis-2971	125	44	single	single	ADJ
fcis-2971	125	45	decision	decision	NOUN
fcis-2971	125	46	tree	tree	NOUN
fcis-2971	125	47	.	.	PUNCT
fcis-2971	126	1	the	the	DET
fcis-2971	126	2	comparison	comparison	NOUN
fcis-2971	126	3	is	be	AUX
fcis-2971	126	4	shown	show	VERB
fcis-2971	126	5	in	in	ADP
fcis-2971	126	6	table	table	NOUN
fcis-2971	126	7	1	1	NUM
fcis-2971	126	8	and	and	CCONJ
fcis-2971	126	9	table	table	NOUN
fcis-2971	126	10	2	2	NUM
fcis-2971	126	11	.	.	PUNCT
fcis-2971	126	12	table	table	NOUN
fcis-2971	126	13	1	1	NUM
fcis-2971	126	14	.	.	PUNCT
fcis-2971	126	15	classification	classification	NOUN
fcis-2971	126	16	accuracy	accuracy	NOUN
fcis-2971	126	17	when	when	SCONJ
fcis-2971	126	18	data	datum	NOUN
fcis-2971	126	19	is	be	AUX
fcis-2971	126	20	divided	divide	VERB
fcis-2971	126	21	into	into	ADP
fcis-2971	126	22	5	5	NUM
fcis-2971	126	23	categories	category	NOUN
fcis-2971	126	24	number	number	NOUN
fcis-2971	126	25	accuracy	accuracy	NOUN
fcis-2971	127	1	k	k	NOUN
fcis-2971	127	2	1	1	NUM
fcis-2971	127	3	5	5	NUM
fcis-2971	127	4	10	10	NUM
fcis-2971	127	5	20	20	NUM
fcis-2971	127	6	20	20	NUM
fcis-2971	127	7	+	+	NUM
fcis-2971	127	8	5	5	NUM
fcis-2971	127	9	85.1	85.1	NUM
fcis-2971	127	10	86.7	86.7	NUM
fcis-2971	127	11	88.4	88.4	NUM
fcis-2971	127	12	89.3	89.3	NUM
fcis-2971	127	13	89.4	89.4	NUM
fcis-2971	127	14	10	10	NUM
fcis-2971	127	15	85.6	85.6	NUM
fcis-2971	127	16	88.5	88.5	NUM
fcis-2971	127	17	91.1	91.1	NUM
fcis-2971	127	18	90.4	90.4	NUM
fcis-2971	127	19	90.3	90.3	NUM
fcis-2971	127	20	20	20	NUM
fcis-2971	127	21	86.4	86.4	NUM
fcis-2971	127	22	89.6	89.6	NUM
fcis-2971	127	23	89.4	89.4	NUM
fcis-2971	127	24	88.9	88.9	NUM
fcis-2971	127	25	90.2	90.2	NUM
fcis-2971	127	26	without	without	ADP
fcis-2971	127	27	pca	pca	PROPN
fcis-2971	127	28	79.43	79.43	NUM
fcis-2971	127	29	80.2	80.2	NUM
fcis-2971	127	30	79.1	79.1	NUM
fcis-2971	127	31	79.5	79.5	NUM
fcis-2971	127	32	79.6	79.6	NUM
fcis-2971	127	33	table	table	NOUN
fcis-2971	127	34	2	2	NUM
fcis-2971	127	35	.	.	PUNCT
fcis-2971	127	36	classification	classification	NOUN
fcis-2971	127	37	accuracy	accuracy	NOUN
fcis-2971	127	38	when	when	SCONJ
fcis-2971	127	39	data	datum	NOUN
fcis-2971	127	40	is	be	AUX
fcis-2971	127	41	divided	divide	VERB
fcis-2971	127	42	into	into	ADP
fcis-2971	127	43	2	2	NUM
fcis-2971	127	44	categories	category	NOUN
fcis-2971	127	45	number	number	NOUN
fcis-2971	127	46	accuracy	accuracy	NOUN
fcis-2971	127	47	k	k	NOUN
fcis-2971	127	48	1	1	NUM
fcis-2971	127	49	5	5	NUM
fcis-2971	127	50	10	10	NUM
fcis-2971	127	51	20	20	NUM
fcis-2971	127	52	20	20	NUM
fcis-2971	127	53	+	+	NUM
fcis-2971	127	54	5	5	NUM
fcis-2971	127	55	94.7	94.7	NUM
fcis-2971	127	56	97.5	97.5	NUM
fcis-2971	127	57	98.1	98.1	NUM
fcis-2971	127	58	98.3	98.3	NUM
fcis-2971	127	59	98.2	98.2	NUM
fcis-2971	127	60	10	10	NUM
fcis-2971	127	61	95.7	95.7	NUM
fcis-2971	127	62	97.5	97.5	NUM
fcis-2971	127	63	98.6	98.6	NUM
fcis-2971	127	64	98.5	98.5	NUM
fcis-2971	127	65	98.4	98.4	NUM
fcis-2971	127	66	without	without	ADP
fcis-2971	127	67	pca	pca	PROPN
fcis-2971	127	68	94.6	94.6	NUM
fcis-2971	127	69	96.8	96.8	NUM
fcis-2971	127	70	97.1	97.1	NUM
fcis-2971	127	71	97.5	97.5	NUM
fcis-2971	127	72	97.9	97.9	NUM
fcis-2971	127	73	it	it	PRON
fcis-2971	127	74	can	can	AUX
fcis-2971	127	75	be	be	AUX
fcis-2971	127	76	seen	see	VERB
fcis-2971	127	77	from	from	ADP
fcis-2971	127	78	table	table	NOUN
fcis-2971	127	79	1	1	NUM
fcis-2971	127	80	and	and	CCONJ
fcis-2971	127	81	table	table	NOUN
fcis-2971	127	82	2	2	NUM
fcis-2971	127	83	that	that	PRON
fcis-2971	127	84	for	for	ADP
fcis-2971	127	85	the	the	DET
fcis-2971	127	86	nsl_kdd	nsl_kdd	NUM
fcis-2971	127	87	dataset	dataset	NOUN
fcis-2971	127	88	used	use	VERB
fcis-2971	127	89	in	in	ADP
fcis-2971	127	90	the	the	DET
fcis-2971	127	91	experiment	experiment	NOUN
fcis-2971	127	92	,	,	PUNCT
fcis-2971	127	93	the	the	DET
fcis-2971	127	94	increase	increase	NOUN
fcis-2971	127	95	in	in	ADP
fcis-2971	127	96	the	the	DET
fcis-2971	127	97	number	number	NOUN
fcis-2971	127	98	of	of	ADP
fcis-2971	127	99	decision	decision	NOUN
fcis-2971	127	100	trees	tree	NOUN
fcis-2971	127	101	in	in	ADP
fcis-2971	127	102	the	the	DET
fcis-2971	127	103	random	random	ADJ
fcis-2971	127	104	forest	forest	NOUN
fcis-2971	127	105	has	have	VERB
fcis-2971	127	106	no	no	DET
fcis-2971	127	107	significant	significant	ADJ
fcis-2971	127	108	impact	impact	NOUN
fcis-2971	127	109	on	on	ADP
fcis-2971	127	110	the	the	DET
fcis-2971	127	111	accuracy	accuracy	NOUN
fcis-2971	127	112	.	.	PUNCT
fcis-2971	128	1	the	the	DET
fcis-2971	128	2	classification	classification	NOUN
fcis-2971	128	3	accuracy	accuracy	NOUN
fcis-2971	128	4	rate	rate	NOUN
fcis-2971	128	5	when	when	SCONJ
fcis-2971	128	6	the	the	DET
fcis-2971	128	7	data	data	NOUN
fcis-2971	128	8	is	be	AUX
fcis-2971	128	9	divided	divide	VERB
fcis-2971	128	10	into	into	ADP
fcis-2971	128	11	two	two	NUM
fcis-2971	128	12	categories	category	NOUN
fcis-2971	128	13	is	be	AUX
fcis-2971	128	14	significantly	significantly	ADV
fcis-2971	128	15	higher	high	ADJ
fcis-2971	128	16	than	than	ADP
fcis-2971	128	17	that	that	PRON
fcis-2971	128	18	when	when	SCONJ
fcis-2971	128	19	the	the	DET
fcis-2971	128	20	data	data	NOUN
fcis-2971	128	21	is	be	AUX
fcis-2971	128	22	divided	divide	VERB
fcis-2971	128	23	into	into	ADP
fcis-2971	128	24	five	five	NUM
fcis-2971	128	25	categories	category	NOUN
fcis-2971	128	26	.	.	PUNCT
fcis-2971	129	1	however	however	ADV
fcis-2971	129	2	,	,	PUNCT
fcis-2971	129	3	no	no	ADV
fcis-2971	129	4	matter	matter	ADV
fcis-2971	129	5	whether	whether	SCONJ
fcis-2971	129	6	the	the	DET
fcis-2971	129	7	data	datum	NOUN
fcis-2971	129	8	is	be	AUX
fcis-2971	129	9	divided	divide	VERB
fcis-2971	129	10	into	into	ADP
fcis-2971	129	11	5	5	NUM
fcis-2971	129	12	or	or	CCONJ
fcis-2971	129	13	2	2	NUM
fcis-2971	129	14	categories	category	NOUN
fcis-2971	129	15	,	,	PUNCT
fcis-2971	129	16	the	the	DET
fcis-2971	129	17	classification	classification	NOUN
fcis-2971	129	18	accuracy	accuracy	NOUN
fcis-2971	129	19	of	of	ADP
fcis-2971	129	20	this	this	DET
fcis-2971	129	21	method	method	NOUN
fcis-2971	129	22	is	be	AUX
fcis-2971	129	23	higher	high	ADJ
fcis-2971	129	24	than	than	ADP
fcis-2971	129	25	that	that	PRON
fcis-2971	129	26	of	of	ADP
fcis-2971	129	27	the	the	DET
fcis-2971	129	28	decision	decision	NOUN
fcis-2971	129	29	tree	tree	NOUN
fcis-2971	129	30	algorithm	algorithm	NOUN
fcis-2971	129	31	.	.	PUNCT
fcis-2971	130	1	when	when	SCONJ
fcis-2971	130	2	the	the	DET
fcis-2971	130	3	data	data	NOUN
fcis-2971	130	4	is	be	AUX
fcis-2971	130	5	divided	divide	VERB
fcis-2971	130	6	into	into	ADP
fcis-2971	130	7	five	five	NUM
fcis-2971	130	8	categories	category	NOUN
fcis-2971	130	9	,	,	PUNCT
fcis-2971	130	10	the	the	DET
fcis-2971	130	11	accuracy	accuracy	NOUN
fcis-2971	130	12	can	can	AUX
fcis-2971	130	13	be	be	AUX
fcis-2971	130	14	effectively	effectively	ADV
fcis-2971	130	15	improved	improve	VERB
fcis-2971	130	16	by	by	ADP
fcis-2971	130	17	using	use	VERB
fcis-2971	130	18	pca	pca	PROPN
fcis-2971	130	19	to	to	PART
fcis-2971	130	20	reduce	reduce	VERB
fcis-2971	130	21	the	the	DET
fcis-2971	130	22	dimensions	dimension	NOUN
fcis-2971	130	23	of	of	ADP
fcis-2971	130	24	the	the	DET
fcis-2971	130	25	data	datum	NOUN
fcis-2971	130	26	and	and	CCONJ
fcis-2971	130	27	then	then	ADV
fcis-2971	130	28	classify	classify	VERB
fcis-2971	130	29	them	they	PRON
fcis-2971	130	30	.	.	PUNCT
fcis-2971	131	1	when	when	SCONJ
fcis-2971	131	2	the	the	DET
fcis-2971	131	3	data	data	NOUN
fcis-2971	131	4	is	be	AUX
fcis-2971	131	5	divided	divide	VERB
fcis-2971	131	6	into	into	ADP
fcis-2971	131	7	two	two	NUM
fcis-2971	131	8	categories	category	NOUN
fcis-2971	131	9	,	,	PUNCT
fcis-2971	131	10	pca	pca	NOUN
fcis-2971	131	11	dimension	dimension	NOUN
fcis-2971	131	12	reduction	reduction	NOUN
fcis-2971	131	13	can	can	AUX
fcis-2971	131	14	improve	improve	VERB
fcis-2971	131	15	the	the	DET
fcis-2971	131	16	accuracy	accuracy	NOUN
fcis-2971	131	17	of	of	ADP
fcis-2971	131	18	classification	classification	NOUN
fcis-2971	131	19	to	to	ADP
fcis-2971	131	20	a	a	DET
fcis-2971	131	21	certain	certain	ADJ
fcis-2971	131	22	extent	extent	NOUN
fcis-2971	131	23	,	,	PUNCT
fcis-2971	131	24	but	but	CCONJ
fcis-2971	131	25	it	it	PRON
fcis-2971	131	26	is	be	AUX
fcis-2971	131	27	not	not	PART
fcis-2971	131	28	as	as	ADV
fcis-2971	131	29	obvious	obvious	ADJ
fcis-2971	131	30	as	as	ADP
fcis-2971	131	31	when	when	SCONJ
fcis-2971	131	32	the	the	DET
fcis-2971	131	33	data	data	NOUN
fcis-2971	131	34	is	be	AUX
fcis-2971	131	35	divided	divide	VERB
fcis-2971	131	36	into	into	ADP
fcis-2971	131	37	five	five	NUM
fcis-2971	131	38	categories	category	NOUN
fcis-2971	131	39	.	.	PUNCT
fcis-2971	132	1	since	since	SCONJ
fcis-2971	132	2	the	the	DET
fcis-2971	132	3	accuracy	accuracy	NOUN
fcis-2971	132	4	of	of	ADP
fcis-2971	132	5	pca	pca	NOUN
fcis-2971	132	6	is	be	AUX
fcis-2971	132	7	not	not	PART
fcis-2971	132	8	different	different	ADJ
fcis-2971	132	9	when	when	SCONJ
fcis-2971	132	10	the	the	DET
fcis-2971	132	11	data	data	NOUN
fcis-2971	132	12	is	be	AUX
fcis-2971	132	13	reduced	reduce	VERB
fcis-2971	132	14	to	to	ADP
fcis-2971	132	15	5	5	NUM
fcis-2971	132	16	and	and	CCONJ
fcis-2971	132	17	10	10	NUM
fcis-2971	132	18	dimensions	dimension	NOUN
fcis-2971	132	19	when	when	SCONJ
fcis-2971	132	20	the	the	DET
fcis-2971	132	21	data	data	NOUN
fcis-2971	132	22	is	be	AUX
fcis-2971	132	23	divided	divide	VERB
fcis-2971	132	24	into	into	ADP
fcis-2971	132	25	2	2	NUM
fcis-2971	132	26	categories	category	NOUN
fcis-2971	132	27	,	,	PUNCT
fcis-2971	132	28	the	the	DET
fcis-2971	132	29	test	test	NOUN
fcis-2971	132	30	is	be	AUX
fcis-2971	132	31	not	not	PART
fcis-2971	132	32	continued	continue	VERB
fcis-2971	132	33	when	when	SCONJ
fcis-2971	132	34	the	the	DET
fcis-2971	132	35	data	data	NOUN
fcis-2971	132	36	is	be	AUX
fcis-2971	132	37	reduced	reduce	VERB
fcis-2971	132	38	to	to	ADP
fcis-2971	132	39	20	20	NUM
fcis-2971	132	40	dimensions	dimension	NOUN
fcis-2971	132	41	.	.	PUNCT
fcis-2971	133	1	this	this	DET
fcis-2971	133	2	paper	paper	NOUN
fcis-2971	133	3	also	also	ADV
fcis-2971	133	4	tested	test	VERB
fcis-2971	133	5	the	the	DET
fcis-2971	133	6	accuracy	accuracy	NOUN
fcis-2971	133	7	of	of	ADP
fcis-2971	133	8	several	several	ADJ
fcis-2971	133	9	commonly	commonly	ADV
fcis-2971	133	10	used	use	VERB
fcis-2971	133	11	machine	machine	NOUN
fcis-2971	133	12	learning	learn	VERB
fcis-2971	133	13	classification	classification	NOUN
fcis-2971	133	14	methods	method	NOUN
fcis-2971	133	15	when	when	SCONJ
fcis-2971	133	16	dividing	divide	VERB
fcis-2971	133	17	the	the	DET
fcis-2971	133	18	experimental	experimental	ADJ
fcis-2971	133	19	data	datum	NOUN
fcis-2971	133	20	into	into	ADP
fcis-2971	133	21	two	two	NUM
fcis-2971	133	22	categories	category	NOUN
fcis-2971	133	23	and	and	CCONJ
fcis-2971	133	24	five	five	NUM
fcis-2971	133	25	categories	category	NOUN
fcis-2971	133	26	under	under	ADP
fcis-2971	133	27	the	the	DET
fcis-2971	133	28	same	same	ADJ
fcis-2971	133	29	data	datum	NOUN
fcis-2971	133	30	set	set	VERB
fcis-2971	133	31	,	,	PUNCT
fcis-2971	133	32	as	as	SCONJ
fcis-2971	133	33	shown	show	VERB
fcis-2971	133	34	in	in	ADP
fcis-2971	133	35	table	table	NOUN
fcis-2971	133	36	3	3	NUM
fcis-2971	133	37	.	.	PUNCT
fcis-2971	134	1	it	it	PRON
fcis-2971	134	2	can	can	AUX
fcis-2971	134	3	be	be	AUX
fcis-2971	134	4	seen	see	VERB
fcis-2971	134	5	that	that	SCONJ
fcis-2971	134	6	the	the	DET
fcis-2971	134	7	best	good	ADJ
fcis-2971	134	8	performance	performance	NOUN
fcis-2971	134	9	is	be	AUX
fcis-2971	134	10	the	the	DET
fcis-2971	134	11	svm	svm	ADJ
fcis-2971	134	12	method	method	NOUN
fcis-2971	134	13	using	use	VERB
fcis-2971	134	14	the	the	DET
fcis-2971	134	15	kernel	kernel	PROPN
fcis-2971	134	16	function	function	NOUN
fcis-2971	134	17	,	,	PUNCT
fcis-2971	134	18	with	with	ADP
fcis-2971	134	19	the	the	DET
fcis-2971	134	20	accuracy	accuracy	NOUN
fcis-2971	134	21	rate	rate	NOUN
fcis-2971	134	22	reaching	reach	VERB
fcis-2971	134	23	90	90	NUM
fcis-2971	134	24	%	%	NOUN
fcis-2971	134	25	,	,	PUNCT
fcis-2971	134	26	and	and	CCONJ
fcis-2971	134	27	the	the	DET
fcis-2971	134	28	accuracy	accuracy	NOUN
fcis-2971	134	29	rate	rate	NOUN
fcis-2971	134	30	of	of	ADP
fcis-2971	134	31	the	the	DET
fcis-2971	134	32	other	other	ADJ
fcis-2971	134	33	classification	classification	NOUN
fcis-2971	134	34	methods	method	NOUN
fcis-2971	134	35	is	be	AUX
fcis-2971	134	36	only	only	ADV
fcis-2971	134	37	about	about	ADV
fcis-2971	134	38	80	80	NUM
fcis-2971	134	39	%	%	NOUN
fcis-2971	134	40	.	.	PUNCT
fcis-2971	135	1	table	table	NOUN
fcis-2971	135	2	3	3	NUM
fcis-2971	135	3	.	.	PUNCT
fcis-2971	136	1	experimental	experimental	ADJ
fcis-2971	136	2	results	result	NOUN
fcis-2971	136	3	of	of	ADP
fcis-2971	136	4	some	some	DET
fcis-2971	136	5	common	common	ADJ
fcis-2971	136	6	classification	classification	NOUN
fcis-2971	136	7	methods	method	NOUN
fcis-2971	136	8	in	in	ADP
fcis-2971	136	9	the	the	DET
fcis-2971	136	10	same	same	ADJ
fcis-2971	136	11	dataset	dataset	NOUN
fcis-2971	136	12	classification	classification	NOUN
fcis-2971	136	13	type	type	NOUN
fcis-2971	136	14	method	method	NOUN
fcis-2971	136	15	2	2	NUM
fcis-2971	136	16	categories	category	NOUN
fcis-2971	136	17	5	5	NUM
fcis-2971	136	18	categories	category	NOUN
fcis-2971	136	19	svm	svm	VERB
fcis-2971	136	20	82.5	82.5	NUM
fcis-2971	136	21	78.7	78.7	NUM
fcis-2971	136	22	svm	svm	NOUN
fcis-2971	136	23	(	(	PUNCT
fcis-2971	136	24	kernel	kernel	PROPN
fcis-2971	136	25	function	function	PROPN
fcis-2971	136	26	)	)	PUNCT
fcis-2971	136	27	90.8	90.8	NUM
fcis-2971	136	28	86.3	86.3	NUM
fcis-2971	136	29	naive	naive	ADJ
fcis-2971	136	30	bayesian	bayesian	NOUN
fcis-2971	136	31	80.3	80.3	NUM
fcis-2971	136	32	77.4	77.4	NUM
fcis-2971	136	33	logistic	logistic	ADJ
fcis-2971	136	34	regression	regression	NOUN
fcis-2971	136	35	78.6	78.6	NUM
fcis-2971	136	36	74.9	74.9	NUM
fcis-2971	136	37	4	4	NUM
fcis-2971	136	38	.	.	PUNCT
fcis-2971	137	1	conclusion	conclusion	NOUN
fcis-2971	137	2	this	this	DET
fcis-2971	137	3	paper	paper	NOUN
fcis-2971	137	4	proposes	propose	VERB
fcis-2971	137	5	an	an	DET
fcis-2971	137	6	intrusion	intrusion	NOUN
fcis-2971	137	7	detection	detection	NOUN
fcis-2971	137	8	method	method	NOUN
fcis-2971	137	9	based	base	VERB
fcis-2971	137	10	on	on	ADP
fcis-2971	137	11	pca	pca	PROPN
fcis-2971	137	12	and	and	CCONJ
fcis-2971	137	13	random	random	ADJ
fcis-2971	137	14	forest	forest	NOUN
fcis-2971	137	15	classification	classification	NOUN
fcis-2971	137	16	by	by	ADP
fcis-2971	137	17	using	use	VERB
fcis-2971	137	18	the	the	DET
fcis-2971	137	19	idea	idea	NOUN
fcis-2971	137	20	of	of	ADP
fcis-2971	137	21	data	datum	NOUN
fcis-2971	137	22	cleaning	clean	VERB
fcis-2971	137	23	before	before	ADP
fcis-2971	137	24	classification	classification	NOUN
fcis-2971	137	25	.	.	PUNCT
fcis-2971	138	1	firstly	firstly	ADV
fcis-2971	138	2	,	,	PUNCT
fcis-2971	138	3	pca	pca	PROPN
fcis-2971	138	4	is	be	AUX
fcis-2971	138	5	used	use	VERB
fcis-2971	138	6	to	to	PART
fcis-2971	138	7	reduce	reduce	VERB
fcis-2971	138	8	the	the	DET
fcis-2971	138	9	feature	feature	NOUN
fcis-2971	138	10	dimension	dimension	NOUN
fcis-2971	138	11	of	of	ADP
fcis-2971	138	12	a	a	DET
fcis-2971	138	13	large	large	ADJ
fcis-2971	138	14	number	number	NOUN
fcis-2971	138	15	of	of	ADP
fcis-2971	138	16	data	datum	NOUN
fcis-2971	138	17	to	to	PART
fcis-2971	138	18	reduce	reduce	VERB
fcis-2971	138	19	the	the	DET
fcis-2971	138	20	amount	amount	NOUN
fcis-2971	138	21	of	of	ADP
fcis-2971	138	22	computation	computation	NOUN
fcis-2971	138	23	and	and	CCONJ
fcis-2971	138	24	remove	remove	VERB
fcis-2971	138	25	the	the	DET
fcis-2971	138	26	noise	noise	NOUN
fcis-2971	138	27	in	in	ADP
fcis-2971	138	28	the	the	DET
fcis-2971	138	29	data	datum	NOUN
fcis-2971	138	30	;	;	PUNCT
fcis-2971	138	31	then	then	ADV
fcis-2971	138	32	,	,	PUNCT
fcis-2971	138	33	the	the	DET
fcis-2971	138	34	reduced	reduced	ADJ
fcis-2971	138	35	dimension	dimension	NOUN
fcis-2971	138	36	data	datum	NOUN
fcis-2971	138	37	are	be	AUX
fcis-2971	138	38	trained	train	VERB
fcis-2971	138	39	using	use	VERB
fcis-2971	138	40	a	a	DET
fcis-2971	138	41	random	random	ADJ
fcis-2971	138	42	forest	forest	NOUN
fcis-2971	138	43	classifier	classifier	NOUN
fcis-2971	138	44	.	.	PUNCT
fcis-2971	139	1	the	the	DET
fcis-2971	139	2	experimental	experimental	ADJ
fcis-2971	139	3	results	result	NOUN
fcis-2971	139	4	show	show	VERB
fcis-2971	139	5	that	that	SCONJ
fcis-2971	139	6	the	the	DET
fcis-2971	139	7	intrusion	intrusion	NOUN
fcis-2971	139	8	detection	detection	NOUN
fcis-2971	139	9	method	method	NOUN
fcis-2971	139	10	proposed	propose	VERB
fcis-2971	139	11	in	in	ADP
fcis-2971	139	12	this	this	DET
fcis-2971	139	13	paper	paper	NOUN
fcis-2971	139	14	can	can	AUX
fcis-2971	139	15	effectively	effectively	ADV
fcis-2971	139	16	remove	remove	VERB
fcis-2971	139	17	the	the	DET
fcis-2971	139	18	noise	noise	NOUN
fcis-2971	139	19	in	in	ADP
fcis-2971	139	20	the	the	DET
fcis-2971	139	21	data	datum	NOUN
fcis-2971	139	22	and	and	CCONJ
fcis-2971	139	23	reduce	reduce	VERB
fcis-2971	139	24	the	the	DET
fcis-2971	139	25	calculation	calculation	NOUN
fcis-2971	139	26	of	of	ADP
fcis-2971	139	27	the	the	DET
fcis-2971	139	28	classifier	classifier	NOUN
fcis-2971	139	29	.	.	PUNCT
fcis-2971	140	1	compared	compare	VERB
fcis-2971	140	2	with	with	ADP
fcis-2971	140	3	the	the	DET
fcis-2971	140	4	common	common	ADJ
fcis-2971	140	5	classification	classification	NOUN
fcis-2971	140	6	methods	method	NOUN
fcis-2971	140	7	,	,	PUNCT
fcis-2971	140	8	it	it	PRON
fcis-2971	140	9	improves	improve	VERB
fcis-2971	140	10	the	the	DET
fcis-2971	140	11	accuracy	accuracy	NOUN
fcis-2971	140	12	of	of	ADP
fcis-2971	140	13	intrusion	intrusion	NOUN
fcis-2971	140	14	detection	detection	NOUN
fcis-2971	140	15	to	to	ADP
fcis-2971	140	16	a	a	DET
fcis-2971	140	17	certain	certain	ADJ
fcis-2971	140	18	extent	extent	NOUN
fcis-2971	140	19	.	.	PUNCT
fcis-2971	141	1	references	reference	NOUN
fcis-2971	141	2	[	[	X
fcis-2971	141	3	1	1	NUM
fcis-2971	141	4	]	]	X
fcis-2971	141	5	zhang	zhang	PROPN
fcis-2971	141	6	shuai	shuai	PROPN
fcis-2971	141	7	,	,	PUNCT
fcis-2971	141	8	di	di	NOUN
fcis-2971	141	9	shaojia	shaojia	NOUN
fcis-2971	141	10	.	.	PUNCT
fcis-2971	142	1	monitoring	monitor	VERB
fcis-2971	142	2	data	datum	NOUN
fcis-2971	142	3	of	of	ADP
fcis-2971	142	4	network	network	NOUN
fcis-2971	142	5	security	security	NOUN
fcis-2971	142	6	in	in	ADP
fcis-2971	142	7	september	september	PROPN
fcis-2971	142	8	2019	2019	NUM
fcis-2971	143	1	[	[	X
fcis-2971	143	2	j	j	X
fcis-2971	143	3	]	]	X
fcis-2971	143	4	.	.	PUNCT
fcis-2971	144	1	netinfo	netinfo	PROPN
fcis-2971	144	2	security	security	NOUN
fcis-2971	144	3	,	,	PUNCT
fcis-2971	144	4	2019	2019	NUM
fcis-2971	144	5	(	(	PUNCT
fcis-2971	144	6	11	11	NUM
fcis-2971	144	7	):	):	PUNCT
fcis-2971	144	8	93	93	NUM
fcis-2971	144	9	-	-	SYM
fcis-2971	144	10	94	94	NUM
fcis-2971	144	11	.	.	PUNCT
fcis-2971	145	1	[	[	X
fcis-2971	145	2	2	2	NUM
fcis-2971	145	3	]	]	X
fcis-2971	145	4	zhang	zhang	PROPN
fcis-2971	145	5	lei	lei	PROPN
fcis-2971	145	6	,	,	PUNCT
fcis-2971	145	7	cui	cui	PROPN
fcis-2971	145	8	yong	yong	PROPN
fcis-2971	145	9	,	,	PUNCT
fcis-2971	145	10	liu	liu	PROPN
fcis-2971	145	11	jing	jing	PROPN
fcis-2971	145	12	.	.	PUNCT
fcis-2971	145	13	application	application	NOUN
fcis-2971	145	14	of	of	ADP
fcis-2971	145	15	machine	machine	NOUN
fcis-2971	145	16	learning	learning	NOUN
fcis-2971	145	17	in	in	ADP
fcis-2971	145	18	cyberspace	cyberspace	ADJ
fcis-2971	145	19	security	security	NOUN
fcis-2971	145	20	research	research	NOUN
fcis-2971	145	21	[	[	X
fcis-2971	145	22	j	j	X
fcis-2971	145	23	]	]	X
fcis-2971	145	24	.	.	PUNCT
fcis-2971	146	1	chinese	chinese	ADJ
fcis-2971	146	2	journal	journal	PROPN
fcis-2971	146	3	of	of	ADP
fcis-2971	146	4	computers	computer	NOUN
fcis-2971	146	5	,	,	PUNCT
fcis-2971	146	6	2018	2018	NUM
fcis-2971	146	7	,	,	PUNCT
fcis-2971	146	8	41(09	41(09	NUM
fcis-2971	146	9	):	):	PUNCT
fcis-2971	146	10	1943	1943	NUM
fcis-2971	146	11	-	-	SYM
fcis-2971	146	12	1975	1975	NUM
fcis-2971	146	13	.	.	PUNCT
fcis-2971	147	1	[	[	X
fcis-2971	147	2	3	3	X
fcis-2971	147	3	]	]	X
fcis-2971	147	4	wang	wang	PROPN
fcis-2971	147	5	xiang	xiang	PROPN
fcis-2971	147	6	,	,	PUNCT
fcis-2971	147	7	hu	hu	PROPN
fcis-2971	147	8	xuegang	xuegang	PROPN
fcis-2971	147	9	,	,	PUNCT
fcis-2971	147	10	yang	yang	PROPN
fcis-2971	147	11	qiujie	qiujie	PROPN
fcis-2971	147	12	.	.	PUNCT
fcis-2971	148	1	research	research	NOUN
fcis-2971	148	2	on	on	ADP
fcis-2971	148	3	improved	improve	VERB
fcis-2971	148	4	intrusion	intrusion	NOUN
fcis-2971	148	5	detection	detection	NOUN
fcis-2971	148	6	model	model	NOUN
fcis-2971	148	7	with	with	ADP
fcis-2971	148	8	random	random	ADJ
fcis-2971	148	9	forest	forest	NOUN
fcis-2971	148	10	based	base	VERB
fcis-2971	148	11	on	on	ADP
fcis-2971	148	12	feature	feature	NOUN
fcis-2971	148	13	evaluation	evaluation	NOUN
fcis-2971	148	14	of	of	ADP
fcis-2971	148	15	one	one	NUM
fcis-2971	148	16	-	-	PUNCT
fcis-2971	148	17	r00	r00	NOUN
fcis-2971	149	1	[	[	X
fcis-2971	149	2	j	j	X
fcis-2971	149	3	]	]	X
fcis-2971	149	4	.	.	PUNCT
fcis-2971	150	1	journal	journal	PROPN
fcis-2971	150	2	of	of	ADP
fcis-2971	150	3	he	he	PROPN
fcis-2971	150	4	fei	fei	PROPN
fcis-2971	150	5	university	university	PROPN
fcis-2971	150	6	of	of	ADP
fcis-2971	150	7	technology	technology	PROPN
fcis-2971	150	8	(	(	PUNCT
fcis-2971	150	9	natural	natural	ADJ
fcis-2971	150	10	science	science	NOUN
fcis-2971	150	11	)	)	PUNCT
fcis-2971	150	12	,	,	PUNCT
fcis-2971	150	13	2015	2015	NUM
fcis-2971	150	14	,	,	PUNCT
fcis-2971	150	15	38	38	NUM
fcis-2971	150	16	(	(	PUNCT
fcis-2971	150	17	05	05	NUM
fcis-2971	150	18	):	):	PUNCT
fcis-2971	150	19	627	627	NUM
fcis-2971	150	20	-	-	SYM
fcis-2971	150	21	630	630	NUM
fcis-2971	150	22	,	,	PUNCT
fcis-2971	150	23	711	711	NUM
fcis-2971	150	24	.	.	PUNCT
fcis-2971	151	1	[	[	X
fcis-2971	151	2	4	4	X
fcis-2971	151	3	]	]	X
fcis-2971	151	4	ren	ren	PROPN
fcis-2971	151	5	jiadong	jiadong	PROPN
fcis-2971	151	6	,	,	PUNCT
fcis-2971	151	7	liu	liu	PROPN
fcis-2971	151	8	xinqian	xinqian	PROPN
fcis-2971	151	9	,	,	PUNCT
fcis-2971	151	10	wang	wang	PROPN
fcis-2971	151	11	qian	qian	PROPN
fcis-2971	151	12	.	.	PUNCT
fcis-2971	152	1	a	a	DET
fcis-2971	152	2	multi	multi	ADJ
fcis-2971	152	3	-	-	ADJ
fcis-2971	152	4	level	level	ADJ
fcis-2971	152	5	intrusion	intrusion	NOUN
fcis-2971	152	6	detection	detection	NOUN
fcis-2971	152	7	method	method	NOUN
fcis-2971	152	8	based	base	VERB
fcis-2971	152	9	on	on	ADP
fcis-2971	152	10	knn	knn	PROPN
fcis-2971	152	11	outlier	outlier	PROPN
fcis-2971	152	12	detection	detection	NOUN
fcis-2971	152	13	and	and	CCONJ
fcis-2971	152	14	random	random	ADJ
fcis-2971	152	15	forests	forest	NOUN
fcis-2971	152	16	[	[	X
fcis-2971	152	17	j	j	X
fcis-2971	152	18	]	]	X
fcis-2971	152	19	.	.	PUNCT
fcis-2971	153	1	journal	journal	PROPN
fcis-2971	153	2	of	of	ADP
fcis-2971	153	3	com⁃	com⁃	PROPN
fcis-2971	153	4	puter	puter	NOUN
fcis-2971	153	5	research	research	NOUN
fcis-2971	153	6	and	and	CCONJ
fcis-2971	153	7	development	development	NOUN
fcis-2971	153	8	,	,	PUNCT
fcis-2971	153	9	2019	2019	NUM
fcis-2971	153	10	,	,	PUNCT
fcis-2971	153	11	56(03	56(03	NUM
fcis-2971	153	12	):	):	PUNCT
fcis-2971	153	13	566	566	NUM
fcis-2971	153	14	-	-	SYM
fcis-2971	153	15	575	575	NUM
fcis-2971	153	16	.	.	PUNCT
fcis-2971	154	1	[	[	X
fcis-2971	154	2	5	5	X
fcis-2971	154	3	]	]	X
fcis-2971	154	4	liang	liang	PROPN
fcis-2971	154	5	c	c	PROPN
fcis-2971	154	6	,	,	PUNCT
fcis-2971	154	7	li	li	PROPN
fcis-2971	154	8	c	c	PROPN
fcis-2971	154	9	h	h	PROPN
fcis-2971	154	10	,	,	PUNCT
fcis-2971	154	11	zhou	zhou	PROPN
fcis-2971	154	12	l	l	PROPN
fcis-2971	154	13	e.	e.	PROPN
fcis-2971	155	1	a	a	DET
fcis-2971	155	2	pca	pca	PROPN
fcis-2971	155	3	-	-	PUNCT
fcis-2971	155	4	bp	bp	PROPN
fcis-2971	155	5	neural	neural	ADJ
fcis-2971	155	6	network	network	NOUN
fcis-2971	155	7	-	-	PUNCT
fcis-2971	155	8	based	base	VERB
fcis-2971	155	9	intrusion	intrusion	NOUN
fcis-2971	155	10	detection	detection	NOUN
fcis-2971	155	11	method	method	NOUN
fcis-2971	155	12	[	[	X
fcis-2971	155	13	j	j	X
fcis-2971	155	14	]	]	X
fcis-2971	155	15	.	.	PUNCT
fcis-2971	156	1	journal	journal	PROPN
fcis-2971	156	2	of	of	ADP
fcis-2971	156	3	air	air	PROPN
fcis-2971	156	4	engineering	engineering	PROPN
fcis-2971	156	5	university	university	PROPN
fcis-2971	156	6	(	(	PUNCT
fcis-2971	156	7	natural	natural	ADJ
fcis-2971	156	8	science	science	NOUN
fcis-2971	156	9	edition	edition	NOUN
fcis-2971	156	10	)	)	PUNCT
fcis-2971	156	11	,	,	PUNCT
fcis-2971	156	12	2016	2016	NUM
fcis-2971	156	13	,	,	PUNCT
fcis-2971	156	14	17(6	17(6	NUM
fcis-2971	156	15	):	):	PUNCT
fcis-2971	156	16	93	93	NUM
fcis-2971	156	17	-	-	SYM
fcis-2971	156	18	98	98	NUM
fcis-2971	156	19	.	.	PUNCT
fcis-2971	157	1	[	[	X
fcis-2971	157	2	6	6	NUM
fcis-2971	157	3	]	]	PUNCT
fcis-2971	157	4	guinde	guinde	NOUN
fcis-2971	157	5	n	n	PROPN
fcis-2971	157	6	b	b	NOUN
fcis-2971	157	7	,	,	PUNCT
fcis-2971	157	8	ziavras	ziavras	PROPN
fcis-2971	157	9	s	s	PART
fcis-2971	157	10	g.	g.	PROPN
fcis-2971	157	11	efficient	efficient	ADJ
fcis-2971	157	12	hardware	hardware	NOUN
fcis-2971	157	13	support	support	NOUN
fcis-2971	157	14	for	for	ADP
fcis-2971	157	15	pattern	pattern	NOUN
fcis-2971	157	16	matching	match	VERB
fcis-2971	157	17	in	in	ADP
fcis-2971	157	18	network	network	NOUN
fcis-2971	157	19	intrusion	intrusion	NOUN
fcis-2971	157	20	detection	detection	NOUN
fcis-2971	158	1	[	[	X
fcis-2971	158	2	j	j	X
fcis-2971	158	3	]	]	X
fcis-2971	158	4	.	.	PUNCT
fcis-2971	159	1	computers	computer	NOUN
fcis-2971	159	2	&	&	CCONJ
fcis-2971	159	3	security	security	NOUN
fcis-2971	159	4	,	,	PUNCT
fcis-2971	159	5	2010	2010	NUM
fcis-2971	159	6	,	,	PUNCT
fcis-2971	159	7	29(7	29(7	NUM
fcis-2971	159	8	):	):	PUNCT
fcis-2971	159	9	756	756	NUM
fcis-2971	159	10	-	-	SYM
fcis-2971	159	11	769	769	NUM
fcis-2971	159	12	.	.	PUNCT
fcis-2971	160	1	[	[	X
fcis-2971	160	2	7	7	X
fcis-2971	160	3	]	]	X
fcis-2971	160	4	quinlan	quinlan	PROPN
fcis-2971	160	5	j	j	PROPN
fcis-2971	160	6	r.	r.	PROPN
fcis-2971	160	7	induction	induction	NOUN
fcis-2971	160	8	of	of	ADP
fcis-2971	160	9	decision	decision	NOUN
fcis-2971	160	10	trees	tree	NOUN
fcis-2971	160	11	[	[	X
fcis-2971	160	12	j	j	X
fcis-2971	160	13	]	]	X
fcis-2971	160	14	.	.	PUNCT
fcis-2971	161	1	machine	machine	NOUN
fcis-2971	161	2	learning	learning	PROPN
fcis-2971	161	3	,	,	PUNCT
fcis-2971	161	4	1986	1986	NUM
fcis-2971	161	5	,	,	PUNCT
fcis-2971	161	6	1(1	1(1	NUM
fcis-2971	161	7	)	)	PUNCT
fcis-2971	161	8	,	,	PUNCT
fcis-2971	161	9	81	81	NUM
fcis-2971	161	10	-	-	SYM
fcis-2971	161	11	106	106	NUM
fcis-2971	161	12	.	.	PUNCT
fcis-2971	162	1	[	[	X
fcis-2971	162	2	8	8	NUM
fcis-2971	162	3	]	]	X
fcis-2971	162	4	quinlan	quinlan	PROPN
fcis-2971	162	5	,	,	PUNCT
fcis-2971	162	6	j.	j.	PROPN
fcis-2971	162	7	r.	r.	PROPN
fcis-2971	162	8	c4.5	c4.5	PROPN
fcis-2971	162	9	:	:	PUNCT
fcis-2971	162	10	programs	program	NOUN
fcis-2971	162	11	for	for	ADP
fcis-2971	162	12	machine	machine	NOUN
fcis-2971	162	13	learning	learn	VERB
fcis-2971	162	14	[	[	X
fcis-2971	162	15	m	m	X
fcis-2971	162	16	]	]	X
fcis-2971	162	17	.	.	PUNCT
fcis-2971	163	1	morgan	morgan	PROPN
fcis-2971	163	2	kaufmann	kaufmann	PROPN
fcis-2971	163	3	publishers	publishers	PROPN
fcis-2971	163	4	,	,	PUNCT
fcis-2971	163	5	1993	1993	NUM
fcis-2971	163	6	:	:	PUNCT
fcis-2971	163	7	1	1	NUM
fcis-2971	163	8	-	-	SYM
fcis-2971	163	9	12	12	NUM
fcis-2971	163	10	.	.	PUNCT
fcis-2971	164	1	[	[	X
fcis-2971	164	2	9	9	NUM
fcis-2971	164	3	]	]	X
fcis-2971	164	4	steinberg	steinberg	PROPN
fcis-2971	164	5	d.	d.	PROPN
fcis-2971	164	6	cart	cart	PROPN
fcis-2971	164	7	:	:	PUNCT
fcis-2971	164	8	classification	classification	NOUN
fcis-2971	164	9	and	and	CCONJ
fcis-2971	164	10	regression	regression	NOUN
fcis-2971	164	11	trees	tree	NOUN
fcis-2971	164	12	［m］.	［m］.	ADJ
fcis-2971	164	13	the	the	DET
fcis-2971	164	14	top	top	ADJ
fcis-2971	164	15	ten	ten	NUM
fcis-2971	164	16	algorithms	algorithm	NOUN
fcis-2971	164	17	in	in	ADP
fcis-2971	164	18	data	datum	NOUN
fcis-2971	164	19	mining	mining	NOUN
fcis-2971	164	20	.	.	PUNCT
fcis-2971	165	1	chapman	chapman	NOUN
fcis-2971	165	2	and	and	CCONJ
fcis-2971	165	3	hall	hall	PROPN
fcis-2971	165	4	/	/	SYM
fcis-2971	165	5	crc	crc	PROPN
fcis-2971	165	6	,	,	PUNCT
fcis-2971	165	7	2009:193	2009:193	NUM
fcis-2971	165	8	-	-	SYM
fcis-2971	165	9	216	216	NUM
fcis-2971	165	10	.	.	PUNCT