Feature selection algorithm based on least squares support vector machine and particle swarm optimization

Authors:
Song Chuyi;Jiang Jingqing;Wu Chunguo;Liang Yanchun
Affiliations:
College of Mathematics, Inner Mongolia University for Nationalities, Tongliao, China;College of Computer Science and Technology, Inner Mongolia University for Nationalities, Tongliao, China;College of Computer Science and Technology, Jilin University, Changchun, China;College of Computer Science and Technology, Jilin University, Changchun, China
Venue:
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
Year:
2011

Citing 2
Cited 0

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Abstract

A hybrid feature selection algorithm based on least squares support vector machine (LSSVM) and discrete particle swarm optimization is proposed in this paper. The proposed algorithm takes advantage of the easy solving of LSSVM, adopts LSSVM to construct classifier, and use accuracy as the main part of fitness function on the process of particle swarm optimization. The simulation results show that the proposed algorithm could obtain the features which contribute a lot to classifier. Therefore the dimension of data is decreased and the efficiency of classifier is improved.