Scientific Programming
Volume 2016, Article ID 2739621, 10 pages
http://dx.doi.org/10.1155/2016/2739621
A Parallel Genetic Algorithm Based Feature Selection and Parameter Optimization for Support Vector Machine
College of Computer Science, Sichuan University, Chengdu, Sichuan 610065, China
Received 27 October 2015; Revised 30 May 2016; Accepted 8 June 2016
Academic Editor: Tomàs Margalef
Copyright © 2016 Zhi Chen 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
The extensive applications of support vector machines (SVMs) require efficient method of constructing a SVM classifier with high classification ability. The performance of SVM crucially depends on whether optimal feature subset and parameter of SVM can be efficiently obtained. In this paper, a coarse-grained parallel genetic algorithm (CGPGA) is used to simultaneously optimize the feature subset and parameters for SVM. The distributed topology and migration policy of CGPGA can help find optimal feature subset and parameters for SVM in significantly shorter time, so as to increase the quality of solution found. In addition, a new fitness function, which combines the classification accuracy obtained from bootstrap method, the number of chosen features, and the number of support vectors, is proposed to lead the search of CGPGA to the direction of optimal generalization error. Experiment results on 12 benchmark datasets show that our proposed approach outperforms genetic algorithm (GA) based method and grid search method in terms of classification accuracy, number of chosen features, number of support vectors, and running time.