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Journal of Optimization
Volume 2016, Article ID 6305043, 8 pages
http://dx.doi.org/10.1155/2016/6305043
Research Article

An Optimal SVM with Feature Selection Using Multiobjective PSO

1Department of Electrical Engineering, University of Birjand, No. 21, Sadaf 1.1 Street, Naranj 2 Alley, Shahid Avini Boulevard, Ghaffari Avenue, Birjand, South Khorasan 97176-33533, Iran
2Department of Electrical Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran

Received 5 December 2015; Revised 22 May 2016; Accepted 7 June 2016

Academic Editor: Joe Imae

Copyright © 2016 Iman Behravan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Support vector machine is a classifier, based on the structured risk minimization principle. The performance of the SVM depends on different parameters such as penalty factor, , and the kernel factor, . Also choosing an appropriate kernel function can improve the recognition score and lower the amount of computation. Furthermore, selecting the useful features among several features in dataset not only increases the performance of the SVM, but also reduces the computational time and complexity. So this is an optimization problem which can be solved by heuristic algorithm. In some cases besides the recognition score, the reliability of the classifier’s output is important. So in such cases a multiobjective optimization algorithm is needed. In this paper we have got the MOPSO algorithm to optimize the parameters of the SVM, choose appropriate kernel function, and select the best feature subset simultaneously in order to optimize the recognition score and the reliability of the SVM concurrently. Nine different datasets, from UCI machine learning repository, are used to evaluate the power and the effectiveness of the proposed method (MOPSO-SVM). The results of the proposed method are compared to those which are achieved by single SVM, RBF, and MLP neural networks.