A Filter-Wrapper hybrid feature selection approach with maximum relevance and minimum redundancy based
on particle swarm optimization(PSO) algorithm is proposed on the analysis of PSO algorithm and simplified PSO algorithm. The Filter is based on mutual information and the composite measure of feature relevance and redundancy, while the Wrapper is based on a simply modified PSO algorithm. The relevance and redundancy criterion is introduced to select features in the PSO’s searching procedure. Meantime, the Filter is fused into the Wrapper. The speed and performance of the search are improved with the higher efficiency of the Filter and the greater accuracy of the Wrapper. The experiment results based on UCI data sets with support vector machine(SVM) as the classifier show the effectiveness and feasibility of the algorithm.