Least squares twin parametric-margin support vector machine for classification

Authors:
Yuan-Hai Shao;Zhen Wang;Wei-Jie Chen;Nai-Yang Deng
Affiliations:
Zhijiang College, Zhejiang University of Technology, Hangzhou, P.R. China 310024;Mathematics Colledge of Jilin University, Changchun, P.R. China 130012;Zhijiang College, Zhejiang University of Technology, Hangzhou, P.R. China 310024;College of Science China Agricultural University, Beijing, P.R. China 100083
Venue:
Applied Intelligence
Year:
2013

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Abstract

In this paper, we propose a novel least squares twin parametric-margin support vector machine (TPMSVM) for binary classification, called LSTPMSVM for short. LSTPMSVM attempts to solve two modified primal problems of TPMSVM, instead of two dual problems usually solved. The solution of the two modified primal problems reduces to solving just two systems of linear equations as opposed to solving two quadratic programming problems along with two systems of linear equations in TPMSVM, which leads to extremely simple and fast algorithm. Classification using nonlinear kernel with reduced technique also leads to systems of linear equations. Therefore our LSTPMSVM is able to solve large datasets accurately without any external optimizers. Further, a particle swarm optimization (PSO) algorithm is introduced to do the parameter selection. Our experiments on synthetic as well as on several benchmark data sets indicate that our LSTPMSVM has comparable classification accuracy to that of TPMSVM but with remarkably less computational time.