Comparative Study of Techniques for Large-Scale Feature Selection

Comparative Study of Techniques for Large-Scale Feature Selection (1994)

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by F. J. Ferri , P. Pudil , M. Hatef , J. Kittler

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Datum	Value	Source
TITLE	b	SVM HeaderParse 0.1
AUTHOR NAME	F. J. Ferri	SVM HeaderParse 0.1
AUTHOR AFFIL	a	SVM HeaderParse 0.2
AUTHOR NAME	P. Pudil	SVM HeaderParse 0.1
AUTHOR NAME	M. Hatef	SVM HeaderParse 0.1
AUTHOR AFFIL	c	SVM HeaderParse 0.2
AUTHOR NAME	J. Kittler	SVM HeaderParse 0.1
AUTHOR AFFIL	; c; a; Dept. Inform`atica i Electr`onica. Universitat de Val`encia; b; Inst. of Information Theory and Automation. Academy of Sciencies of the Czech	SVM HeaderParse 0.2
AUTHOR ADDR	; 46100 Burjassot (Val`encia) Spain; Republic. 18208, Prague. Czech Republic	SVM HeaderParse 0.1
ABSTRACT	The combinatorial search problem arising in feature selection in high dimensional spaces is considered. Recently developed techniques based on the classical sequential methods and the (l; r) search called Floating search algorithms are compared against the Genetic approach to feature subset search. Both approaches have been designed with the view to give a good compromise between efficiency and effectiveness for large problems. The purpose of this paper is to investigate the applicability of these techniques to high dimensional problems of Feature Selection. The aim is to establish whether the properties inferred for these techniques from medium scale experiments involving up to a few tens of dimensions extend to dimensionalities of one order of magnitude higher. Further, relative merits of these techniques vis-a-vis such high dimensional problems are explored and the possibility of exploiting the best aspects of these methods to create a composite feature selection procedure with superior properties is considered. 1.	SVM HeaderParse 0.1
CITATIONS	10 found	ParsCit 1.0