Evolutionary Computing Optimization for Parameter Determination and Feature Selection of Support Vector Machines

Abstract

Support vector machine (SVM) is a popular pattern classification method with many diverse applications. Kernel parameter setting in the SVM training procedure, along with the feature selection, significantly influences the classification accuracy. This study simultaneously determines the parameter values while discovering a subset of features, increasing SVM classification accuracy. The study focuses two evolutionary computing approaches to optimize the parameters of SVM: particle swarm optimization (PSO) and genetic algorithm (GA). And we combine the two evolutionary methods with SVM to choose appropriate subset features and SVM parameters, experimental results demonstrate that the classification accuracy surpass traditional grid searching approach. Also the paper compares PSO with GA method based SVM classification and they have similar results.