Title: Differential evolution-based parameters optimisation and feature selection for support vector machine

Authors: Jun Li; Lixin Ding; Bo Li

Addresses: State Key Laboratory of Software Engineering, Wuhan University, Wuhan, 430072, China; School of Computer, Wuhan University, Wuhan,430072, China; College of Computer Science and Technology, Wuhan University of Science and Technology, WUST, Wuhan, 430065, China ' State Key Laboratory of Software Engineering, Wuhan University, Wuhan, 430072, China; School of Computer, Wuhan University, Wuhan, 430072, China ' College of Computer Science and Technology, Wuhan University of Science and Technology, WUST, Wuhan, 430065, China

Abstract: This paper addresses the problem of SVM classification optimisation. For this purpose, the authors propose an SVM classification system based on differential evolution (DE) to improve the generalisation performance of the SVM classifier. In the classification system, a method of simultaneous parameters optimisation and feature selection for support vector machine is put forward. The experiments are conducted on the basis of benchmark dataset. The obtained results clearly confirm the superiority of the DE-SVM-FS approach compared to default SVM classifier and DE-SVM algorithm; this suggests that further substantial improvements in terms of classification accuracy can be achieved by the proposed DE-SVM-FS classification system.

Keywords: high-dimensional classification; support vector machines; SVM; differential evolution; parameter optimisation; feature selection.

DOI: 10.1504/IJCSE.2016.080212

International Journal of Computational Science and Engineering, 2016 Vol.13 No.4, pp.355 - 363

Received: 26 Jun 2014
Accepted: 23 Nov 2014
Published online: 08 Nov 2016 *

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