A distributed PSOSVM hybrid system with feature selection and parameter optimization

-Abstract

This study proposed a novel PSO–SVM model that hybridized the particle swarm optimization (PSO) and support vector machines (SVM) to

improve the classification accuracy with a small and appropriate feature subset. This optimization mechanism combined the discrete PSO with the

continuous-valued PSO to simultaneously optimize the input feature subset selection and the SVM kernel parameter setting. The hybrid PSO–SVM

data mining system was implemented via a distributed architecture using the web service technology to reduce the computational time. In a

heterogeneous computing environment, the PSO optimization was performed on the application server and the SVM model was trained on the

client (agent) computer. The experimental results showed the proposed approach can correctly select the discriminating input features and also

achieve high classification accuracy.

# 2007 Elsevier B.V. All rights reserved.