Feature-Weighted Local Support Vector Machine of Particle Swarm Optimization

Abstract

Local support vector machine gives the feature same weight in classification. In fact, many datasets have some weak or irrelevant features related to the classification. Thus giving features same weight may reduce the classification accuracy of local support vector machine.This paper puts forward a new local support vector machine that the feature weight is optimized by PSO (Particle Swarm Optimization), it is tested on the international standard UCI data sets and the images of tree taxonomy data sets, the results show that the accuracy of the algorithm we proposed is better than the general local support vector machine.