Simultaneous Feature Selection and SVM Parameters Optimization Algorithm Based on Binary PSO
基于二进制PSO算法的特征选择及SVM参数同步优化

Feature selection and classifier parameter optimization are two important aspects for improving classifier performance and are solved separately traditionally. Recently, with the wide applications of evolutionary computation in pattern recognition area, simultaneous feature selection and parameter optimization become possible and tendency. To solve the problem, we propose a simultaneous feature selection and SVM parameter optimization algorithm based on binary PSO algorithm called PSO-SVM. The experiments show that the algorithm can efficiently find the suitable feature subsets and SVM parameters, which result in good classification performance. Compared with GA-SVM , PSO-SVM can get a more compact feature subset and run more efficiently.