关键词: 最小二乘支持向量机, 特征选择, 参数优化, 粒子群算法

Abstract: For the feature selection and parameter optimization of LS-SVM，a simultaneous feature selection and LS-SVM parameter optimization algorithm are proposed based on PSO algorithm.At first，a population of particles（feature subsets） is randomly generated.Then feature and parameter are optimized by PSO algorithms.The experiments on the UCI database are done with this algorithm.Experimental results indicate that this method can efficiently find the suitable feature subsets and LS-SVM parameters.Compared with GALS-SVM and LS-SVM，PSOLS-SVM can get a better classification performance.

Key words: Least Squares Support Vector Machines（LS-SVM）, feature selection, parameters optimization, particle swarm optimization algorithm