Feature Subset Selection Based on Improved Discrete Particle Swarm and Support Vector Machine Algorithm

In creating a pattern classifier, feature selection is often used to prune irrelevant and noisy features to producing effective features. Manually developing a feature set can be a very time consuming and costly endeavor. In this paper, an efficient feature selection algorithm based on improved binary particle swarm optimization and support vector machine Algorithm (IBPSO-SVM) was used. First a population of particles (feature subsets) were randomly generated, and then optimized by IBPSO-SVM wrapper algorithms; finally the best fitness feature subset was applied to SVM classification. The simulation experiment results have proved that the feature subset selection algorithm based on IBPSO-SVM is very effective.