Scalable Feature Selection in High-Dimensional Data Based on GRASP

Figures & data

FIGURE 1 Overall procedure of feature selection.

FIGURE 2 GRASP algorithm for feature selection (FCGRASP).

FIGURE 3 An extended version of SA used in the improvement phase.

FIGURE 4 The acceptance probability against ΔP in the proposed SA.

TABLE 1 Characteristics of the Datasets

Dataset	Abbreviation	#instances	#features	#classes
Connectionist Bench	CB	200	60	2
WarpPIE10P	WAR	210	2420	10
PCMAC	PCM	1943	3289	2
TOX-171	TOX	171	5748	4
Arcene	ARC	200	9961	2
Pixraw10P	PIX	100	10000	10
ORLRaws10P	ORL	100	10340	10
CLL-SUB-111	CLL	111	11340	3
GLI-85	GLI	85	22283	2

TABLE 2 The Accuracy, Runtime, and Parameters Set for FSGRASP and SAGRASP

						Parameters
Abbreviation		FCGRASP Precision	SAGRASP Precision	FCGRASP Run-Time	SAGRASP Run-Time	Size	NumIter	K_temp	P_add	SA_iter
1	CB	0.873	0.895	149	2054	20	100	0.5	0.7	1000
2	WAR	0.937	0.940	802	1865	150	100	1	0.75	1000
3	PCM	0.976	0.976	1278	1677	150	100	0.5	0.5	1000
4	TOX	0.926	0.967	1622	3867	200	100	1	0.75	1000
5	ARC	0.923	0.930	1502	4209	300	100	0.5	0.7	1000
6	PIX	1.000	1.000	1628	2036	300	100	2	0.75	1000
7	ORL	1.000	1.000	1684	1688	300	100	0.5	0.7	1000
8	CLL	0.868	0.865	4655	1599	300	100	10	0.6	500
9	GLI	0.972	0.972	2784	3364	500	100	0.5	0.5	1000

FIGURE 5 The effect of acceptance probability on accuracy over the ARC dataset.

FIGURE 6 The effect of acceptance probability on the size of the selected subset of features over the ARC dataset.