An Optimal SVM with Feature Selection Using Multiobjective PSO

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, C, and the kernel factor, σ. 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.