id	sid	tid	token	lemma	pos
ajst-14953	1	1	academic	academic	ADJ
ajst-14953	1	2	journal	journal	NOUN
ajst-14953	1	3	of	of	ADP
ajst-14953	1	4	science	science	NOUN
ajst-14953	1	5	and	and	CCONJ
ajst-14953	1	6	technology	technology	NOUN
ajst-14953	1	7	issn	issn	NOUN
ajst-14953	1	8	:	:	PUNCT
ajst-14953	1	9	2771	2771	NUM
ajst-14953	1	10	-	-	SYM
ajst-14953	1	11	3032	3032	NUM
ajst-14953	1	12	|	|	NOUN
ajst-14953	1	13	vol	vol	NOUN
ajst-14953	1	14	.	.	PROPN
ajst-14953	1	15	8	8	NUM
ajst-14953	1	16	,	,	PUNCT
ajst-14953	1	17	no	no	INTJ
ajst-14953	1	18	.	.	NOUN
ajst-14953	1	19	2	2	NUM
ajst-14953	1	20	,	,	PUNCT
ajst-14953	1	21	2023	2023	NUM
ajst-14953	1	22	81	81	NUM
ajst-14953	1	23	a	a	DET
ajst-14953	1	24	network	network	NOUN
ajst-14953	1	25	intrusion	intrusion	NOUN
ajst-14953	1	26	detection	detection	NOUN
ajst-14953	1	27	method	method	NOUN
ajst-14953	1	28	based	base	VERB
ajst-14953	1	29	on	on	ADP
ajst-14953	1	30	a	a	DET
ajst-14953	1	31	fast	fast	ADJ
ajst-14953	1	32	knn	knn	NOUN
ajst-14953	1	33	algorithm	algorithm	PROPN
ajst-14953	1	34	pengcheng	pengcheng	PROPN
ajst-14953	2	1	he	he	PRON
ajst-14953	2	2	,	,	PUNCT
ajst-14953	2	3	guifeng	guifeng	PROPN
ajst-14953	2	4	feng	feng	PROPN
ajst-14953	2	5	*	*	PROPN
ajst-14953	2	6	,	,	PUNCT
ajst-14953	2	7	huixu	huixu	PROPN
ajst-14953	2	8	li	li	PROPN
ajst-14953	2	9	,	,	PUNCT
ajst-14953	2	10	le	le	PROPN
ajst-14953	2	11	yang	yang	PROPN
ajst-14953	2	12	foshan	foshan	PROPN
ajst-14953	2	13	network	network	PROPN
ajst-14953	2	14	security	security	PROPN
ajst-14953	2	15	emergency	emergency	NOUN
ajst-14953	2	16	command	command	NOUN
ajst-14953	2	17	center	center	NOUN
ajst-14953	2	18	,	,	PUNCT
ajst-14953	2	19	foshan	foshan	PROPN
ajst-14953	2	20	,	,	PUNCT
ajst-14953	2	21	528000	528000	NUM
ajst-14953	2	22	,	,	PUNCT
ajst-14953	2	23	china	china	PROPN
ajst-14953	2	24	*	*	PUNCT
ajst-14953	2	25	corresponding	correspond	VERB
ajst-14953	2	26	author	author	NOUN
ajst-14953	2	27	:	:	PUNCT
ajst-14953	2	28	guifeng	guifeng	PROPN
ajst-14953	2	29	feng	feng	PROPN
ajst-14953	2	30	(	(	PUNCT
ajst-14953	2	31	email	email	NOUN
ajst-14953	2	32	:	:	PUNCT
ajst-14953	2	33	1215458536	1215458536	NUM
ajst-14953	2	34	@qq.com	@qq.com	NUM
ajst-14953	2	35	)	)	PUNCT
ajst-14953	2	36	abstract	abstract	NOUN
ajst-14953	2	37	:	:	PUNCT
ajst-14953	2	38	this	this	DET
ajst-14953	2	39	paper	paper	NOUN
ajst-14953	2	40	proposes	propose	VERB
ajst-14953	2	41	a	a	DET
ajst-14953	2	42	network	network	NOUN
ajst-14953	2	43	intrusion	intrusion	NOUN
ajst-14953	2	44	detection	detection	NOUN
ajst-14953	2	45	method	method	NOUN
ajst-14953	2	46	based	base	VERB
ajst-14953	2	47	on	on	ADP
ajst-14953	2	48	fast	fast	ADJ
ajst-14953	2	49	knn	knn	PROPN
ajst-14953	2	50	algorithm	algorithm	PROPN
ajst-14953	2	51	.	.	PUNCT
ajst-14953	3	1	first	first	ADV
ajst-14953	3	2	,	,	PUNCT
ajst-14953	3	3	continuous	continuous	ADJ
ajst-14953	3	4	data	datum	NOUN
ajst-14953	3	5	discretization	discretization	NOUN
ajst-14953	3	6	processing	processing	NOUN
ajst-14953	3	7	and	and	CCONJ
ajst-14953	3	8	character	character	NOUN
ajst-14953	3	9	data	datum	NOUN
ajst-14953	3	10	digitization	digitization	NOUN
ajst-14953	3	11	processing	processing	NOUN
ajst-14953	3	12	are	be	AUX
ajst-14953	3	13	performed	perform	VERB
ajst-14953	3	14	on	on	ADP
ajst-14953	3	15	the	the	DET
ajst-14953	3	16	original	original	ADJ
ajst-14953	3	17	data	datum	NOUN
ajst-14953	3	18	.	.	PUNCT
ajst-14953	4	1	then	then	ADV
ajst-14953	4	2	the	the	DET
ajst-14953	4	3	feature	feature	NOUN
ajst-14953	4	4	reduction	reduction	NOUN
ajst-14953	4	5	algorithm	algorithm	NOUN
ajst-14953	4	6	based	base	VERB
ajst-14953	4	7	on	on	ADP
ajst-14953	4	8	mutual	mutual	ADJ
ajst-14953	4	9	information	information	NOUN
ajst-14953	4	10	is	be	AUX
ajst-14953	4	11	used	use	VERB
ajst-14953	4	12	to	to	PART
ajst-14953	4	13	reduce	reduce	VERB
ajst-14953	4	14	the	the	DET
ajst-14953	4	15	features	feature	NOUN
ajst-14953	4	16	of	of	ADP
ajst-14953	4	17	the	the	DET
ajst-14953	4	18	preprocessed	preprocesse	VERB
ajst-14953	4	19	data	datum	NOUN
ajst-14953	4	20	.	.	PUNCT
ajst-14953	5	1	finally	finally	ADV
ajst-14953	5	2	,	,	PUNCT
ajst-14953	5	3	the	the	DET
ajst-14953	5	4	fast	fast	ADJ
ajst-14953	5	5	knn	knn	NOUN
ajst-14953	5	6	algorithm	algorithm	PROPN
ajst-14953	5	7	is	be	AUX
ajst-14953	5	8	used	use	VERB
ajst-14953	5	9	to	to	PART
ajst-14953	5	10	classify	classify	VERB
ajst-14953	5	11	and	and	CCONJ
ajst-14953	5	12	detect	detect	VERB
ajst-14953	5	13	the	the	DET
ajst-14953	5	14	feature	feature	NOUN
ajst-14953	5	15	-	-	PUNCT
ajst-14953	5	16	reduced	reduce	VERB
ajst-14953	5	17	data	datum	NOUN
ajst-14953	5	18	,	,	PUNCT
ajst-14953	5	19	and	and	CCONJ
ajst-14953	5	20	the	the	DET
ajst-14953	5	21	classification	classification	NOUN
ajst-14953	5	22	results	result	NOUN
ajst-14953	5	23	are	be	AUX
ajst-14953	5	24	output	output	NOUN
ajst-14953	5	25	.	.	PUNCT
ajst-14953	6	1	the	the	DET
ajst-14953	6	2	above	above	ADJ
ajst-14953	6	3	method	method	NOUN
ajst-14953	6	4	was	be	AUX
ajst-14953	6	5	verified	verify	VERB
ajst-14953	6	6	on	on	ADP
ajst-14953	6	7	the	the	DET
ajst-14953	6	8	kdd	kdd	PROPN
ajst-14953	6	9	cup99	cup99	PROPN
ajst-14953	6	10	data	datum	NOUN
ajst-14953	6	11	set	set	VERB
ajst-14953	6	12	and	and	CCONJ
ajst-14953	6	13	compared	compare	VERB
ajst-14953	6	14	with	with	ADP
ajst-14953	6	15	existing	exist	VERB
ajst-14953	6	16	technologies	technology	NOUN
ajst-14953	6	17	.	.	PUNCT
ajst-14953	7	1	the	the	DET
ajst-14953	7	2	method	method	NOUN
ajst-14953	7	3	proposed	propose	VERB
ajst-14953	7	4	in	in	ADP
ajst-14953	7	5	this	this	DET
ajst-14953	7	6	article	article	NOUN
ajst-14953	7	7	clearly	clearly	ADV
ajst-14953	7	8	has	have	VERB
ajst-14953	7	9	the	the	DET
ajst-14953	7	10	advantages	advantage	NOUN
ajst-14953	7	11	of	of	ADP
ajst-14953	7	12	high	high	ADJ
ajst-14953	7	13	classification	classification	NOUN
ajst-14953	7	14	efficiency	efficiency	NOUN
ajst-14953	7	15	and	and	CCONJ
ajst-14953	7	16	classification	classification	NOUN
ajst-14953	7	17	accuracy	accuracy	NOUN
ajst-14953	7	18	.	.	PUNCT
ajst-14953	8	1	keywords	keyword	NOUN
ajst-14953	8	2	:	:	PUNCT
ajst-14953	8	3	network	network	NOUN
ajst-14953	8	4	security	security	NOUN
ajst-14953	8	5	,	,	PUNCT
ajst-14953	8	6	intrusion	intrusion	NOUN
ajst-14953	8	7	detection	detection	NOUN
ajst-14953	8	8	,	,	PUNCT
ajst-14953	8	9	k	k	ADJ
ajst-14953	8	10	-	-	ADJ
ajst-14953	8	11	neighbor	neighbor	NOUN
ajst-14953	8	12	algorithm	algorithm	NOUN
ajst-14953	8	13	,	,	PUNCT
ajst-14953	8	14	machine	machine	NOUN
ajst-14953	8	15	learning	learning	NOUN
ajst-14953	8	16	.	.	PUNCT
ajst-14953	9	1	1	1	X
ajst-14953	9	2	.	.	X
ajst-14953	9	3	introduction	introduction	NOUN
ajst-14953	9	4	with	with	ADP
ajst-14953	9	5	the	the	DET
ajst-14953	9	6	complexity	complexity	NOUN
ajst-14953	9	7	,	,	PUNCT
ajst-14953	9	8	diversification	diversification	NOUN
ajst-14953	9	9	and	and	CCONJ
ajst-14953	9	10	intelligence	intelligence	NOUN
ajst-14953	9	11	of	of	ADP
ajst-14953	9	12	various	various	ADJ
ajst-14953	9	13	computer	computer	NOUN
ajst-14953	9	14	network	network	NOUN
ajst-14953	9	15	attack	attack	NOUN
ajst-14953	9	16	means	mean	VERB
ajst-14953	9	17	,	,	PUNCT
ajst-14953	9	18	the	the	DET
ajst-14953	9	19	problem	problem	NOUN
ajst-14953	9	20	of	of	ADP
ajst-14953	9	21	network	network	NOUN
ajst-14953	9	22	information	information	NOUN
ajst-14953	9	23	security	security	NOUN
ajst-14953	9	24	has	have	AUX
ajst-14953	9	25	become	become	VERB
ajst-14953	9	26	increasingly	increasingly	ADV
ajst-14953	9	27	prominent	prominent	ADJ
ajst-14953	9	28	.	.	PUNCT
ajst-14953	10	1	benefit	benefit	NOUN
ajst-14953	10	2	to	to	ADP
ajst-14953	10	3	network	network	NOUN
ajst-14953	10	4	destruction	destruction	NOUN
ajst-14953	10	5	of	of	ADP
ajst-14953	10	6	terminal	terminal	ADJ
ajst-14953	10	7	operating	operating	NOUN
ajst-14953	10	8	system	system	NOUN
ajst-14953	10	9	,	,	PUNCT
ajst-14953	10	10	illegal	illegal	ADJ
ajst-14953	10	11	theft	theft	NOUN
ajst-14953	10	12	of	of	ADP
ajst-14953	10	13	personal	personal	ADJ
ajst-14953	10	14	data	datum	NOUN
ajst-14953	10	15	,	,	PUNCT
ajst-14953	10	16	bank	bank	NOUN
ajst-14953	10	17	account	account	NOUN
ajst-14953	10	18	password	password	NOUN
ajst-14953	10	19	,	,	PUNCT
ajst-14953	10	20	illegal	illegal	ADJ
ajst-14953	10	21	intrusion	intrusion	NOUN
ajst-14953	10	22	of	of	ADP
ajst-14953	10	23	system	system	NOUN
ajst-14953	10	24	database	database	NOUN
ajst-14953	10	25	and	and	CCONJ
ajst-14953	10	26	other	other	ADJ
ajst-14953	10	27	behaviors	behavior	NOUN
ajst-14953	10	28	seriously	seriously	ADV
ajst-14953	10	29	hinder	hinder	VERB
ajst-14953	10	30	the	the	DET
ajst-14953	10	31	normal	normal	ADJ
ajst-14953	10	32	use	use	NOUN
ajst-14953	10	33	of	of	ADP
ajst-14953	10	34	the	the	DET
ajst-14953	10	35	internet	internet	NOUN
ajst-14953	10	36	.	.	PUNCT
ajst-14953	11	1	intrusion	intrusion	NOUN
ajst-14953	11	2	detection	detection	NOUN
ajst-14953	11	3	technology	technology	NOUN
ajst-14953	11	4	as	as	ADP
ajst-14953	11	5	an	an	DET
ajst-14953	11	6	important	important	ADJ
ajst-14953	11	7	dynamic	dynamic	ADJ
ajst-14953	11	8	protection	protection	NOUN
ajst-14953	11	9	means	mean	NOUN
ajst-14953	11	10	of	of	ADP
ajst-14953	11	11	network	network	NOUN
ajst-14953	11	12	security	security	NOUN
ajst-14953	11	13	system	system	NOUN
ajst-14953	11	14	,	,	PUNCT
ajst-14953	11	15	can	can	AUX
ajst-14953	11	16	identify	identify	VERB
ajst-14953	11	17	the	the	DET
ajst-14953	11	18	computer	computer	NOUN
ajst-14953	11	19	network	network	NOUN
ajst-14953	11	20	illegal	illegal	ADJ
ajst-14953	11	21	or	or	CCONJ
ajst-14953	11	22	malicious	malicious	ADJ
ajst-14953	11	23	attacks	attack	NOUN
ajst-14953	11	24	,	,	PUNCT
ajst-14953	11	25	and	and	CCONJ
ajst-14953	11	26	the	the	DET
ajst-14953	11	27	corresponding	corresponding	ADJ
ajst-14953	11	28	response	response	NOUN
ajst-14953	11	29	,	,	PUNCT
ajst-14953	11	30	as	as	ADP
ajst-14953	11	31	a	a	DET
ajst-14953	11	32	network	network	NOUN
ajst-14953	11	33	security	security	NOUN
ajst-14953	11	34	technology	technology	NOUN
ajst-14953	11	35	,	,	PUNCT
ajst-14953	11	36	and	and	CCONJ
ajst-14953	11	37	the	the	DET
ajst-14953	11	38	second	second	ADJ
ajst-14953	11	39	security	security	NOUN
ajst-14953	11	40	gate	gate	NOUN
ajst-14953	11	41	after	after	ADP
ajst-14953	11	42	the	the	DET
ajst-14953	11	43	firewall	firewall	NOUN
ajst-14953	11	44	,	,	PUNCT
ajst-14953	11	45	intrusion	intrusion	NOUN
ajst-14953	11	46	detection	detection	NOUN
ajst-14953	11	47	technology	technology	NOUN
ajst-14953	11	48	is	be	AUX
ajst-14953	11	49	one	one	NUM
ajst-14953	11	50	of	of	ADP
ajst-14953	11	51	the	the	DET
ajst-14953	11	52	very	very	ADV
ajst-14953	11	53	important	important	ADJ
ajst-14953	11	54	core	core	NOUN
ajst-14953	11	55	technology	technology	NOUN
ajst-14953	11	56	of	of	ADP
ajst-14953	11	57	internet	internet	NOUN
ajst-14953	11	58	security	security	NOUN
ajst-14953	11	59	,	,	PUNCT
ajst-14953	11	60	it	it	PRON
ajst-14953	11	61	extends	extend	VERB
ajst-14953	11	62	the	the	DET
ajst-14953	11	63	system	system	NOUN
ajst-14953	11	64	administrator	administrator	NOUN
ajst-14953	11	65	security	security	NOUN
ajst-14953	11	66	management	management	NOUN
ajst-14953	11	67	ability	ability	NOUN
ajst-14953	11	68	at	at	ADP
ajst-14953	11	69	the	the	DET
ajst-14953	11	70	same	same	ADJ
ajst-14953	11	71	time	time	NOUN
ajst-14953	11	72	can	can	AUX
ajst-14953	11	73	improve	improve	VERB
ajst-14953	11	74	the	the	DET
ajst-14953	11	75	integrity	integrity	NOUN
ajst-14953	11	76	of	of	ADP
ajst-14953	11	77	the	the	DET
ajst-14953	11	78	system	system	NOUN
ajst-14953	11	79	security	security	NOUN
ajst-14953	11	80	structure	structure	NOUN
ajst-14953	11	81	.	.	PUNCT
ajst-14953	12	1	intrusion	intrusion	NOUN
ajst-14953	12	2	detection	detection	NOUN
ajst-14953	12	3	algorithm	algorithm	NOUN
ajst-14953	12	4	is	be	AUX
ajst-14953	12	5	the	the	DET
ajst-14953	12	6	most	most	ADV
ajst-14953	12	7	core	core	ADJ
ajst-14953	12	8	part	part	NOUN
ajst-14953	12	9	of	of	ADP
ajst-14953	12	10	the	the	DET
ajst-14953	12	11	intrusion	intrusion	NOUN
ajst-14953	12	12	detection	detection	NOUN
ajst-14953	12	13	system	system	NOUN
ajst-14953	12	14	.	.	PUNCT
ajst-14953	13	1	its	its	PRON
ajst-14953	13	2	detection	detection	NOUN
ajst-14953	13	3	ability	ability	NOUN
ajst-14953	13	4	and	and	CCONJ
ajst-14953	13	5	efficiency	efficiency	NOUN
ajst-14953	13	6	directly	directly	ADV
ajst-14953	13	7	determine	determine	VERB
ajst-14953	13	8	the	the	DET
ajst-14953	13	9	detection	detection	NOUN
ajst-14953	13	10	ability	ability	NOUN
ajst-14953	13	11	of	of	ADP
ajst-14953	13	12	the	the	DET
ajst-14953	13	13	whole	whole	ADJ
ajst-14953	13	14	intrusion	intrusion	NOUN
ajst-14953	13	15	detection	detection	NOUN
ajst-14953	13	16	system	system	NOUN
ajst-14953	13	17	.	.	PUNCT
ajst-14953	14	1	the	the	DET
ajst-14953	14	2	k	k	NOUN
ajst-14953	14	3	-	-	PUNCT
ajst-14953	14	4	nearest	near	ADJ
ajst-14953	14	5	neighbor	neighbor	NOUN
ajst-14953	14	6	algorithm	algorithm	NOUN
ajst-14953	14	7	,	,	PUNCT
ajst-14953	14	8	also	also	ADV
ajst-14953	14	9	called	call	VERB
ajst-14953	14	10	knn	knn	PROPN
ajst-14953	14	11	or	or	CCONJ
ajst-14953	14	12	knn	knn	PROPN
ajst-14953	14	13	,	,	PUNCT
ajst-14953	14	14	is	be	AUX
ajst-14953	14	15	a	a	DET
ajst-14953	14	16	non	non	ADJ
ajst-14953	14	17	-	-	ADJ
ajst-14953	14	18	parametric	parametric	ADJ
ajst-14953	14	19	,	,	PUNCT
ajst-14953	14	20	supervised	supervised	ADJ
ajst-14953	14	21	learning	learn	VERB
ajst-14953	14	22	classifier	classifier	NOUN
ajst-14953	14	23	where	where	SCONJ
ajst-14953	14	24	knn	knn	PROPN
ajst-14953	14	25	uses	use	VERB
ajst-14953	14	26	proximity	proximity	NOUN
ajst-14953	14	27	to	to	PART
ajst-14953	14	28	classify	classify	VERB
ajst-14953	14	29	or	or	CCONJ
ajst-14953	14	30	predict	predict	VERB
ajst-14953	14	31	groups	group	NOUN
ajst-14953	14	32	of	of	ADP
ajst-14953	14	33	individual	individual	ADJ
ajst-14953	14	34	data	datum	NOUN
ajst-14953	14	35	points	point	NOUN
ajst-14953	14	36	.	.	PUNCT
ajst-14953	15	1	the	the	DET
ajst-14953	15	2	existing	exist	VERB
ajst-14953	15	3	knn	knn	NOUN
ajst-14953	15	4	algorithm	algorithm	NOUN
ajst-14953	15	5	and	and	CCONJ
ajst-14953	15	6	related	relate	VERB
ajst-14953	15	7	improved	improved	ADJ
ajst-14953	15	8	algorithms	algorithm	NOUN
ajst-14953	15	9	have	have	AUX
ajst-14953	15	10	been	be	AUX
ajst-14953	15	11	widely	widely	ADV
ajst-14953	15	12	used	use	VERB
ajst-14953	15	13	in	in	ADP
ajst-14953	15	14	network	network	NOUN
ajst-14953	15	15	intrusion	intrusion	NOUN
ajst-14953	15	16	detection[1	detection[1	PROPN
ajst-14953	15	17	-	-	PUNCT
ajst-14953	15	18	6	6	NUM
ajst-14953	15	19	]	]	PUNCT
ajst-14953	15	20	.	.	PUNCT
ajst-14953	16	1	however	however	ADV
ajst-14953	16	2	,	,	PUNCT
ajst-14953	16	3	there	there	PRON
ajst-14953	16	4	is	be	VERB
ajst-14953	16	5	still	still	ADV
ajst-14953	16	6	some	some	DET
ajst-14953	16	7	room	room	NOUN
ajst-14953	16	8	for	for	ADP
ajst-14953	16	9	improvement	improvement	NOUN
ajst-14953	16	10	in	in	ADP
ajst-14953	16	11	both	both	CCONJ
ajst-14953	16	12	the	the	DET
ajst-14953	16	13	detection	detection	NOUN
ajst-14953	16	14	capacity	capacity	NOUN
ajst-14953	16	15	and	and	CCONJ
ajst-14953	16	16	the	the	DET
ajst-14953	16	17	detection	detection	NOUN
ajst-14953	16	18	efficiency	efficiency	NOUN
ajst-14953	16	19	.	.	PUNCT
ajst-14953	17	1	it	it	PRON
ajst-14953	17	2	is	be	AUX
ajst-14953	17	3	of	of	ADP
ajst-14953	17	4	great	great	ADJ
ajst-14953	17	5	significance	significance	NOUN
ajst-14953	17	6	to	to	PART
ajst-14953	17	7	improve	improve	VERB
ajst-14953	17	8	the	the	DET
ajst-14953	17	9	classification	classification	NOUN
ajst-14953	17	10	accuracy	accuracy	NOUN
ajst-14953	17	11	,	,	PUNCT
ajst-14953	17	12	reduce	reduce	VERB
ajst-14953	17	13	the	the	DET
ajst-14953	17	14	false	false	ADJ
ajst-14953	17	15	detection	detection	NOUN
ajst-14953	17	16	rate	rate	NOUN
ajst-14953	17	17	,	,	PUNCT
ajst-14953	17	18	and	and	CCONJ
ajst-14953	17	19	maximize	maximize	VERB
ajst-14953	17	20	the	the	DET
ajst-14953	17	21	learning	learning	NOUN
ajst-14953	17	22	speed	speed	NOUN
ajst-14953	17	23	of	of	ADP
ajst-14953	17	24	the	the	DET
ajst-14953	17	25	algorithm	algorithm	NOUN
ajst-14953	17	26	.	.	PUNCT
ajst-14953	18	1	the	the	DET
ajst-14953	18	2	fast	fast	ADJ
ajst-14953	18	3	knn	knn	PROPN
ajst-14953	18	4	algorithm	algorithm	PROPN
ajst-14953	18	5	proposed	propose	VERB
ajst-14953	18	6	in	in	ADP
ajst-14953	18	7	this	this	DET
ajst-14953	18	8	paper	paper	NOUN
ajst-14953	18	9	is	be	AUX
ajst-14953	18	10	improved	improve	VERB
ajst-14953	18	11	in	in	ADP
ajst-14953	18	12	terms	term	NOUN
ajst-14953	18	13	of	of	ADP
ajst-14953	18	14	efficiency	efficiency	NOUN
ajst-14953	18	15	and	and	CCONJ
ajst-14953	18	16	accuracy	accuracy	NOUN
ajst-14953	18	17	,	,	PUNCT
ajst-14953	18	18	and	and	CCONJ
ajst-14953	18	19	is	be	AUX
ajst-14953	18	20	validated	validate	VERB
ajst-14953	18	21	on	on	ADP
ajst-14953	18	22	the	the	DET
ajst-14953	18	23	kdd	kdd	PROPN
ajst-14953	18	24	99	99	NUM
ajst-14953	18	25	dataset	dataset	NOUN
ajst-14953	18	26	.	.	PUNCT
ajst-14953	19	1	2	2	X
ajst-14953	19	2	.	.	X
ajst-14953	19	3	the	the	DET
ajst-14953	19	4	knn	knn	PROPN
ajst-14953	19	5	invasion	invasion	PROPN
ajst-14953	19	6	detection	detection	NOUN
ajst-14953	19	7	model	model	NOUN
ajst-14953	19	8	2.1	2.1	NUM
ajst-14953	19	9	.	.	PUNCT
ajst-14953	20	1	the	the	DET
ajst-14953	20	2	k	k	NOUN
ajst-14953	20	3	-	-	PUNCT
ajst-14953	20	4	nearest	near	ADJ
ajst-14953	20	5	neighbor	neighbor	NOUN
ajst-14953	20	6	algorithm	algorithm	NOUN
ajst-14953	20	7	the	the	DET
ajst-14953	20	8	k	k	ADJ
ajst-14953	20	9	-	-	ADJ
ajst-14953	20	10	neighbor	neighbor	NOUN
ajst-14953	20	11	algorithm	algorithm	NOUN
ajst-14953	20	12	(	(	PUNCT
ajst-14953	20	13	knn	knn	PROPN
ajst-14953	20	14	algorithm	algorithm	PROPN
ajst-14953	20	15	)	)	PUNCT
ajst-14953	20	16	was	be	AUX
ajst-14953	20	17	proposed	propose	VERB
ajst-14953	20	18	by	by	ADP
ajst-14953	20	19	thamas	thama	NOUN
ajst-14953	20	20	in	in	ADP
ajst-14953	20	21	1967	1967	NUM
ajst-14953	20	22	.	.	PUNCT
ajst-14953	21	1	it	it	PRON
ajst-14953	21	2	is	be	AUX
ajst-14953	21	3	based	base	VERB
ajst-14953	21	4	on	on	ADP
ajst-14953	21	5	the	the	DET
ajst-14953	21	6	following	follow	VERB
ajst-14953	21	7	idea	idea	NOUN
ajst-14953	21	8	;	;	PUNCT
ajst-14953	21	9	to	to	PART
ajst-14953	21	10	determine	determine	VERB
ajst-14953	21	11	the	the	DET
ajst-14953	21	12	category	category	NOUN
ajst-14953	21	13	of	of	ADP
ajst-14953	21	14	a	a	DET
ajst-14953	21	15	sample	sample	NOUN
ajst-14953	21	16	,	,	PUNCT
ajst-14953	21	17	you	you	PRON
ajst-14953	21	18	can	can	AUX
ajst-14953	21	19	calculate	calculate	VERB
ajst-14953	21	20	the	the	DET
ajst-14953	21	21	distance	distance	NOUN
ajst-14953	21	22	from	from	ADP
ajst-14953	21	23	all	all	DET
ajst-14953	21	24	training	training	NOUN
ajst-14953	21	25	samples	sample	NOUN
ajst-14953	21	26	,	,	PUNCT
ajst-14953	21	27	then	then	ADV
ajst-14953	21	28	find	find	VERB
ajst-14953	21	29	the	the	DET
ajst-14953	21	30	closest	close	ADJ
ajst-14953	21	31	k	k	PROPN
ajst-14953	21	32	samples	sample	NOUN
ajst-14953	21	33	,	,	PUNCT
ajst-14953	21	34	count	count	VERB
ajst-14953	21	35	the	the	DET
ajst-14953	21	36	number	number	NOUN
ajst-14953	21	37	of	of	ADP
ajst-14953	21	38	categories	category	NOUN
ajst-14953	21	39	of	of	ADP
ajst-14953	21	40	the	the	DET
ajst-14953	21	41	sample	sample	NOUN
ajst-14953	21	42	,	,	PUNCT
ajst-14953	21	43	the	the	DET
ajst-14953	21	44	largest	large	ADJ
ajst-14953	21	45	class	class	NOUN
ajst-14953	21	46	is	be	AUX
ajst-14953	21	47	the	the	DET
ajst-14953	21	48	classification	classification	NOUN
ajst-14953	21	49	result	result	NOUN
ajst-14953	21	50	,	,	PUNCT
ajst-14953	21	51	for	for	ADP
ajst-14953	21	52	the	the	DET
ajst-14953	21	53	classification	classification	NOUN
ajst-14953	21	54	problem	problem	NOUN
ajst-14953	21	55	,	,	PUNCT
ajst-14953	21	56	given	give	VERB
ajst-14953	21	57	a	a	DET
ajst-14953	21	58	training	training	NOUN
ajst-14953	21	59	sample	sample	NOUN
ajst-14953	21	60	(	(	PUNCT
ajst-14953	21	61	xi	xi	PROPN
ajst-14953	21	62	,	,	PUNCT
ajst-14953	21	63	yi	yi	PROPN
ajst-14953	21	64	)	)	PUNCT
ajst-14953	21	65	,	,	PUNCT
ajst-14953	21	66	where	where	SCONJ
ajst-14953	21	67	xi	xi	PROPN
ajst-14953	21	68	is	be	AUX
ajst-14953	21	69	the	the	DET
ajst-14953	21	70	feature	feature	NOUN
ajst-14953	21	71	vector	vector	NOUN
ajst-14953	21	72	,	,	PUNCT
ajst-14953	21	73	yi	yi	PROPN
ajst-14953	21	74	is	be	AUX
ajst-14953	21	75	the	the	DET
ajst-14953	21	76	label	label	NOUN
ajst-14953	21	77	value	value	NOUN
ajst-14953	21	78	.	.	PUNCT
ajst-14953	22	1	set	set	VERB
ajst-14953	22	2	parameter	parameter	PROPN
ajst-14953	22	3	k	k	PROPN
ajst-14953	22	4	,	,	PUNCT
ajst-14953	22	5	assume	assume	VERB
ajst-14953	22	6	the	the	DET
ajst-14953	22	7	type	type	NOUN
ajst-14953	22	8	is	be	AUX
ajst-14953	22	9	c	c	NOUN
ajst-14953	22	10	,	,	PUNCT
ajst-14953	22	11	and	and	CCONJ
ajst-14953	22	12	the	the	DET
ajst-14953	22	13	feature	feature	NOUN
ajst-14953	22	14	vector	vector	NOUN
ajst-14953	22	15	of	of	ADP
ajst-14953	22	16	the	the	DET
ajst-14953	22	17	sample	sample	NOUN
ajst-14953	22	18	to	to	PART
ajst-14953	22	19	be	be	AUX
ajst-14953	22	20	classified	classify	VERB
ajst-14953	22	21	is	be	AUX
ajst-14953	22	22	x.	x.	NOUN
ajst-14953	22	23	the	the	DET
ajst-14953	22	24	flow	flow	NOUN
ajst-14953	22	25	of	of	ADP
ajst-14953	22	26	the	the	DET
ajst-14953	22	27	prediction	prediction	NOUN
ajst-14953	22	28	algorithm	algorithm	NOUN
ajst-14953	22	29	is	be	AUX
ajst-14953	22	30	as	as	SCONJ
ajst-14953	22	31	follows	follow	VERB
ajst-14953	22	32	:	:	PUNCT
ajst-14953	22	33	(	(	PUNCT
ajst-14953	22	34	1	1	X
ajst-14953	22	35	)	)	PUNCT
ajst-14953	22	36	find	find	VERB
ajst-14953	22	37	the	the	DET
ajst-14953	22	38	k	k	PROPN
ajst-14953	22	39	samples	sample	NOUN
ajst-14953	22	40	closest	close	ADJ
ajst-14953	22	41	to	to	ADP
ajst-14953	22	42	x	x	PUNCT
ajst-14953	22	43	in	in	ADP
ajst-14953	22	44	the	the	DET
ajst-14953	22	45	training	training	NOUN
ajst-14953	22	46	sample	sample	NOUN
ajst-14953	22	47	set	set	NOUN
ajst-14953	22	48	,	,	PUNCT
ajst-14953	22	49	assuming	assume	VERB
ajst-14953	22	50	that	that	SCONJ
ajst-14953	22	51	the	the	DET
ajst-14953	22	52	set	set	NOUN
ajst-14953	22	53	of	of	ADP
ajst-14953	22	54	these	these	DET
ajst-14953	22	55	samples	sample	NOUN
ajst-14953	22	56	is	be	AUX
ajst-14953	22	57	n.	n.	NOUN
ajst-14953	22	58	(	(	PUNCT
ajst-14953	22	59	2	2	NUM
ajst-14953	22	60	)	)	PUNCT
ajst-14953	22	61	count	count	VERB
ajst-14953	22	62	the	the	DET
ajst-14953	22	63	number	number	NOUN
ajst-14953	22	64	of	of	ADP
ajst-14953	22	65	samples	sample	NOUN
ajst-14953	22	66	of	of	ADP
ajst-14953	22	67	each	each	DET
ajst-14953	22	68	class	class	NOUN
ajst-14953	22	69	in	in	ADP
ajst-14953	22	70	the	the	DET
ajst-14953	22	71	set	set	ADJ
ajst-14953	22	72	n.	n.	NOUN
ajst-14953	22	73	ci，i	ci，i	PROPN
ajst-14953	22	74	1,2	1,2	NUM
ajst-14953	22	75	，	，	PROPN
ajst-14953	22	76	.	.	PUNCT
ajst-14953	22	77	.	.	PUNCT
ajst-14953	23	1	.，e	.，e	PROPN
ajst-14953	23	2	.	.	PUNCT
ajst-14953	24	1	(	(	PUNCT
ajst-14953	24	2	3	3	X
ajst-14953	24	3	)	)	PUNCT
ajst-14953	24	4	the	the	DET
ajst-14953	24	5	final	final	ADJ
ajst-14953	24	6	classification	classification	NOUN
ajst-14953	24	7	result	result	NOUN
ajst-14953	24	8	is	be	AUX
ajst-14953	24	9	the	the	DET
ajst-14953	24	10	arg	arg	NOUN
ajst-14953	24	11	maxci	maxci	NOUN
ajst-14953	24	12	.	.	PUNCT
ajst-14953	25	1	where	where	SCONJ
ajst-14953	25	2	arg	arg	NOUN
ajst-14953	25	3	maxc	maxc	NOUN
ajst-14953	25	4	representing	represent	VERB
ajst-14953	25	5	the	the	DET
ajst-14953	25	6	largest	large	ADJ
ajst-14953	25	7	value	value	NOUN
ajst-14953	25	8	corresponding	correspond	VERB
ajst-14953	25	9	to	to	ADP
ajst-14953	25	10	the	the	DET
ajst-14953	25	11	class	class	NOUN
ajst-14953	25	12	i	i	PRON
ajst-14953	25	13	,	,	PUNCT
ajst-14953	25	14	if	if	SCONJ
ajst-14953	25	15	k=1	k=1	PROPN
ajst-14953	25	16	,	,	PUNCT
ajst-14953	25	17	the	the	DET
ajst-14953	25	18	k	k	PROPN
ajst-14953	25	19	nearest	near	ADJ
ajst-14953	25	20	neighbor	neighbor	PROPN
ajst-14953	25	21	algorithm	algorithm	PROPN
ajst-14953	25	22	degenerate	degenerate	ADJ
ajst-14953	25	23	into	into	ADP
ajst-14953	25	24	the	the	DET
ajst-14953	25	25	nearest	near	ADJ
ajst-14953	25	26	neighbor	neighbor	NOUN
ajst-14953	25	27	algorithm	algorithm	NOUN
ajst-14953	25	28	.	.	PUNCT
ajst-14953	26	1	2.2	2.2	NUM
ajst-14953	26	2	.	.	PUNCT
ajst-14953	26	3	intrusion	intrusion	NOUN
ajst-14953	26	4	detection	detection	NOUN
ajst-14953	26	5	model	model	NOUN
ajst-14953	26	6	based	base	VERB
ajst-14953	26	7	on	on	ADP
ajst-14953	26	8	the	the	DET
ajst-14953	26	9	fast	fast	ADJ
ajst-14953	26	10	knn	knn	PROPN
ajst-14953	26	11	algorithm	algorithm	PROPN
ajst-14953	26	12	the	the	DET
ajst-14953	26	13	structure	structure	NOUN
ajst-14953	26	14	of	of	ADP
ajst-14953	26	15	the	the	DET
ajst-14953	26	16	invasion	invasion	NOUN
ajst-14953	26	17	detection	detection	NOUN
ajst-14953	26	18	model	model	NOUN
ajst-14953	26	19	based	base	VERB
ajst-14953	26	20	on	on	ADP
ajst-14953	26	21	the	the	DET
ajst-14953	26	22	fast	fast	ADJ
ajst-14953	26	23	knn	knn	PROPN
ajst-14953	26	24	algorithm	algorithm	PROPN
ajst-14953	26	25	is	be	AUX
ajst-14953	26	26	shown	show	VERB
ajst-14953	26	27	in	in	ADP
ajst-14953	26	28	figure	figure	NOUN
ajst-14953	26	29	1	1	NUM
ajst-14953	26	30	,	,	PUNCT
ajst-14953	26	31	as	as	SCONJ
ajst-14953	26	32	follows	follow	VERB
ajst-14953	26	33	:	:	PUNCT
ajst-14953	26	34	step	step	NOUN
ajst-14953	26	35	1	1	NUM
ajst-14953	26	36	:	:	PUNCT
ajst-14953	26	37	data	datum	NOUN
ajst-14953	26	38	preprocessing	preprocessing	NOUN
ajst-14953	26	39	step	step	NOUN
ajst-14953	26	40	:	:	PUNCT
ajst-14953	26	41	receive	receive	VERB
ajst-14953	26	42	the	the	DET
ajst-14953	26	43	intrusion	intrusion	NOUN
ajst-14953	26	44	detection	detection	NOUN
ajst-14953	26	45	raw	raw	ADJ
ajst-14953	26	46	data	datum	NOUN
ajst-14953	26	47	,	,	PUNCT
ajst-14953	26	48	conduct	conduct	VERB
ajst-14953	26	49	data	datum	NOUN
ajst-14953	26	50	preprocessing	preprocessing	NOUN
ajst-14953	26	51	of	of	ADP
ajst-14953	26	52	the	the	DET
ajst-14953	26	53	raw	raw	ADJ
ajst-14953	26	54	data	datum	NOUN
ajst-14953	26	55	,	,	PUNCT
ajst-14953	26	56	including	include	VERB
ajst-14953	26	57	continuous	continuous	ADJ
ajst-14953	26	58	data	datum	NOUN
ajst-14953	26	59	discretization	discretization	NOUN
ajst-14953	26	60	processing	processing	NOUN
ajst-14953	26	61	and	and	CCONJ
ajst-14953	26	62	character	character	NOUN
ajst-14953	26	63	data	datum	NOUN
ajst-14953	26	64	digital	digital	ADJ
ajst-14953	26	65	processing	processing	NOUN
ajst-14953	26	66	.	.	PUNCT
ajst-14953	27	1	step	step	NOUN
ajst-14953	27	2	2	2	NUM
ajst-14953	27	3	:	:	PUNCT
ajst-14953	27	4	feature	feature	NOUN
ajst-14953	27	5	reduction	reduction	NOUN
ajst-14953	27	6	step	step	NOUN
ajst-14953	27	7	.	.	PUNCT
ajst-14953	28	1	the	the	DET
ajst-14953	28	2	feature	feature	NOUN
ajst-14953	28	3	reduction	reduction	NOUN
ajst-14953	28	4	algorithm	algorithm	NOUN
ajst-14953	28	5	based	base	VERB
ajst-14953	28	6	on	on	ADP
ajst-14953	28	7	mutual	mutual	ADJ
ajst-14953	28	8	information	information	NOUN
ajst-14953	28	9	is	be	AUX
ajst-14953	28	10	used	use	VERB
ajst-14953	28	11	to	to	PART
ajst-14953	28	12	reduce	reduce	VERB
ajst-14953	28	13	the	the	DET
ajst-14953	28	14	pretreated	pretreate	VERB
ajst-14953	28	15	data	datum	NOUN
ajst-14953	28	16	.	.	PUNCT
ajst-14953	29	1	step	step	NOUN
ajst-14953	29	2	3	3	NUM
ajst-14953	29	3	:	:	PUNCT
ajst-14953	29	4	use	use	VERB
ajst-14953	29	5	the	the	DET
ajst-14953	29	6	fast	fast	ADJ
ajst-14953	29	7	knn	knn	NOUN
ajst-14953	29	8	algorithm	algorithm	NOUN
ajst-14953	29	9	to	to	PART
ajst-14953	29	10	classify	classify	VERB
ajst-14953	29	11	the	the	DET
ajst-14953	29	12	data	datum	NOUN
ajst-14953	29	13	after	after	ADP
ajst-14953	29	14	feature	feature	NOUN
ajst-14953	29	15	reduction	reduction	NOUN
ajst-14953	29	16	and	and	CCONJ
ajst-14953	29	17	output	output	VERB
ajst-14953	29	18	the	the	DET
ajst-14953	29	19	classification	classification	NOUN
ajst-14953	29	20	.	.	PUNCT
ajst-14953	30	1	the	the	DET
ajst-14953	30	2	feature	feature	NOUN
ajst-14953	30	3	reduction	reduction	NOUN
ajst-14953	30	4	step	step	NOUN
ajst-14953	30	5	of	of	ADP
ajst-14953	30	6	step	step	NOUN
ajst-14953	30	7	2	2	NUM
ajst-14953	30	8	specifically	specifically	ADV
ajst-14953	30	9	includes	include	VERB
ajst-14953	30	10	the	the	DET
ajst-14953	30	11	following	follow	VERB
ajst-14953	30	12	sub	sub	NOUN
ajst-14953	30	13	-	-	NOUN
ajst-14953	30	14	steps	step	NOUN
ajst-14953	30	15	:	:	PUNCT
ajst-14953	30	16	(	(	PUNCT
ajst-14953	30	17	1	1	X
ajst-14953	30	18	)	)	PUNCT
ajst-14953	30	19	initialization	initialization	NOUN
ajst-14953	30	20	:	:	PUNCT
ajst-14953	30	21	the	the	DET
ajst-14953	30	22	feature	feature	NOUN
ajst-14953	30	23	set	set	NOUN
ajst-14953	30	24	of	of	ADP
ajst-14953	30	25	the	the	DET
ajst-14953	30	26	original	original	ADJ
ajst-14953	30	27	data	datum	NOUN
ajst-14953	30	28	is	be	AUX
ajst-14953	30	29	set	set	VERB
ajst-14953	30	30	to	to	ADP
ajst-14953	30	31	f	f	PROPN
ajst-14953	30	32	f	f	PROPN
ajst-14953	30	33	,	,	PUNCT
ajst-14953	30	34	f	f	PROPN
ajst-14953	30	35	⋯	⋯	PROPN
ajst-14953	30	36	f	f	PROPN
ajst-14953	30	37	,	,	PUNCT
ajst-14953	30	38	m	m	PROPN
ajst-14953	30	39	is	be	AUX
ajst-14953	30	40	the	the	DET
ajst-14953	30	41	total	total	ADJ
ajst-14953	30	42	number	number	NOUN
ajst-14953	30	43	of	of	ADP
ajst-14953	30	44	features	feature	NOUN
ajst-14953	30	45	.	.	PUNCT
ajst-14953	31	1	the	the	DET
ajst-14953	31	2	category	category	NOUN
ajst-14953	31	3	identifier	identifier	NOUN
ajst-14953	31	4	of	of	ADP
ajst-14953	31	5	the	the	DET
ajst-14953	31	6	data	datum	NOUN
ajst-14953	31	7	set	set	VERB
ajst-14953	31	8	is	be	AUX
ajst-14953	31	9	set	set	VERB
ajst-14953	31	10	to	to	AUX
ajst-14953	31	11	y.	y.	PROPN
ajst-14953	31	12	set	set	VERB
ajst-14953	31	13	the	the	DET
ajst-14953	31	14	empty	empty	ADJ
ajst-14953	31	15	set	set	NOUN
ajst-14953	31	16	s	s	PROPN
ajst-14953	31	17	,	,	PUNCT
ajst-14953	31	18	assuming	assume	VERB
ajst-14953	31	19	that	that	SCONJ
ajst-14953	31	20	n	n	PRON
ajst-14953	31	21	features	feature	VERB
ajst-14953	31	22	need	need	VERB
ajst-14953	31	23	to	to	PART
ajst-14953	31	24	be	be	AUX
ajst-14953	31	25	selected	select	VERB
ajst-14953	31	26	.	.	PUNCT
ajst-14953	32	1	(	(	PUNCT
ajst-14953	32	2	2	2	X
ajst-14953	32	3	)	)	PUNCT
ajst-14953	32	4	select	select	VERB
ajst-14953	32	5	the	the	DET
ajst-14953	32	6	first	first	ADJ
ajst-14953	32	7	feature	feature	NOUN
ajst-14953	32	8	:	:	PUNCT
ajst-14953	32	9	for	for	ADP
ajst-14953	32	10	each	each	DET
ajst-14953	32	11	feature	feature	NOUN
ajst-14953	32	12	f	f	PROPN
ajst-14953	32	13	in	in	ADP
ajst-14953	32	14	f	f	PROPN
ajst-14953	32	15	,	,	PUNCT
ajst-14953	32	16	calculate	calculate	VERB
ajst-14953	32	17	the	the	DET
ajst-14953	32	18	mutual	mutual	ADJ
ajst-14953	32	19	information	information	NOUN
ajst-14953	33	1	i	i	PRON
ajst-14953	33	2	f	f	PROPN
ajst-14953	33	3	;	;	PUNCT
ajst-14953	33	4	y	y	X
ajst-14953	33	5	)	)	PUNCT
ajst-14953	33	6	between	between	ADP
ajst-14953	33	7	f	f	PROPN
ajst-14953	33	8	and	and	CCONJ
ajst-14953	33	9	the	the	DET
ajst-14953	33	10	category	category	NOUN
ajst-14953	33	11	identifier	identifier	NOUN
ajst-14953	33	12	y	y	PROPN
ajst-14953	33	13	,	,	PUNCT
ajst-14953	33	14	then	then	ADV
ajst-14953	33	15	choose	choose	VERB
ajst-14953	33	16	f	f	PROPN
ajst-14953	33	17	that	that	PRON
ajst-14953	33	18	maximizes	maximize	VERB
ajst-14953	33	19	the	the	DET
ajst-14953	33	20	value	value	NOUN
ajst-14953	33	21	of	of	ADP
ajst-14953	33	22	82	82	NUM
ajst-14953	33	23	i	i	NOUN
ajst-14953	33	24	f	f	PROPN
ajst-14953	33	25	;	;	PUNCT
ajst-14953	33	26	y	y	PROPN
ajst-14953	33	27	)	)	PUNCT
ajst-14953	33	28	,	,	PUNCT
ajst-14953	33	29	store	store	NOUN
ajst-14953	33	30	f	f	PROPN
ajst-14953	33	31	in	in	ADP
ajst-14953	33	32	the	the	DET
ajst-14953	33	33	set	set	NOUN
ajst-14953	33	34	s	s	PROPN
ajst-14953	33	35	,	,	PUNCT
ajst-14953	33	36	this	this	DET
ajst-14953	33	37	feature	feature	NOUN
ajst-14953	33	38	is	be	AUX
ajst-14953	33	39	the	the	DET
ajst-14953	33	40	first	first	ADJ
ajst-14953	33	41	feature	feature	NOUN
ajst-14953	33	42	,	,	PUNCT
ajst-14953	33	43	and	and	CCONJ
ajst-14953	33	44	remove	remove	VERB
ajst-14953	33	45	f	f	PROPN
ajst-14953	33	46	from	from	ADP
ajst-14953	33	47	the	the	DET
ajst-14953	33	48	set	set	NOUN
ajst-14953	33	49	f.	f.	PROPN
ajst-14953	33	50	(	(	PUNCT
ajst-14953	33	51	3	3	X
ajst-14953	33	52	)	)	PUNCT
ajst-14953	33	53	the	the	DET
ajst-14953	33	54	remaining	remain	VERB
ajst-14953	33	55	n-1	n-1	NOUN
ajst-14953	33	56	features	feature	NOUN
ajst-14953	33	57	were	be	AUX
ajst-14953	33	58	selected	select	VERB
ajst-14953	33	59	in	in	ADP
ajst-14953	33	60	turn	turn	NOUN
ajst-14953	33	61	:	:	PUNCT
ajst-14953	33	62	select	select	VERB
ajst-14953	33	63	the	the	DET
ajst-14953	33	64	q	q	NOUN
ajst-14953	33	65	-	-	PUNCT
ajst-14953	33	66	th	th	VERB
ajst-14953	33	67	feature	feature	NOUN
ajst-14953	33	68	using	use	VERB
ajst-14953	33	69	the	the	DET
ajst-14953	33	70	"	"	PUNCT
ajst-14953	33	71	minimum	minimum	ADJ
ajst-14953	33	72	redundancymaximum	redundancymaximum	ADJ
ajst-14953	33	73	correlation	correlation	NOUN
ajst-14953	33	74	"	"	PUNCT
ajst-14953	33	75	standard	standard	ADJ
ajst-14953	33	76	strategy	strategy	NOUN
ajst-14953	33	77	:	:	PUNCT
ajst-14953	33	78			NOUN
ajst-14953	33	79			PROPN
ajst-14953	33	80			NOUN
ajst-14953	33	81			NUM
ajst-14953	33	82			NUM
ajst-14953	33	83			NUM
ajst-14953	33	84			PROPN
ajst-14953	33	85			PROPN
ajst-14953	33	86			NOUN
ajst-14953	33	87			NUM
ajst-14953	33	88			NUM
ajst-14953	33	89			ADP
ajst-14953	33	90			PROPN
ajst-14953	33	91			PROPN
ajst-14953	33	92			PROPN
ajst-14953	33	93			X
ajst-14953	33	94			PROPN
ajst-14953	33	95			PROPN
ajst-14953	33	96	ffffi	ffffi	PROPN
ajst-14953	33	97	q	q	PROPN
ajst-14953	33	98	yfii	yfii	PROPN
ajst-14953	34	1	i	i	PRON
ajst-14953	34	2	sf	sf	VERB
ajst-14953	34	3	tii	tii	PROPN
ajst-14953	34	4	mi	mi	PROPN
ajst-14953	34	5	q	q	PROPN
ajst-14953	34	6	qt	qt	PROPN
ajst-14953	34	7	|	|	NOUN
ajst-14953	34	8	;	;	PUNCT
ajst-14953	34	9	1	1	NUM
ajst-14953	34	10	1	1	NUM
ajst-14953	34	11	;	;	PUNCT
ajst-14953	34	12	maxarg	maxarg	NOUN
ajst-14953	34	13	1	1	NUM
ajst-14953	34	14	1	1	NUM
ajst-14953	34	15	in	in	ADP
ajst-14953	34	16	the	the	DET
ajst-14953	34	17	formula	formula	NOUN
ajst-14953	34	18	,	,	PUNCT
ajst-14953	34	19	term	term	NOUN
ajst-14953	34	20			NOUN
ajst-14953	34	21	yfi	yfi	NOUN
ajst-14953	35	1	i	i	PRON
ajst-14953	35	2	;	;	PUNCT
ajst-14953	35	3	is	be	AUX
ajst-14953	35	4	the	the	DET
ajst-14953	35	5	"	"	PUNCT
ajst-14953	35	6	maximum	maximum	ADJ
ajst-14953	35	7	correlation	correlation	NOUN
ajst-14953	35	8	"	"	PUNCT
ajst-14953	35	9	condition	condition	NOUN
ajst-14953	35	10	,	,	PUNCT
ajst-14953	35	11	qi	qi	PROPN
ajst-14953	35	12	represents	represent	VERB
ajst-14953	35	13	the	the	DET
ajst-14953	35	14	mutual	mutual	ADJ
ajst-14953	35	15	information	information	NOUN
ajst-14953	35	16	of	of	ADP
ajst-14953	35	17	the	the	DET
ajst-14953	35	18	q	q	NOUN
ajst-14953	35	19	features	feature	NOUN
ajst-14953	35	20	,	,	PUNCT
ajst-14953	35	21	and	and	CCONJ
ajst-14953	35	22	1qs	1qs	NUM
ajst-14953	35	23	represents	represent	VERB
ajst-14953	35	24	the	the	DET
ajst-14953	35	25	feature	feature	NOUN
ajst-14953	35	26	subset	subset	NOUN
ajst-14953	35	27	containing	contain	VERB
ajst-14953	35	28	the	the	DET
ajst-14953	35	29	selected	select	VERB
ajst-14953	35	30	1q	1q	PROPN
ajst-14953	35	31	features	feature	NOUN
ajst-14953	35	32	.	.	PUNCT
ajst-14953	36	1	4	4	X
ajst-14953	36	2	)	)	PUNCT
ajst-14953	36	3	output	output	NOUN
ajst-14953	36	4	the	the	DET
ajst-14953	36	5	selected	select	VERB
ajst-14953	36	6	feature	feature	NOUN
ajst-14953	36	7	subset	subset	VERB
ajst-14953	36	8	s.	s.	PROPN
ajst-14953	36	9	figure	figure	PROPN
ajst-14953	36	10	1	1	NUM
ajst-14953	36	11	.	.	PUNCT
ajst-14953	37	1	structure	structure	NOUN
ajst-14953	37	2	of	of	ADP
ajst-14953	37	3	the	the	DET
ajst-14953	37	4	intrusion	intrusion	NOUN
ajst-14953	37	5	detection	detection	NOUN
ajst-14953	37	6	model	model	NOUN
ajst-14953	37	7	based	base	VERB
ajst-14953	37	8	on	on	ADP
ajst-14953	37	9	the	the	DET
ajst-14953	37	10	fast	fast	ADJ
ajst-14953	37	11	knn	knn	PROPN
ajst-14953	37	12	algorithm	algorithm	PROPN
ajst-14953	37	13	the	the	DET
ajst-14953	37	14	fast	fast	ADJ
ajst-14953	37	15	knn	knn	PROPN
ajst-14953	37	16	algorithm	algorithm	PROPN
ajst-14953	37	17	is	be	AUX
ajst-14953	37	18	used	use	VERB
ajst-14953	37	19	to	to	PART
ajst-14953	37	20	classify	classify	VERB
ajst-14953	37	21	and	and	CCONJ
ajst-14953	37	22	detect	detect	VERB
ajst-14953	37	23	the	the	DET
ajst-14953	37	24	data	datum	NOUN
ajst-14953	37	25	after	after	ADP
ajst-14953	37	26	feature	feature	NOUN
ajst-14953	37	27	reduction	reduction	NOUN
ajst-14953	37	28	:	:	PUNCT
ajst-14953	37	29	(	(	PUNCT
ajst-14953	37	30	1	1	X
ajst-14953	37	31	)	)	PUNCT
ajst-14953	37	32	obtain	obtain	VERB
ajst-14953	37	33	the	the	DET
ajst-14953	37	34	training	training	NOUN
ajst-14953	37	35	sample	sample	NOUN
ajst-14953	37	36	set	set	NOUN
ajst-14953	37	37	,	,	PUNCT
ajst-14953	37	38	and	and	CCONJ
ajst-14953	37	39	delete	delete	VERB
ajst-14953	37	40	the	the	DET
ajst-14953	37	41	duplicate	duplicate	ADJ
ajst-14953	37	42	data	datum	NOUN
ajst-14953	37	43	in	in	ADP
ajst-14953	37	44	the	the	DET
ajst-14953	37	45	training	training	NOUN
ajst-14953	37	46	sample	sample	NOUN
ajst-14953	37	47	set	set	NOUN
ajst-14953	37	48	;	;	PUNCT
ajst-14953	37	49	(	(	PUNCT
ajst-14953	37	50	2	2	X
ajst-14953	37	51	)	)	PUNCT
ajst-14953	37	52	establish	establish	VERB
ajst-14953	37	53	the	the	DET
ajst-14953	37	54	index	index	NOUN
ajst-14953	37	55	model	model	NOUN
ajst-14953	37	56	;	;	PUNCT
ajst-14953	37	57	(	(	PUNCT
ajst-14953	37	58	3	3	X
ajst-14953	37	59	)	)	PUNCT
ajst-14953	37	60	for	for	SCONJ
ajst-14953	37	61	the	the	DET
ajst-14953	37	62	current	current	ADJ
ajst-14953	37	63	samples	sample	NOUN
ajst-14953	37	64	to	to	PART
ajst-14953	37	65	be	be	AUX
ajst-14953	37	66	classified	classify	VERB
ajst-14953	37	67	,	,	PUNCT
ajst-14953	37	68	judge	judge	VERB
ajst-14953	37	69	whether	whether	SCONJ
ajst-14953	37	70	there	there	PRON
ajst-14953	37	71	are	be	VERB
ajst-14953	37	72	samples	sample	NOUN
ajst-14953	37	73	with	with	ADP
ajst-14953	37	74	the	the	DET
ajst-14953	37	75	same	same	ADJ
ajst-14953	37	76	samples	sample	NOUN
ajst-14953	37	77	in	in	ADP
ajst-14953	37	78	the	the	DET
ajst-14953	37	79	classified	classified	ADJ
ajst-14953	37	80	sample	sample	NOUN
ajst-14953	37	81	set	set	VERB
ajst-14953	37	82	as	as	SCONJ
ajst-14953	37	83	the	the	DET
ajst-14953	37	84	samples	sample	NOUN
ajst-14953	37	85	to	to	PART
ajst-14953	37	86	be	be	AUX
ajst-14953	37	87	classified	classify	VERB
ajst-14953	37	88	;	;	PUNCT
ajst-14953	37	89	if	if	SCONJ
ajst-14953	37	90	so	so	ADV
ajst-14953	37	91	,	,	PUNCT
ajst-14953	37	92	the	the	DET
ajst-14953	37	93	category	category	NOUN
ajst-14953	37	94	identification	identification	NOUN
ajst-14953	37	95	of	of	ADP
ajst-14953	37	96	the	the	DET
ajst-14953	37	97	same	same	ADJ
ajst-14953	37	98	classified	classified	ADJ
ajst-14953	37	99	samples	sample	NOUN
ajst-14953	37	100	will	will	AUX
ajst-14953	37	101	be	be	AUX
ajst-14953	37	102	directly	directly	ADV
ajst-14953	37	103	output	output	ADJ
ajst-14953	37	104	;	;	PUNCT
ajst-14953	37	105	if	if	SCONJ
ajst-14953	37	106	no	no	ADV
ajst-14953	37	107	,	,	PUNCT
ajst-14953	37	108	perform	perform	VERB
ajst-14953	37	109	step	step	NOUN
ajst-14953	37	110	4	4	NUM
ajst-14953	37	111	)	)	PUNCT
ajst-14953	37	112	;	;	PUNCT
ajst-14953	37	113	(	(	PUNCT
ajst-14953	37	114	4	4	X
ajst-14953	37	115	)	)	PUNCT
ajst-14953	37	116	quickly	quickly	ADV
ajst-14953	37	117	search	search	VERB
ajst-14953	37	118	for	for	ADP
ajst-14953	37	119	the	the	DET
ajst-14953	37	120	k	k	PROPN
ajst-14953	37	121	nearest	near	ADJ
ajst-14953	37	122	neighbors	neighbor	NOUN
ajst-14953	37	123	of	of	ADP
ajst-14953	37	124	the	the	DET
ajst-14953	37	125	samples	sample	NOUN
ajst-14953	37	126	to	to	PART
ajst-14953	37	127	be	be	AUX
ajst-14953	37	128	classified	classify	VERB
ajst-14953	37	129	in	in	ADP
ajst-14953	37	130	the	the	DET
ajst-14953	37	131	training	training	NOUN
ajst-14953	37	132	sample	sample	NOUN
ajst-14953	37	133	set	set	VERB
ajst-14953	37	134	according	accord	VERB
ajst-14953	37	135	to	to	ADP
ajst-14953	37	136	the	the	DET
ajst-14953	37	137	established	establish	VERB
ajst-14953	37	138	index	index	NOUN
ajst-14953	37	139	model	model	NOUN
ajst-14953	37	140	;	;	PUNCT
ajst-14953	37	141	(	(	PUNCT
ajst-14953	37	142	5	5	X
ajst-14953	37	143	)	)	PUNCT
ajst-14953	37	144	output	output	NOUN
ajst-14953	37	145	the	the	DET
ajst-14953	37	146	category	category	NOUN
ajst-14953	37	147	identification	identification	NOUN
ajst-14953	37	148	of	of	ADP
ajst-14953	37	149	the	the	DET
ajst-14953	37	150	sample	sample	NOUN
ajst-14953	37	151	to	to	PART
ajst-14953	37	152	be	be	AUX
ajst-14953	37	153	classified	classify	VERB
ajst-14953	37	154	according	accord	VERB
ajst-14953	37	155	to	to	ADP
ajst-14953	37	156	the	the	DET
ajst-14953	37	157	k	k	PROPN
ajst-14953	37	158	nearest	near	ADJ
ajst-14953	37	159	neighbors	neighbor	NOUN
ajst-14953	37	160	.	.	PUNCT
ajst-14953	38	1	3	3	X
ajst-14953	38	2	.	.	X
ajst-14953	38	3	experiments	experiment	NOUN
ajst-14953	38	4	in	in	ADP
ajst-14953	38	5	the	the	DET
ajst-14953	38	6	experimental	experimental	ADJ
ajst-14953	38	7	section	section	NOUN
ajst-14953	38	8	,	,	PUNCT
ajst-14953	38	9	the	the	DET
ajst-14953	38	10	proposed	propose	VERB
ajst-14953	38	11	method	method	NOUN
ajst-14953	38	12	in	in	ADP
ajst-14953	38	13	this	this	DET
ajst-14953	38	14	paper	paper	NOUN
ajst-14953	38	15	is	be	AUX
ajst-14953	38	16	compared	compare	VERB
ajst-14953	38	17	with	with	ADP
ajst-14953	38	18	the	the	DET
ajst-14953	38	19	conventional	conventional	ADJ
ajst-14953	38	20	knn	knn	PROPN
ajst-14953	38	21	algorithm	algorithm	PROPN
ajst-14953	38	22	.	.	PUNCT
ajst-14953	39	1	experiments	experiment	NOUN
ajst-14953	39	2	were	be	AUX
ajst-14953	39	3	performed	perform	VERB
ajst-14953	39	4	based	base	VERB
ajst-14953	39	5	on	on	ADP
ajst-14953	39	6	the	the	DET
ajst-14953	39	7	kdd	kdd	PROPN
ajst-14953	39	8	99	99	NUM
ajst-14953	39	9	dataset	dataset	NOUN
ajst-14953	39	10	.	.	PUNCT
ajst-14953	40	1	the	the	DET
ajst-14953	40	2	evaluation	evaluation	NOUN
ajst-14953	40	3	criteria	criterion	NOUN
ajst-14953	40	4	include	include	VERB
ajst-14953	40	5	classification	classification	NOUN
ajst-14953	40	6	accuracy	accuracy	NOUN
ajst-14953	40	7	,	,	PUNCT
ajst-14953	40	8	misdetection	misdetection	NOUN
ajst-14953	40	9	rate	rate	NOUN
ajst-14953	40	10	and	and	CCONJ
ajst-14953	40	11	missed	miss	VERB
ajst-14953	40	12	detection	detection	NOUN
ajst-14953	40	13	rate	rate	NOUN
ajst-14953	40	14	.	.	PUNCT
ajst-14953	41	1	as	as	SCONJ
ajst-14953	41	2	the	the	DET
ajst-14953	41	3	original	original	ADJ
ajst-14953	41	4	kdd	kdd	PROPN
ajst-14953	41	5	cup99	cup99	PROPN
ajst-14953	41	6	dataset	dataset	NOUN
ajst-14953	41	7	is	be	AUX
ajst-14953	41	8	too	too	ADV
ajst-14953	41	9	large	large	ADJ
ajst-14953	41	10	,	,	PUNCT
ajst-14953	41	11	only	only	ADV
ajst-14953	41	12	80056	80056	NUM
ajst-14953	41	13	of	of	ADP
ajst-14953	41	14	them	they	PRON
ajst-14953	41	15	are	be	AUX
ajst-14953	41	16	randomly	randomly	ADV
ajst-14953	41	17	selected	select	VERB
ajst-14953	41	18	for	for	ADP
ajst-14953	41	19	the	the	DET
ajst-14953	41	20	study	study	NOUN
ajst-14953	41	21	,	,	PUNCT
ajst-14953	41	22	40000	40000	NUM
ajst-14953	41	23	are	be	AUX
ajst-14953	41	24	used	use	VERB
ajst-14953	41	25	as	as	ADP
ajst-14953	41	26	training	training	NOUN
ajst-14953	41	27	samples	sample	NOUN
ajst-14953	41	28	and	and	CCONJ
ajst-14953	41	29	40056	40056	NUM
ajst-14953	41	30	were	be	AUX
ajst-14953	41	31	used	use	VERB
ajst-14953	41	32	as	as	ADP
ajst-14953	41	33	test	test	NOUN
ajst-14953	41	34	samples	sample	NOUN
ajst-14953	41	35	.	.	PUNCT
ajst-14953	42	1	then	then	ADV
ajst-14953	42	2	,	,	PUNCT
ajst-14953	42	3	the	the	DET
ajst-14953	42	4	selected	select	VERB
ajst-14953	42	5	training	training	NOUN
ajst-14953	42	6	sample	sample	NOUN
ajst-14953	42	7	data	datum	NOUN
ajst-14953	42	8	and	and	CCONJ
ajst-14953	42	9	test	test	NOUN
ajst-14953	42	10	sample	sample	NOUN
ajst-14953	42	11	data	datum	NOUN
ajst-14953	42	12	are	be	AUX
ajst-14953	42	13	preprocessed	preprocesse	VERB
ajst-14953	42	14	in	in	ADP
ajst-14953	42	15	the	the	DET
ajst-14953	42	16	same	same	ADJ
ajst-14953	42	17	mode	mode	NOUN
ajst-14953	42	18	,	,	PUNCT
ajst-14953	42	19	including	include	VERB
ajst-14953	42	20	:	:	PUNCT
ajst-14953	42	21	discretization	discretization	NOUN
ajst-14953	42	22	of	of	ADP
ajst-14953	42	23	continuous	continuous	ADJ
ajst-14953	42	24	data	datum	NOUN
ajst-14953	42	25	and	and	CCONJ
ajst-14953	42	26	digitization	digitization	NOUN
ajst-14953	42	27	of	of	ADP
ajst-14953	42	28	character	character	NOUN
ajst-14953	42	29	data	datum	NOUN
ajst-14953	42	30	.	.	PUNCT
ajst-14953	43	1	the	the	DET
ajst-14953	43	2	feature	feature	NOUN
ajst-14953	43	3	reduction	reduction	NOUN
ajst-14953	43	4	algorithm	algorithm	NOUN
ajst-14953	43	5	based	base	VERB
ajst-14953	43	6	on	on	ADP
ajst-14953	43	7	mutual	mutual	ADJ
ajst-14953	43	8	information	information	NOUN
ajst-14953	43	9	is	be	AUX
ajst-14953	43	10	then	then	ADV
ajst-14953	43	11	used	use	VERB
ajst-14953	43	12	to	to	PART
ajst-14953	43	13	reduce	reduce	VERB
ajst-14953	43	14	the	the	DET
ajst-14953	43	15	collated	collated	ADJ
ajst-14953	43	16	sample	sample	NOUN
ajst-14953	43	17	set	set	VERB
ajst-14953	43	18	with	with	ADP
ajst-14953	43	19	a	a	DET
ajst-14953	43	20	feature	feature	NOUN
ajst-14953	43	21	space	space	NOUN
ajst-14953	43	22	of	of	ADP
ajst-14953	43	23	41	41	NUM
ajst-14953	43	24	dimensions	dimension	NOUN
ajst-14953	43	25	.	.	PUNCT
ajst-14953	44	1	then	then	ADV
ajst-14953	44	2	the	the	DET
ajst-14953	44	3	training	training	NOUN
ajst-14953	44	4	sample	sample	NOUN
ajst-14953	44	5	data	datum	NOUN
ajst-14953	44	6	set	set	VERB
ajst-14953	44	7	is	be	AUX
ajst-14953	44	8	repeated	repeat	VERB
ajst-14953	44	9	by	by	ADP
ajst-14953	44	10	reducing	reduce	VERB
ajst-14953	44	11	the	the	DET
ajst-14953	44	12	data	datum	NOUN
ajst-14953	44	13	,	,	PUNCT
ajst-14953	44	14	the	the	DET
ajst-14953	44	15	number	number	NOUN
ajst-14953	44	16	of	of	ADP
ajst-14953	44	17	training	training	NOUN
ajst-14953	44	18	samples	sample	NOUN
ajst-14953	44	19	is	be	AUX
ajst-14953	44	20	greatly	greatly	ADV
ajst-14953	44	21	reduced	reduce	VERB
ajst-14953	44	22	,	,	PUNCT
ajst-14953	44	23	and	and	CCONJ
ajst-14953	44	24	then	then	ADV
ajst-14953	44	25	the	the	DET
ajst-14953	44	26	improved	improved	ADJ
ajst-14953	44	27	fast	fast	ADJ
ajst-14953	44	28	knn	knn	NOUN
ajst-14953	44	29	classification	classification	NOUN
ajst-14953	44	30	algorithm	algorithm	NOUN
ajst-14953	44	31	(	(	PUNCT
ajst-14953	44	32	i.	i.	PROPN
ajst-14953	44	33	e.	e.	PROPN
ajst-14953	44	34	,	,	PUNCT
ajst-14953	44	35	index	index	NOUN
ajst-14953	44	36	model	model	NOUN
ajst-14953	44	37	,	,	PUNCT
ajst-14953	44	38	cache	cache	NOUN
ajst-14953	44	39	function	function	NOUN
ajst-14953	44	40	)	)	PUNCT
ajst-14953	44	41	is	be	AUX
ajst-14953	44	42	used	use	VERB
ajst-14953	44	43	for	for	ADP
ajst-14953	44	44	classification	classification	NOUN
ajst-14953	44	45	detection	detection	NOUN
ajst-14953	44	46	,	,	PUNCT
ajst-14953	44	47	and	and	CCONJ
ajst-14953	44	48	finally	finally	ADV
ajst-14953	44	49	the	the	DET
ajst-14953	44	50	required	require	VERB
ajst-14953	44	51	results	result	NOUN
ajst-14953	44	52	are	be	AUX
ajst-14953	44	53	obtained	obtain	VERB
ajst-14953	44	54	.	.	PUNCT
ajst-14953	45	1	size	size	NOUN
ajst-14953	45	2	reduction	reduction	NOUN
ajst-14953	45	3	of	of	ADP
ajst-14953	45	4	the	the	DET
ajst-14953	45	5	preprocessed	preprocesse	VERB
ajst-14953	45	6	kdd	kdd	PROPN
ajst-14953	45	7	cup99	cup99	PROPN
ajst-14953	45	8	dataset	dataset	NOUN
ajst-14953	45	9	using	use	VERB
ajst-14953	45	10	a	a	DET
ajst-14953	45	11	mutual	mutual	ADJ
ajst-14953	45	12	information	information	NOUN
ajst-14953	45	13	-	-	PUNCT
ajst-14953	45	14	based	base	VERB
ajst-14953	45	15	feature	feature	NOUN
ajst-14953	45	16	reduction	reduction	NOUN
ajst-14953	45	17	method	method	NOUN
ajst-14953	45	18	.	.	PUNCT
ajst-14953	46	1	in	in	ADP
ajst-14953	46	2	the	the	DET
ajst-14953	46	3	experiment	experiment	NOUN
ajst-14953	46	4	,	,	PUNCT
ajst-14953	46	5	no	no	ADV
ajst-14953	46	6	matter	matter	ADV
ajst-14953	46	7	the	the	DET
ajst-14953	46	8	2	2	NUM
ajst-14953	46	9	classification	classification	NOUN
ajst-14953	46	10	data	datum	NOUN
ajst-14953	46	11	set	set	VERB
ajst-14953	46	12	or	or	CCONJ
ajst-14953	46	13	the	the	DET
ajst-14953	46	14	5	5	NUM
ajst-14953	46	15	classification	classification	NOUN
ajst-14953	46	16	data	datum	NOUN
ajst-14953	46	17	set	set	NOUN
ajst-14953	46	18	,	,	PUNCT
ajst-14953	46	19	when	when	SCONJ
ajst-14953	46	20	the	the	DET
ajst-14953	46	21	features	feature	NOUN
ajst-14953	46	22	are	be	AUX
ajst-14953	46	23	taken	take	VERB
ajst-14953	46	24	above	above	ADP
ajst-14953	46	25	5	5	NUM
ajst-14953	46	26	dimensions	dimension	NOUN
ajst-14953	46	27	,	,	PUNCT
ajst-14953	46	28	the	the	DET
ajst-14953	46	29	libsvm	libsvm	NOUN
ajst-14953	46	30	classification	classification	NOUN
ajst-14953	46	31	tool	tool	NOUN
ajst-14953	46	32	can	can	AUX
ajst-14953	46	33	be	be	AUX
ajst-14953	46	34	maintained	maintain	VERB
ajst-14953	46	35	above	above	ADP
ajst-14953	46	36	98	98	NUM
ajst-14953	46	37	%	%	NOUN
ajst-14953	46	38	.	.	PUNCT
ajst-14953	47	1	to	to	PART
ajst-14953	47	2	speed	speed	VERB
ajst-14953	47	3	up	up	ADP
ajst-14953	47	4	the	the	DET
ajst-14953	47	5	classification	classification	NOUN
ajst-14953	47	6	when	when	SCONJ
ajst-14953	47	7	using	use	VERB
ajst-14953	47	8	knn	knn	PROPN
ajst-14953	47	9	classification	classification	NOUN
ajst-14953	47	10	,	,	PUNCT
ajst-14953	47	11	we	we	PRON
ajst-14953	47	12	choose	choose	VERB
ajst-14953	47	13	to	to	PART
ajst-14953	47	14	reduce	reduce	VERB
ajst-14953	47	15	the	the	DET
ajst-14953	47	16	feature	feature	NOUN
ajst-14953	47	17	dimension	dimension	NOUN
ajst-14953	47	18	to	to	ADP
ajst-14953	47	19	5	5	NUM
ajst-14953	47	20	dimensions	dimension	NOUN
ajst-14953	47	21	.	.	PUNCT
ajst-14953	48	1	and	and	CCONJ
ajst-14953	48	2	it	it	PRON
ajst-14953	48	3	was	be	AUX
ajst-14953	48	4	then	then	ADV
ajst-14953	48	5	used	use	VERB
ajst-14953	48	6	for	for	ADP
ajst-14953	48	7	subsequent	subsequent	ADJ
ajst-14953	48	8	invasion	invasion	NOUN
ajst-14953	48	9	detection	detection	NOUN
ajst-14953	48	10	pattern	pattern	NOUN
ajst-14953	48	11	classification	classification	NOUN
ajst-14953	48	12	studies	study	NOUN
ajst-14953	48	13	.	.	PUNCT
ajst-14953	49	1	3.1	3.1	NUM
ajst-14953	49	2	.	.	PUNCT
ajst-14953	49	3	verify	verify	VERB
ajst-14953	49	4	the	the	DET
ajst-14953	49	5	algorithm	algorithm	NOUN
ajst-14953	49	6	learning	learn	VERB
ajst-14953	49	7	speed	speed	NOUN
ajst-14953	49	8	and	and	CCONJ
ajst-14953	49	9	training	training	NOUN
ajst-14953	49	10	sample	sample	NOUN
ajst-14953	49	11	size	size	NOUN
ajst-14953	49	12	correlation	correlation	NOUN
ajst-14953	49	13	to	to	PART
ajst-14953	49	14	verify	verify	VERB
ajst-14953	49	15	the	the	DET
ajst-14953	49	16	correlation	correlation	NOUN
ajst-14953	49	17	of	of	ADP
ajst-14953	49	18	the	the	DET
ajst-14953	49	19	learning	learning	NOUN
ajst-14953	49	20	speed	speed	NOUN
ajst-14953	49	21	of	of	ADP
ajst-14953	49	22	the	the	DET
ajst-14953	49	23	knn	knn	PROPN
ajst-14953	49	24	algorithm	algorithm	PROPN
ajst-14953	49	25	with	with	ADP
ajst-14953	49	26	the	the	DET
ajst-14953	49	27	training	training	NOUN
ajst-14953	49	28	sample	sample	NOUN
ajst-14953	49	29	library	library	NOUN
ajst-14953	49	30	size	size	NOUN
ajst-14953	49	31	.	.	PUNCT
ajst-14953	50	1	now	now	ADV
ajst-14953	50	2	,	,	PUNCT
ajst-14953	50	3	based	base	VERB
ajst-14953	50	4	on	on	ADP
ajst-14953	50	5	the	the	DET
ajst-14953	50	6	uncensored	uncensored	ADJ
ajst-14953	50	7	training	training	NOUN
ajst-14953	50	8	sample	sample	NOUN
ajst-14953	50	9	library	library	NOUN
ajst-14953	50	10	and	and	CCONJ
ajst-14953	50	11	the	the	DET
ajst-14953	50	12	deleted	delete	VERB
ajst-14953	50	13	training	training	NOUN
ajst-14953	50	14	sample	sample	NOUN
ajst-14953	50	15	library	library	NOUN
ajst-14953	50	16	as	as	ADP
ajst-14953	50	17	experimental	experimental	ADJ
ajst-14953	50	18	data	datum	NOUN
ajst-14953	50	19	,	,	PUNCT
ajst-14953	50	20	the	the	DET
ajst-14953	50	21	knn	knn	PROPN
ajst-14953	50	22	algorithm	algorithm	PROPN
ajst-14953	50	23	is	be	AUX
ajst-14953	50	24	uniformly	uniformly	ADV
ajst-14953	50	25	used	use	VERB
ajst-14953	50	26	for	for	ADP
ajst-14953	50	27	classification	classification	NOUN
ajst-14953	50	28	learning	learning	NOUN
ajst-14953	50	29	.	.	PUNCT
ajst-14953	51	1	in	in	ADP
ajst-14953	51	2	order	order	NOUN
ajst-14953	51	3	to	to	PART
ajst-14953	51	4	save	save	VERB
ajst-14953	51	5	the	the	DET
ajst-14953	51	6	experimental	experimental	ADJ
ajst-14953	51	7	time	time	NOUN
ajst-14953	51	8	,	,	PUNCT
ajst-14953	51	9	only	only	ADV
ajst-14953	51	10	the	the	DET
ajst-14953	51	11	k	k	PROPN
ajst-14953	51	12	values	value	NOUN
ajst-14953	51	13	are	be	AUX
ajst-14953	51	14	4	4	NUM
ajst-14953	51	15	and	and	CCONJ
ajst-14953	51	16	10	10	NUM
ajst-14953	51	17	,	,	PUNCT
ajst-14953	51	18	and	and	CCONJ
ajst-14953	51	19	the	the	DET
ajst-14953	51	20	effect	effect	NOUN
ajst-14953	51	21	of	of	ADP
ajst-14953	51	22	the	the	DET
ajst-14953	51	23	deletion	deletion	NOUN
ajst-14953	51	24	of	of	ADP
ajst-14953	51	25	the	the	DET
ajst-14953	51	26	training	training	NOUN
ajst-14953	51	27	sample	sample	NOUN
ajst-14953	51	28	library	library	NOUN
ajst-14953	51	29	on	on	ADP
ajst-14953	51	30	the	the	DET
ajst-14953	51	31	learning	learning	NOUN
ajst-14953	51	32	speed	speed	NOUN
ajst-14953	51	33	is	be	AUX
ajst-14953	51	34	studied	study	VERB
ajst-14953	51	35	.	.	PUNCT
ajst-14953	52	1	the	the	DET
ajst-14953	52	2	experimental	experimental	ADJ
ajst-14953	52	3	results	result	NOUN
ajst-14953	52	4	are	be	AUX
ajst-14953	52	5	shown	show	VERB
ajst-14953	52	6	in	in	ADP
ajst-14953	52	7	table	table	NOUN
ajst-14953	52	8	1	1	NUM
ajst-14953	52	9	.	.	PUNCT
ajst-14953	52	10	table	table	NOUN
ajst-14953	52	11	1	1	NUM
ajst-14953	52	12	.	.	PUNCT
ajst-14953	52	13	comparison	comparison	NOUN
ajst-14953	52	14	of	of	ADP
ajst-14953	52	15	experimental	experimental	ADJ
ajst-14953	52	16	results	result	NOUN
ajst-14953	52	17	before	before	ADP
ajst-14953	52	18	and	and	CCONJ
ajst-14953	52	19	after	after	ADP
ajst-14953	52	20	deletion	deletion	NOUN
ajst-14953	52	21	of	of	ADP
ajst-14953	52	22	the	the	DET
ajst-14953	52	23	training	training	NOUN
ajst-14953	52	24	sample	sample	NOUN
ajst-14953	52	25	library	library	NOUN
ajst-14953	52	26	it	it	PRON
ajst-14953	52	27	can	can	AUX
ajst-14953	52	28	be	be	AUX
ajst-14953	52	29	seen	see	VERB
ajst-14953	52	30	from	from	ADP
ajst-14953	52	31	table	table	NOUN
ajst-14953	52	32	1	1	NUM
ajst-14953	52	33	,	,	PUNCT
ajst-14953	52	34	if	if	SCONJ
ajst-14953	52	35	the	the	DET
ajst-14953	52	36	duplicate	duplicate	ADJ
ajst-14953	52	37	data	datum	NOUN
ajst-14953	52	38	in	in	ADP
ajst-14953	52	39	the	the	DET
ajst-14953	52	40	training	training	NOUN
ajst-14953	52	41	sample	sample	NOUN
ajst-14953	52	42	library	library	NOUN
ajst-14953	52	43	is	be	AUX
ajst-14953	52	44	not	not	PART
ajst-14953	52	45	censored	censor	VERB
ajst-14953	52	46	,	,	PUNCT
ajst-14953	52	47	the	the	DET
ajst-14953	52	48	time	time	NOUN
ajst-14953	52	49	cost	cost	NOUN
ajst-14953	52	50	of	of	ADP
ajst-14953	52	51	the	the	DET
ajst-14953	52	52	whole	whole	ADJ
ajst-14953	52	53	learning	learning	NOUN
ajst-14953	52	54	process	process	NOUN
ajst-14953	52	55	is	be	AUX
ajst-14953	52	56	very	very	ADV
ajst-14953	52	57	huge	huge	ADJ
ajst-14953	52	58	,	,	PUNCT
ajst-14953	52	59	which	which	PRON
ajst-14953	52	60	is	be	AUX
ajst-14953	52	61	very	very	ADV
ajst-14953	52	62	undesirable	undesirable	ADJ
ajst-14953	52	63	.	.	PUNCT
ajst-14953	53	1	however	however	ADV
ajst-14953	53	2	,	,	PUNCT
ajst-14953	53	3	after	after	ADP
ajst-14953	53	4	deleting	delete	VERB
ajst-14953	53	5	the	the	DET
ajst-14953	53	6	duplicate	duplicate	ADJ
ajst-14953	53	7	data	datum	NOUN
ajst-14953	53	8	of	of	ADP
ajst-14953	53	9	the	the	DET
ajst-14953	53	10	training	training	NOUN
ajst-14953	53	11	sample	sample	NOUN
ajst-14953	53	12	library	library	NOUN
ajst-14953	53	13	,	,	PUNCT
ajst-14953	53	14	the	the	DET
ajst-14953	53	15	time	time	NOUN
ajst-14953	53	16	spent	spend	VERB
ajst-14953	53	17	by	by	ADP
ajst-14953	53	18	the	the	DET
ajst-14953	53	19	algorithm	algorithm	NOUN
ajst-14953	53	20	learning	learning	NOUN
ajst-14953	53	21	is	be	AUX
ajst-14953	53	22	significantly	significantly	ADV
ajst-14953	53	23	shortened	shorten	VERB
ajst-14953	53	24	by	by	ADP
ajst-14953	53	25	about	about	ADV
ajst-14953	53	26	13	13	NUM
ajst-14953	53	27	times	time	NOUN
ajst-14953	53	28	.	.	PUNCT
ajst-14953	54	1	this	this	PRON
ajst-14953	54	2	is	be	AUX
ajst-14953	54	3	because	because	SCONJ
ajst-14953	54	4	knn	knn	PROPN
ajst-14953	54	5	is	be	AUX
ajst-14953	54	6	a	a	DET
ajst-14953	54	7	pattern	pattern	NOUN
ajst-14953	54	8	classification	classification	NOUN
ajst-14953	54	9	method	method	NOUN
ajst-14953	54	10	based	base	VERB
ajst-14953	54	11	on	on	ADP
ajst-14953	54	12	distance	distance	NOUN
ajst-14953	54	13	calculation	calculation	NOUN
ajst-14953	54	14	,	,	PUNCT
ajst-14953	54	15	so	so	CCONJ
ajst-14953	54	16	the	the	PRON
ajst-14953	54	17	larger	large	ADJ
ajst-14953	54	18	the	the	DET
ajst-14953	54	19	training	training	NOUN
ajst-14953	54	20	sample	sample	NOUN
ajst-14953	54	21	library	library	NOUN
ajst-14953	54	22	,	,	PUNCT
ajst-14953	54	23	the	the	PRON
ajst-14953	54	24	larger	large	ADJ
ajst-14953	54	25	the	the	DET
ajst-14953	54	26	calculation	calculation	NOUN
ajst-14953	54	27	amount	amount	NOUN
ajst-14953	54	28	of	of	ADP
ajst-14953	54	29	knn	knn	PROPN
ajst-14953	54	30	will	will	AUX
ajst-14953	54	31	be	be	AUX
ajst-14953	54	32	,	,	PUNCT
ajst-14953	54	33	and	and	CCONJ
ajst-14953	54	34	the	the	PRON
ajst-14953	54	35	longer	long	ADJ
ajst-14953	54	36	the	the	DET
ajst-14953	54	37	corresponding	correspond	VERB
ajst-14953	54	38	classification	classification	NOUN
ajst-14953	54	39	learning	learning	NOUN
ajst-14953	54	40	time	time	NOUN
ajst-14953	54	41	will	will	AUX
ajst-14953	54	42	be	be	AUX
ajst-14953	54	43	.	.	PUNCT
ajst-14953	55	1	at	at	ADP
ajst-14953	55	2	the	the	DET
ajst-14953	55	3	same	same	ADJ
ajst-14953	55	4	time	time	NOUN
ajst-14953	55	5	,	,	PUNCT
ajst-14953	55	6	it	it	PRON
ajst-14953	55	7	can	can	AUX
ajst-14953	55	8	be	be	AUX
ajst-14953	55	9	seen	see	VERB
ajst-14953	55	10	from	from	ADP
ajst-14953	55	11	the	the	DET
ajst-14953	55	12	experimental	experimental	ADJ
ajst-14953	55	13	data	datum	NOUN
ajst-14953	55	14	that	that	SCONJ
ajst-14953	55	15	before	before	ADP
ajst-14953	55	16	and	and	CCONJ
ajst-14953	55	17	after	after	ADP
ajst-14953	55	18	the	the	DET
ajst-14953	55	19	deletion	deletion	NOUN
ajst-14953	55	20	of	of	ADP
ajst-14953	55	21	the	the	DET
ajst-14953	55	22	training	training	NOUN
ajst-14953	55	23	sample	sample	NOUN
ajst-14953	55	24	library	library	NOUN
ajst-14953	55	25	,	,	PUNCT
ajst-14953	55	26	the	the	DET
ajst-14953	55	27	classification	classification	NOUN
ajst-14953	55	28	accuracy	accuracy	NOUN
ajst-14953	55	29	of	of	ADP
ajst-14953	55	30	the	the	DET
ajst-14953	55	31	algorithm	algorithm	NOUN
ajst-14953	55	32	did	do	AUX
ajst-14953	55	33	not	not	PART
ajst-14953	55	34	change	change	VERB
ajst-14953	55	35	greatly	greatly	ADV
ajst-14953	55	36	either	either	CCONJ
ajst-14953	55	37	for	for	ADP
ajst-14953	55	38	the	the	DET
ajst-14953	55	39	2	2	NUM
ajst-14953	55	40	classification	classification	NOUN
ajst-14953	55	41	data	datum	NOUN
ajst-14953	55	42	set	set	VERB
ajst-14953	55	43	or	or	CCONJ
ajst-14953	55	44	the	the	DET
ajst-14953	55	45	5	5	NUM
ajst-14953	55	46	83	83	NUM
ajst-14953	55	47	classification	classification	NOUN
ajst-14953	55	48	data	datum	NOUN
ajst-14953	55	49	set	set	VERB
ajst-14953	55	50	.	.	PUNCT
ajst-14953	56	1	3.2	3.2	NUM
ajst-14953	56	2	.	.	PUNCT
ajst-14953	57	1	performance	performance	NOUN
ajst-14953	57	2	comparison	comparison	NOUN
ajst-14953	57	3	of	of	ADP
ajst-14953	57	4	the	the	DET
ajst-14953	57	5	fast	fast	ADJ
ajst-14953	57	6	knn	knn	NOUN
ajst-14953	57	7	algorithm	algorithm	NOUN
ajst-14953	57	8	and	and	CCONJ
ajst-14953	57	9	the	the	DET
ajst-14953	57	10	knn	knn	PROPN
ajst-14953	57	11	algorithm	algorithm	PROPN
ajst-14953	57	12	in	in	ADP
ajst-14953	57	13	order	order	NOUN
ajst-14953	57	14	to	to	PART
ajst-14953	57	15	verify	verify	VERB
ajst-14953	57	16	the	the	DET
ajst-14953	57	17	superiority	superiority	NOUN
ajst-14953	57	18	of	of	ADP
ajst-14953	57	19	the	the	DET
ajst-14953	57	20	fast	fast	ADJ
ajst-14953	57	21	knn	knn	NOUN
ajst-14953	57	22	algorithm	algorithm	NOUN
ajst-14953	57	23	in	in	ADP
ajst-14953	57	24	speed	speed	NOUN
ajst-14953	57	25	,	,	PUNCT
ajst-14953	57	26	different	different	ADJ
ajst-14953	57	27	nearest	near	ADJ
ajst-14953	57	28	neighbor	neighbor	NOUN
ajst-14953	57	29	k	k	PROPN
ajst-14953	57	30	values	value	NOUN
ajst-14953	57	31	are	be	AUX
ajst-14953	57	32	now	now	ADV
ajst-14953	57	33	selected	select	VERB
ajst-14953	57	34	,	,	PUNCT
ajst-14953	57	35	and	and	CCONJ
ajst-14953	57	36	the	the	DET
ajst-14953	57	37	knn	knn	PROPN
ajst-14953	57	38	algorithm	algorithm	PROPN
ajst-14953	57	39	and	and	CCONJ
ajst-14953	57	40	the	the	DET
ajst-14953	57	41	fast	fast	ADJ
ajst-14953	57	42	knn	knn	PROPN
ajst-14953	57	43	algorithm	algorithm	PROPN
ajst-14953	57	44	are	be	AUX
ajst-14953	57	45	used	use	VERB
ajst-14953	57	46	for	for	ADP
ajst-14953	57	47	classification	classification	NOUN
ajst-14953	57	48	learning	learning	NOUN
ajst-14953	57	49	.	.	PUNCT
ajst-14953	58	1	in	in	ADP
ajst-14953	58	2	order	order	NOUN
ajst-14953	58	3	to	to	PART
ajst-14953	58	4	save	save	VERB
ajst-14953	58	5	the	the	DET
ajst-14953	58	6	experimental	experimental	ADJ
ajst-14953	58	7	time	time	NOUN
ajst-14953	58	8	,	,	PUNCT
ajst-14953	58	9	the	the	DET
ajst-14953	58	10	two	two	NUM
ajst-14953	58	11	classification	classification	NOUN
ajst-14953	58	12	learning	learn	VERB
ajst-14953	58	13	algorithms	algorithm	NOUN
ajst-14953	58	14	use	use	VERB
ajst-14953	58	15	the	the	DET
ajst-14953	58	16	training	training	NOUN
ajst-14953	58	17	sample	sample	NOUN
ajst-14953	58	18	library	library	NOUN
ajst-14953	58	19	after	after	ADP
ajst-14953	58	20	deleting	delete	VERB
ajst-14953	58	21	the	the	DET
ajst-14953	58	22	repeated	repeat	VERB
ajst-14953	58	23	data	datum	NOUN
ajst-14953	58	24	.	.	PUNCT
ajst-14953	59	1	(	(	PUNCT
ajst-14953	59	2	1	1	X
ajst-14953	59	3	)	)	SYM
ajst-14953	59	4	2	2	NUM
ajst-14953	59	5	classification	classification	NOUN
ajst-14953	59	6	status	status	NOUN
ajst-14953	59	7	:	:	PUNCT
ajst-14953	59	8	when	when	SCONJ
ajst-14953	59	9	the	the	DET
ajst-14953	59	10	experimental	experimental	ADJ
ajst-14953	59	11	data	datum	NOUN
ajst-14953	59	12	are	be	AUX
ajst-14953	59	13	2	2	NUM
ajst-14953	59	14	classification	classification	NOUN
ajst-14953	59	15	cases	case	NOUN
ajst-14953	59	16	,	,	PUNCT
ajst-14953	59	17	which	which	PRON
ajst-14953	59	18	is	be	AUX
ajst-14953	59	19	normal	normal	ADJ
ajst-14953	59	20	and	and	CCONJ
ajst-14953	59	21	abnormal	abnormal	ADJ
ajst-14953	59	22	,	,	PUNCT
ajst-14953	59	23	the	the	DET
ajst-14953	59	24	experimental	experimental	ADJ
ajst-14953	59	25	results	result	NOUN
ajst-14953	59	26	of	of	ADP
ajst-14953	59	27	knn	knn	PROPN
ajst-14953	59	28	versus	versus	ADP
ajst-14953	59	29	fast	fast	PROPN
ajst-14953	59	30	knn	knn	PROPN
ajst-14953	59	31	are	be	AUX
ajst-14953	59	32	shown	show	VERB
ajst-14953	59	33	in	in	ADP
ajst-14953	59	34	table	table	NOUN
ajst-14953	59	35	2	2	NUM
ajst-14953	59	36	.	.	PUNCT
ajst-14953	59	37	table	table	NOUN
ajst-14953	59	38	2	2	NUM
ajst-14953	59	39	.	.	PUNCT
ajst-14953	59	40	comparison	comparison	NOUN
ajst-14953	59	41	of	of	ADP
ajst-14953	59	42	the	the	DET
ajst-14953	59	43	2	2	NUM
ajst-14953	59	44	-	-	PUNCT
ajst-14953	59	45	classification	classification	NOUN
ajst-14953	59	46	experiment	experiment	NOUN
ajst-14953	59	47	results	result	NOUN
ajst-14953	59	48	between	between	ADP
ajst-14953	59	49	knn	knn	PROPN
ajst-14953	59	50	and	and	CCONJ
ajst-14953	59	51	fast	fast	ADJ
ajst-14953	59	52	knn	knn	PROPN
ajst-14953	59	53	the	the	DET
ajst-14953	59	54	four	four	NUM
ajst-14953	59	55	performance	performance	NOUN
ajst-14953	59	56	indexes	index	NOUN
ajst-14953	59	57	of	of	ADP
ajst-14953	59	58	knn	knn	PROPN
ajst-14953	59	59	and	and	CCONJ
ajst-14953	59	60	fast	fast	PROPN
ajst-14953	59	61	knn	knn	PROPN
ajst-14953	59	62	under	under	ADP
ajst-14953	59	63	2	2	NUM
ajst-14953	59	64	classification	classification	NOUN
ajst-14953	59	65	:	:	PUNCT
ajst-14953	59	66	classification	classification	NOUN
ajst-14953	59	67	accuracy	accuracy	NOUN
ajst-14953	59	68	,	,	PUNCT
ajst-14953	59	69	false	false	ADJ
ajst-14953	59	70	detection	detection	NOUN
ajst-14953	59	71	rate	rate	NOUN
ajst-14953	59	72	,	,	PUNCT
ajst-14953	59	73	omission	omission	NOUN
ajst-14953	59	74	rate	rate	NOUN
ajst-14953	59	75	and	and	CCONJ
ajst-14953	59	76	running	running	NOUN
ajst-14953	59	77	speed	speed	NOUN
ajst-14953	59	78	.	.	PUNCT
ajst-14953	60	1	it	it	PRON
ajst-14953	60	2	can	can	AUX
ajst-14953	60	3	be	be	AUX
ajst-14953	60	4	concluded	conclude	VERB
ajst-14953	60	5	that	that	SCONJ
ajst-14953	60	6	:	:	PUNCT
ajst-14953	60	7	when	when	SCONJ
ajst-14953	60	8	k=4	k=4	PROPN
ajst-14953	60	9	,	,	PUNCT
ajst-14953	60	10	the	the	DET
ajst-14953	60	11	fast	fast	ADJ
ajst-14953	60	12	knn	knn	NOUN
ajst-14953	60	13	classification	classification	NOUN
ajst-14953	60	14	algorithm	algorithm	NOUN
ajst-14953	60	15	has	have	VERB
ajst-14953	60	16	a	a	DET
ajst-14953	60	17	relatively	relatively	ADV
ajst-14953	60	18	high	high	ADJ
ajst-14953	60	19	performance	performance	NOUN
ajst-14953	60	20	.	.	PUNCT
ajst-14953	61	1	(	(	PUNCT
ajst-14953	61	2	2	2	X
ajst-14953	61	3	)	)	SYM
ajst-14953	61	4	5	5	NUM
ajst-14953	61	5	classification	classification	NOUN
ajst-14953	61	6	status	status	NOUN
ajst-14953	61	7	:	:	PUNCT
ajst-14953	61	8	when	when	SCONJ
ajst-14953	61	9	the	the	DET
ajst-14953	61	10	experimental	experimental	ADJ
ajst-14953	61	11	data	data	NOUN
ajst-14953	61	12	is	be	AUX
ajst-14953	61	13	5	5	NUM
ajst-14953	61	14	classification	classification	NOUN
ajst-14953	61	15	cases	case	NOUN
ajst-14953	61	16	,	,	PUNCT
ajst-14953	61	17	that	that	ADV
ajst-14953	61	18	is	is	ADV
ajst-14953	61	19	,	,	PUNCT
ajst-14953	61	20	normal	normal	ADJ
ajst-14953	61	21	and	and	CCONJ
ajst-14953	61	22	4	4	NUM
ajst-14953	61	23	attack	attack	NOUN
ajst-14953	61	24	types	type	NOUN
ajst-14953	61	25	,	,	PUNCT
ajst-14953	61	26	the	the	DET
ajst-14953	61	27	experimental	experimental	ADJ
ajst-14953	61	28	results	result	NOUN
ajst-14953	61	29	of	of	ADP
ajst-14953	61	30	knn	knn	PROPN
ajst-14953	61	31	and	and	CCONJ
ajst-14953	61	32	fast	fast	ADJ
ajst-14953	61	33	knn	knn	PROPN
ajst-14953	61	34	are	be	AUX
ajst-14953	61	35	shown	show	VERB
ajst-14953	61	36	in	in	ADP
ajst-14953	61	37	table	table	NOUN
ajst-14953	61	38	3	3	NUM
ajst-14953	61	39	.	.	PUNCT
ajst-14953	61	40	table	table	NOUN
ajst-14953	61	41	3	3	NUM
ajst-14953	61	42	.	.	PUNCT
ajst-14953	61	43	comparison	comparison	NOUN
ajst-14953	61	44	of	of	ADP
ajst-14953	61	45	the	the	DET
ajst-14953	61	46	5	5	NUM
ajst-14953	61	47	-	-	PUNCT
ajst-14953	61	48	classification	classification	NOUN
ajst-14953	61	49	experiment	experiment	NOUN
ajst-14953	61	50	results	result	NOUN
ajst-14953	61	51	between	between	ADP
ajst-14953	61	52	knn	knn	PROPN
ajst-14953	61	53	and	and	CCONJ
ajst-14953	61	54	fast	fast	ADJ
ajst-14953	61	55	knn	knn	PROPN
ajst-14953	61	56	comprehensive	comprehensive	ADJ
ajst-14953	61	57	analysis	analysis	NOUN
ajst-14953	61	58	of	of	ADP
ajst-14953	61	59	four	four	NUM
ajst-14953	61	60	performance	performance	NOUN
ajst-14953	61	61	indicators	indicator	NOUN
ajst-14953	61	62	of	of	ADP
ajst-14953	61	63	knn	knn	PROPN
ajst-14953	61	64	and	and	CCONJ
ajst-14953	61	65	fast	fast	PROPN
ajst-14953	61	66	knn	knn	PROPN
ajst-14953	61	67	under	under	ADP
ajst-14953	61	68	5	5	NUM
ajst-14953	61	69	categories	category	NOUN
ajst-14953	61	70	:	:	PUNCT
ajst-14953	61	71	classification	classification	NOUN
ajst-14953	61	72	accuracy	accuracy	NOUN
ajst-14953	61	73	,	,	PUNCT
ajst-14953	61	74	false	false	ADJ
ajst-14953	61	75	detection	detection	NOUN
ajst-14953	61	76	rate	rate	NOUN
ajst-14953	61	77	,	,	PUNCT
ajst-14953	61	78	missed	miss	VERB
ajst-14953	61	79	detection	detection	NOUN
ajst-14953	61	80	rate	rate	NOUN
ajst-14953	61	81	and	and	CCONJ
ajst-14953	61	82	running	run	VERB
ajst-14953	61	83	speed	speed	NOUN
ajst-14953	61	84	.	.	PUNCT
ajst-14953	62	1	it	it	PRON
ajst-14953	62	2	can	can	AUX
ajst-14953	62	3	be	be	AUX
ajst-14953	62	4	concluded	conclude	VERB
ajst-14953	62	5	that	that	SCONJ
ajst-14953	62	6	when	when	SCONJ
ajst-14953	62	7	k=6	k=6	X
ajst-14953	62	8	,	,	PUNCT
ajst-14953	62	9	the	the	DET
ajst-14953	62	10	fast	fast	ADJ
ajst-14953	62	11	knn	knn	NOUN
ajst-14953	62	12	classification	classification	NOUN
ajst-14953	62	13	algorithm	algorithm	NOUN
ajst-14953	62	14	has	have	VERB
ajst-14953	62	15	relatively	relatively	ADV
ajst-14953	62	16	high	high	ADJ
ajst-14953	62	17	performance	performance	NOUN
ajst-14953	62	18	.	.	PUNCT
ajst-14953	63	1	from	from	ADP
ajst-14953	63	2	the	the	DET
ajst-14953	63	3	experimental	experimental	ADJ
ajst-14953	63	4	results	result	NOUN
ajst-14953	63	5	in	in	ADP
ajst-14953	63	6	3.1	3.1	NUM
ajst-14953	63	7	and	and	CCONJ
ajst-14953	63	8	3.2	3.2	NUM
ajst-14953	63	9	,	,	PUNCT
ajst-14953	63	10	it	it	PRON
ajst-14953	63	11	can	can	AUX
ajst-14953	63	12	be	be	AUX
ajst-14953	63	13	concluded	conclude	VERB
ajst-14953	63	14	that	that	SCONJ
ajst-14953	63	15	the	the	DET
ajst-14953	63	16	improved	improve	VERB
ajst-14953	63	17	fast	fast	ADJ
ajst-14953	63	18	knn	knn	NOUN
ajst-14953	63	19	algorithm	algorithm	NOUN
ajst-14953	63	20	not	not	PART
ajst-14953	63	21	only	only	ADV
ajst-14953	63	22	improves	improve	VERB
ajst-14953	63	23	the	the	DET
ajst-14953	63	24	classification	classification	NOUN
ajst-14953	63	25	accuracy	accuracy	NOUN
ajst-14953	63	26	,	,	PUNCT
ajst-14953	63	27	but	but	CCONJ
ajst-14953	63	28	also	also	ADV
ajst-14953	63	29	the	the	DET
ajst-14953	63	30	classification	classification	NOUN
ajst-14953	63	31	speed	speed	NOUN
ajst-14953	63	32	is	be	AUX
ajst-14953	63	33	about	about	ADV
ajst-14953	63	34	200	200	NUM
ajst-14953	63	35	times	time	NOUN
ajst-14953	63	36	faster	fast	ADJ
ajst-14953	63	37	than	than	ADP
ajst-14953	63	38	the	the	DET
ajst-14953	63	39	traditional	traditional	ADJ
ajst-14953	63	40	knn	knn	NOUN
ajst-14953	63	41	algorithm	algorithm	PROPN
ajst-14953	63	42	.	.	PUNCT
ajst-14953	64	1	4	4	X
ajst-14953	64	2	.	.	X
ajst-14953	64	3	conclusion	conclusion	NOUN
ajst-14953	64	4	there	there	PRON
ajst-14953	64	5	is	be	VERB
ajst-14953	64	6	a	a	DET
ajst-14953	64	7	large	large	ADJ
ajst-14953	64	8	amount	amount	NOUN
ajst-14953	64	9	of	of	ADP
ajst-14953	64	10	unlabeled	unlabeled	ADJ
ajst-14953	64	11	network	network	NOUN
ajst-14953	64	12	data	datum	NOUN
ajst-14953	64	13	in	in	ADP
ajst-14953	64	14	the	the	DET
ajst-14953	64	15	internet	internet	NOUN
ajst-14953	64	16	environment	environment	NOUN
ajst-14953	64	17	,	,	PUNCT
ajst-14953	64	18	and	and	CCONJ
ajst-14953	64	19	intrusion	intrusion	NOUN
ajst-14953	64	20	detection	detection	NOUN
ajst-14953	64	21	systems	system	NOUN
ajst-14953	64	22	need	need	VERB
ajst-14953	64	23	to	to	PART
ajst-14953	64	24	detect	detect	VERB
ajst-14953	64	25	and	and	CCONJ
ajst-14953	64	26	classify	classify	VERB
ajst-14953	64	27	various	various	ADJ
ajst-14953	64	28	intrusion	intrusion	NOUN
ajst-14953	64	29	events	event	NOUN
ajst-14953	64	30	.	.	PUNCT
ajst-14953	65	1	this	this	DET
ajst-14953	65	2	paper	paper	NOUN
ajst-14953	65	3	proposes	propose	VERB
ajst-14953	65	4	a	a	DET
ajst-14953	65	5	fast	fast	ADJ
ajst-14953	65	6	knn	knn	NOUN
ajst-14953	65	7	algorithm	algorithm	NOUN
ajst-14953	65	8	to	to	PART
ajst-14953	65	9	address	address	VERB
ajst-14953	65	10	the	the	DET
ajst-14953	65	11	shortcomings	shortcoming	NOUN
ajst-14953	65	12	of	of	ADP
ajst-14953	65	13	the	the	DET
ajst-14953	65	14	traditional	traditional	ADJ
ajst-14953	65	15	knn	knn	PROPN
ajst-14953	65	16	algorithm	algorithm	PROPN
ajst-14953	65	17	.	.	PUNCT
ajst-14953	66	1	this	this	DET
ajst-14953	66	2	feature	feature	NOUN
ajst-14953	66	3	reduction	reduction	NOUN
ajst-14953	66	4	algorithm	algorithm	NOUN
ajst-14953	66	5	based	base	VERB
ajst-14953	66	6	on	on	ADP
ajst-14953	66	7	mutual	mutual	ADJ
ajst-14953	66	8	information	information	NOUN
ajst-14953	66	9	reduces	reduce	VERB
ajst-14953	66	10	the	the	DET
ajst-14953	66	11	highdimensional	highdimensional	ADJ
ajst-14953	66	12	feature	feature	NOUN
ajst-14953	66	13	set	set	NOUN
ajst-14953	66	14	of	of	ADP
ajst-14953	66	15	the	the	DET
ajst-14953	66	16	original	original	ADJ
ajst-14953	66	17	data	datum	NOUN
ajst-14953	66	18	,	,	PUNCT
ajst-14953	66	19	removes	remove	VERB
ajst-14953	66	20	redundant	redundant	ADJ
ajst-14953	66	21	information	information	NOUN
ajst-14953	66	22	and	and	CCONJ
ajst-14953	66	23	interference	interference	VERB
ajst-14953	66	24	information	information	NOUN
ajst-14953	66	25	in	in	ADP
ajst-14953	66	26	the	the	DET
ajst-14953	66	27	feature	feature	NOUN
ajst-14953	66	28	set	set	NOUN
ajst-14953	66	29	,	,	PUNCT
ajst-14953	66	30	and	and	CCONJ
ajst-14953	66	31	improves	improve	VERB
ajst-14953	66	32	the	the	DET
ajst-14953	66	33	performance	performance	NOUN
ajst-14953	66	34	of	of	ADP
ajst-14953	66	35	the	the	DET
ajst-14953	66	36	knn	knn	PROPN
ajst-14953	66	37	algorithm	algorithm	PROPN
ajst-14953	66	38	.	.	PUNCT
ajst-14953	67	1	judging	judge	VERB
ajst-14953	67	2	from	from	ADP
ajst-14953	67	3	the	the	DET
ajst-14953	67	4	experimental	experimental	ADJ
ajst-14953	67	5	results	result	NOUN
ajst-14953	67	6	,	,	PUNCT
ajst-14953	67	7	the	the	DET
ajst-14953	67	8	improvements	improvement	NOUN
ajst-14953	67	9	of	of	ADP
ajst-14953	67	10	the	the	DET
ajst-14953	67	11	improved	improved	ADJ
ajst-14953	67	12	algorithm	algorithm	NOUN
ajst-14953	67	13	are	be	AUX
ajst-14953	67	14	as	as	SCONJ
ajst-14953	67	15	follows	follow	VERB
ajst-14953	67	16	:	:	PUNCT
ajst-14953	67	17	by	by	ADP
ajst-14953	67	18	deleting	delete	VERB
ajst-14953	67	19	the	the	DET
ajst-14953	67	20	training	training	NOUN
ajst-14953	67	21	sample	sample	NOUN
ajst-14953	67	22	library	library	NOUN
ajst-14953	67	23	and	and	CCONJ
ajst-14953	67	24	reducing	reduce	VERB
ajst-14953	67	25	the	the	DET
ajst-14953	67	26	training	training	NOUN
ajst-14953	67	27	sample	sample	NOUN
ajst-14953	67	28	set	set	NOUN
ajst-14953	67	29	,	,	PUNCT
ajst-14953	67	30	the	the	DET
ajst-14953	67	31	consumption	consumption	NOUN
ajst-14953	67	32	of	of	ADP
ajst-14953	67	33	algorithm	algorithm	NOUN
ajst-14953	67	34	learning	learning	NOUN
ajst-14953	67	35	time	time	NOUN
ajst-14953	67	36	is	be	AUX
ajst-14953	67	37	reduced	reduce	VERB
ajst-14953	67	38	to	to	ADP
ajst-14953	67	39	a	a	DET
ajst-14953	67	40	large	large	ADJ
ajst-14953	67	41	extent	extent	NOUN
ajst-14953	67	42	and	and	CCONJ
ajst-14953	67	43	the	the	DET
ajst-14953	67	44	efficiency	efficiency	NOUN
ajst-14953	67	45	is	be	AUX
ajst-14953	67	46	accelerated	accelerate	VERB
ajst-14953	67	47	.	.	PUNCT
ajst-14953	68	1	by	by	ADP
ajst-14953	68	2	establishing	establish	VERB
ajst-14953	68	3	an	an	DET
ajst-14953	68	4	index	index	NOUN
ajst-14953	68	5	model	model	NOUN
ajst-14953	68	6	and	and	CCONJ
ajst-14953	68	7	using	use	VERB
ajst-14953	68	8	caching	cache	VERB
ajst-14953	68	9	technology	technology	NOUN
ajst-14953	68	10	,	,	PUNCT
ajst-14953	68	11	the	the	DET
ajst-14953	68	12	search	search	NOUN
ajst-14953	68	13	range	range	NOUN
ajst-14953	68	14	and	and	CCONJ
ajst-14953	68	15	the	the	DET
ajst-14953	68	16	number	number	NOUN
ajst-14953	68	17	of	of	ADP
ajst-14953	68	18	disk	disk	NOUN
ajst-14953	68	19	starts	start	NOUN
ajst-14953	68	20	are	be	AUX
ajst-14953	68	21	reduced	reduce	VERB
ajst-14953	68	22	,	,	PUNCT
ajst-14953	68	23	and	and	CCONJ
ajst-14953	68	24	the	the	DET
ajst-14953	68	25	time	time	NOUN
ajst-14953	68	26	to	to	PART
ajst-14953	68	27	search	search	VERB
ajst-14953	68	28	for	for	ADP
ajst-14953	68	29	k	k	PROPN
ajst-14953	68	30	nearest	near	ADJ
ajst-14953	68	31	neighbors	neighbor	NOUN
ajst-14953	68	32	is	be	AUX
ajst-14953	68	33	reduced	reduce	VERB
ajst-14953	68	34	,	,	PUNCT
ajst-14953	68	35	thereby	thereby	ADV
ajst-14953	68	36	greatly	greatly	ADV
ajst-14953	68	37	speeding	speed	VERB
ajst-14953	68	38	up	up	ADP
ajst-14953	68	39	classification	classification	NOUN
ajst-14953	68	40	,	,	PUNCT
ajst-14953	68	41	improving	improve	VERB
ajst-14953	68	42	the	the	DET
ajst-14953	68	43	efficiency	efficiency	NOUN
ajst-14953	68	44	of	of	ADP
ajst-14953	68	45	the	the	DET
ajst-14953	68	46	knn	knn	PROPN
ajst-14953	68	47	algorithm	algorithm	PROPN
ajst-14953	68	48	,	,	PUNCT
ajst-14953	68	49	and	and	CCONJ
ajst-14953	68	50	shortening	shorten	VERB
ajst-14953	68	51	the	the	DET
ajst-14953	68	52	classification	classification	NOUN
ajst-14953	68	53	time	time	NOUN
ajst-14953	68	54	.	.	PUNCT
ajst-14953	69	1	references	reference	NOUN
ajst-14953	69	2	[	[	X
ajst-14953	69	3	1	1	NUM
ajst-14953	69	4	]	]	X
ajst-14953	69	5	guo	guo	PROPN
ajst-14953	69	6	,	,	PUNCT
ajst-14953	69	7	gongde	gongde	NOUN
ajst-14953	69	8	,	,	PUNCT
ajst-14953	69	9	et	et	PROPN
ajst-14953	69	10	al	al	PROPN
ajst-14953	69	11	.	.	PUNCT
ajst-14953	70	1	"	"	PUNCT
ajst-14953	70	2	knn	knn	PROPN
ajst-14953	70	3	model	model	NOUN
ajst-14953	70	4	-	-	PUNCT
ajst-14953	70	5	based	base	VERB
ajst-14953	70	6	approach	approach	NOUN
ajst-14953	70	7	in	in	ADP
ajst-14953	70	8	classification	classification	NOUN
ajst-14953	70	9	.	.	PUNCT
ajst-14953	70	10	"	"	PUNCT
ajst-14953	71	1	on	on	ADP
ajst-14953	71	2	the	the	DET
ajst-14953	71	3	move	move	NOUN
ajst-14953	71	4	to	to	ADP
ajst-14953	71	5	meaningful	meaningful	ADJ
ajst-14953	71	6	internet	internet	NOUN
ajst-14953	71	7	systems	system	NOUN
ajst-14953	71	8	2003	2003	NUM
ajst-14953	71	9	:	:	PUNCT
ajst-14953	71	10	coopis	coopis	PROPN
ajst-14953	71	11	,	,	PUNCT
ajst-14953	71	12	doa	doa	NOUN
ajst-14953	71	13	,	,	PUNCT
ajst-14953	71	14	and	and	CCONJ
ajst-14953	71	15	odbase	odbase	NOUN
ajst-14953	71	16	:	:	PUNCT
ajst-14953	71	17	otm	otm	NOUN
ajst-14953	71	18	confederated	confederate	VERB
ajst-14953	71	19	international	international	ADJ
ajst-14953	71	20	conferences	conference	NOUN
ajst-14953	71	21	,	,	PUNCT
ajst-14953	71	22	coopis	coopis	NOUN
ajst-14953	71	23	,	,	PUNCT
ajst-14953	71	24	doa	doa	NOUN
ajst-14953	71	25	,	,	PUNCT
ajst-14953	71	26	and	and	CCONJ
ajst-14953	71	27	odbase	odbase	NOUN
ajst-14953	71	28	2003	2003	NUM
ajst-14953	71	29	,	,	PUNCT
ajst-14953	71	30	catania	catania	PROPN
ajst-14953	71	31	,	,	PUNCT
ajst-14953	71	32	sicily	sicily	ADV
ajst-14953	71	33	,	,	PUNCT
ajst-14953	71	34	italy	italy	PROPN
ajst-14953	71	35	,	,	PUNCT
ajst-14953	71	36	november	november	PROPN
ajst-14953	71	37	3	3	NUM
ajst-14953	71	38	-	-	SYM
ajst-14953	71	39	7	7	NUM
ajst-14953	71	40	,	,	PUNCT
ajst-14953	71	41	2003	2003	NUM
ajst-14953	71	42	.	.	PUNCT
ajst-14953	71	43	proceedings	proceeding	NOUN
ajst-14953	71	44	.	.	PUNCT
ajst-14953	72	1	springer	springer	PROPN
ajst-14953	72	2	berlin	berlin	PROPN
ajst-14953	72	3	heidelberg	heidelberg	PROPN
ajst-14953	72	4	,	,	PUNCT
ajst-14953	72	5	2003	2003	NUM
ajst-14953	72	6	.	.	PUNCT
ajst-14953	73	1	[	[	X
ajst-14953	73	2	2	2	NUM
ajst-14953	73	3	]	]	X
ajst-14953	73	4	zhang	zhang	PROPN
ajst-14953	73	5	,	,	PUNCT
ajst-14953	73	6	shichao	shichao	NOUN
ajst-14953	73	7	,	,	PUNCT
ajst-14953	73	8	et	et	PROPN
ajst-14953	73	9	al	al	PROPN
ajst-14953	73	10	.	.	PUNCT
ajst-14953	74	1	"	"	PUNCT
ajst-14953	74	2	learning	learn	VERB
ajst-14953	74	3	k	k	X
ajst-14953	74	4	for	for	ADP
ajst-14953	74	5	knn	knn	PROPN
ajst-14953	74	6	classification	classification	NOUN
ajst-14953	74	7	.	.	PUNCT
ajst-14953	74	8	"	"	PUNCT
ajst-14953	75	1	acm	acm	PROPN
ajst-14953	75	2	transactions	transaction	NOUN
ajst-14953	75	3	on	on	ADP
ajst-14953	75	4	intelligent	intelligent	ADJ
ajst-14953	75	5	systems	system	NOUN
ajst-14953	75	6	and	and	CCONJ
ajst-14953	75	7	technology	technology	NOUN
ajst-14953	75	8	(	(	PUNCT
ajst-14953	75	9	tist	tist	NOUN
ajst-14953	75	10	)	)	PUNCT
ajst-14953	75	11	8.3	8.3	NUM
ajst-14953	75	12	(	(	PUNCT
ajst-14953	75	13	2017	2017	NUM
ajst-14953	75	14	):	):	PUNCT
ajst-14953	75	15	1	1	NUM
ajst-14953	75	16	-	-	SYM
ajst-14953	75	17	19	19	NUM
ajst-14953	75	18	.	.	PUNCT
ajst-14953	76	1	[	[	X
ajst-14953	76	2	3	3	X
ajst-14953	76	3	]	]	PUNCT
ajst-14953	76	4	k.	k.	PROPN
ajst-14953	76	5	mohammed	mohammed	PROPN
ajst-14953	76	6	,	,	PUNCT
ajst-14953	76	7	abdul	abdul	PROPN
ajst-14953	76	8	hanan	hanan	PROPN
ajst-14953	76	9	,	,	PUNCT
ajst-14953	76	10	et	et	PROPN
ajst-14953	76	11	al	al	PROPN
ajst-14953	76	12	.	.	PUNCT
ajst-14953	77	1	"	"	PUNCT
ajst-14953	77	2	iot	iot	PROPN
ajst-14953	77	3	cyber	cyber	NOUN
ajst-14953	77	4	-	-	PUNCT
ajst-14953	77	5	attack	attack	NOUN
ajst-14953	77	6	detection	detection	NOUN
ajst-14953	77	7	:	:	PUNCT
ajst-14953	77	8	a	a	DET
ajst-14953	77	9	comparative	comparative	ADJ
ajst-14953	77	10	analysis	analysis	NOUN
ajst-14953	77	11	.	.	PUNCT
ajst-14953	77	12	"	"	PUNCT
ajst-14953	78	1	international	international	ADJ
ajst-14953	78	2	conference	conference	NOUN
ajst-14953	78	3	on	on	ADP
ajst-14953	78	4	data	datum	NOUN
ajst-14953	78	5	science	science	NOUN
ajst-14953	78	6	,	,	PUNCT
ajst-14953	78	7	e	e	NOUN
ajst-14953	78	8	-	-	ADJ
ajst-14953	78	9	learning	learning	ADJ
ajst-14953	78	10	and	and	CCONJ
ajst-14953	78	11	information	information	NOUN
ajst-14953	78	12	systems	system	NOUN
ajst-14953	78	13	2021	2021	NUM
ajst-14953	78	14	.	.	PUNCT
ajst-14953	78	15	2021	2021	NUM
ajst-14953	78	16	.	.	PUNCT
ajst-14953	79	1	[	[	X
ajst-14953	79	2	4	4	NUM
ajst-14953	79	3	]	]	X
ajst-14953	79	4	pathak	pathak	PROPN
ajst-14953	79	5	,	,	PUNCT
ajst-14953	79	6	ashwini	ashwini	PROPN
ajst-14953	79	7	,	,	PUNCT
ajst-14953	79	8	and	and	CCONJ
ajst-14953	79	9	sakshi	sakshi	PROPN
ajst-14953	79	10	pathak	pathak	PROPN
ajst-14953	79	11	.	.	PUNCT
ajst-14953	80	1	"	"	PUNCT
ajst-14953	80	2	study	study	VERB
ajst-14953	80	3	on	on	ADP
ajst-14953	80	4	decision	decision	NOUN
ajst-14953	80	5	tree	tree	NOUN
ajst-14953	80	6	and	and	CCONJ
ajst-14953	80	7	knn	knn	PROPN
ajst-14953	80	8	algorithm	algorithm	PROPN
ajst-14953	80	9	for	for	ADP
ajst-14953	80	10	intrusion	intrusion	NOUN
ajst-14953	80	11	detection	detection	NOUN
ajst-14953	80	12	system	system	NOUN
ajst-14953	80	13	.	.	PUNCT
ajst-14953	80	14	"	"	PUNCT
ajst-14953	81	1	international	international	ADJ
ajst-14953	81	2	journal	journal	NOUN
ajst-14953	81	3	of	of	ADP
ajst-14953	81	4	engineering	engineering	PROPN
ajst-14953	81	5	research	research	PROPN
ajst-14953	81	6	&	&	CCONJ
ajst-14953	81	7	technology	technology	PROPN
ajst-14953	81	8	9.5	9.5	NUM
ajst-14953	81	9	(	(	PUNCT
ajst-14953	81	10	2020	2020	NUM
ajst-14953	81	11	):	):	PUNCT
ajst-14953	81	12	376	376	NUM
ajst-14953	81	13	-	-	SYM
ajst-14953	81	14	381	381	NUM
ajst-14953	81	15	.	.	PUNCT
ajst-14953	82	1	[	[	X
ajst-14953	82	2	5	5	NUM
ajst-14953	82	3	]	]	X
ajst-14953	82	4	rak	rak	PROPN
ajst-14953	82	5	,	,	PUNCT
ajst-14953	82	6	ewa	ewa	PROPN
ajst-14953	82	7	,	,	PUNCT
ajst-14953	82	8	et	et	PROPN
ajst-14953	82	9	al	al	PROPN
ajst-14953	82	10	.	.	PUNCT
ajst-14953	83	1	"	"	PUNCT
ajst-14953	83	2	the	the	DET
ajst-14953	83	3	distributivity	distributivity	NOUN
ajst-14953	83	4	law	law	NOUN
ajst-14953	83	5	as	as	ADP
ajst-14953	83	6	a	a	DET
ajst-14953	83	7	tool	tool	NOUN
ajst-14953	83	8	of	of	ADP
ajst-14953	83	9	k	k	PROPN
ajst-14953	83	10	-	-	PUNCT
ajst-14953	83	11	nn	nn	PROPN
ajst-14953	83	12	classifiers	classifier	NOUN
ajst-14953	83	13	’	'	PUNCT
ajst-14953	83	14	aggregation	aggregation	NOUN
ajst-14953	83	15	:	:	PUNCT
ajst-14953	83	16	mining	mine	VERB
ajst-14953	83	17	a	a	DET
ajst-14953	83	18	cyber	cyber	NOUN
ajst-14953	83	19	-	-	PUNCT
ajst-14953	83	20	attack	attack	NOUN
ajst-14953	83	21	data	datum	NOUN
ajst-14953	83	22	set	set	VERB
ajst-14953	83	23	.	.	PUNCT
ajst-14953	83	24	"	"	PUNCT
ajst-14953	84	1	2020	2020	NUM
ajst-14953	84	2	ieee	ieee	PROPN
ajst-14953	84	3	international	international	ADJ
ajst-14953	84	4	conference	conference	NOUN
ajst-14953	84	5	on	on	ADP
ajst-14953	84	6	fuzzy	fuzzy	ADJ
ajst-14953	84	7	systems	system	NOUN
ajst-14953	84	8	(	(	PUNCT
ajst-14953	84	9	fuzzieee	fuzzieee	NOUN
ajst-14953	84	10	)	)	PUNCT
ajst-14953	84	11	.	.	PUNCT
ajst-14953	85	1	ieee	ieee	PROPN
ajst-14953	85	2	,	,	PUNCT
ajst-14953	85	3	2020	2020	NUM
ajst-14953	85	4	.	.	PUNCT
ajst-14953	86	1	[	[	X
ajst-14953	86	2	6	6	NUM
ajst-14953	86	3	]	]	PUNCT
ajst-14953	86	4	sakhnini	sakhnini	NOUN
ajst-14953	86	5	,	,	PUNCT
ajst-14953	86	6	jacob	jacob	PROPN
ajst-14953	86	7	,	,	PUNCT
ajst-14953	86	8	hadis	hadis	PROPN
ajst-14953	86	9	karimipour	karimipour	PROPN
ajst-14953	86	10	,	,	PUNCT
ajst-14953	86	11	and	and	CCONJ
ajst-14953	86	12	ali	ali	PROPN
ajst-14953	86	13	dehghantanha	dehghantanha	NOUN
ajst-14953	86	14	.	.	PUNCT
ajst-14953	87	1	"	"	PUNCT
ajst-14953	87	2	smart	smart	ADJ
ajst-14953	87	3	grid	grid	NOUN
ajst-14953	87	4	cyber	cyber	NOUN
ajst-14953	87	5	attacks	attack	NOUN
ajst-14953	87	6	detection	detection	NOUN
ajst-14953	87	7	using	use	VERB
ajst-14953	87	8	supervised	supervised	ADJ
ajst-14953	87	9	learning	learning	NOUN
ajst-14953	87	10	and	and	CCONJ
ajst-14953	87	11	heuristic	heuristic	ADJ
ajst-14953	87	12	feature	feature	NOUN
ajst-14953	87	13	selection	selection	NOUN
ajst-14953	87	14	.	.	PUNCT
ajst-14953	87	15	"	"	PUNCT
ajst-14953	88	1	2019	2019	NUM
ajst-14953	88	2	ieee	ieee	NOUN
ajst-14953	88	3	7th	7th	ADJ
ajst-14953	88	4	international	international	ADJ
ajst-14953	88	5	conference	conference	NOUN
ajst-14953	88	6	on	on	ADP
ajst-14953	88	7	smart	smart	ADJ
ajst-14953	88	8	energy	energy	NOUN
ajst-14953	88	9	grid	grid	NOUN
ajst-14953	88	10	engineering	engineering	NOUN
ajst-14953	88	11	(	(	PUNCT
ajst-14953	88	12	sege	sege	NOUN
ajst-14953	88	13	)	)	PUNCT
ajst-14953	88	14	.	.	PUNCT
ajst-14953	89	1	ieee	ieee	PROPN
ajst-14953	89	2	,	,	PUNCT
ajst-14953	89	3	2019	2019	NUM
ajst-14953	89	4	.	.	PUNCT