An Improved Particle Swarm Optimization for Feature Selection

作     者：Yuanning Liu 

作者机构：College of Computer Science and Technology Jilin University Changchun 130012 P. R. China Key Laboratory of S.vmbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 P. R. China 

出 版 物：《Journal of Bionic Engineering》 (仿生工程学报（英文版）)

年 卷 期：2011年第8卷第2期

页      面：191-200页

核心收录：

学科分类：12[管理学] 08[工学] 0710[理学-生物学] 0831[工学-生物医学工程（可授工学、理学、医学学位）] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 080203[工学-机械设计及理论] 0835[工学-软件工程] 0802[工学-机械工程] 0836[工学-生物工程] 0811[工学-控制科学与工程] 0702[理学-物理学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Acknowledgments This work was supported by National Natural Science Foundation of China (Grant no. 60971089)  National Electronic Development Foundation of China (Grant no. 2009537)  Jilin Province Science and Tech- nology Department Project of China (Grant no. 20090502) 

主　　题：particle swarm optimization feature selection data mining support vector machines 

摘      要：Particle Swarm Optimization （PSO） is a popular and bionic algorithm based on the social behavior associated with bird flocking for optimization problems. To maintain the diversity of swarms, a few studies of multi-swarm strategy have been reported. However, the competition among swarms, reservation or destruction of a swarm, has not been considered further. In this paper, we formulate four rules by introducing the mechanism for survival of the fittest, which simulates the competition among the swarms. Based on the mechanism, we design a modified Multi-Swarm PSO （MSPSO） to solve discrete problems, which consists of a number of sub-swarms and a multi-swarm scheduler that can monitor and control each sub-swarm using the rules. To further settle the feature selection problems, we propose an Improved Feature Selection （1FS） method by integrating MSPSO, Support Vector Machines （SVM） with F-score method. The IFS method aims to achieve higher generalization capa- bility through performing kernel parameter optimization and feature selection simultaneously. The performance of the proposed method is compared with that of the standard PSO based, Genetic Algorithm （GA） based and the grid search based mcthods on 10 benchmark datasets, taken from UCI machine learning and StatLog databases. The numerical results and statistical analysis show that the proposed IFS method performs significantly better than the other three methods in terms of prediction accuracy with smaller subset of features.