Feature selection based on particle swarm optimal with multiple evolutionary strategies

Author

Zhao, Jing ; Han, Chongzhao ; Wei, Bin ; Zhao, Qi ; Xiao, Peng ; Zhang, Kedai

Author_Institution

MOE Key Lab. For Intell. Networks & Network Security, Xi´´an Jiaotong Univ., Xi´´an, China

fYear

2012

fDate

9-12 July 2012

Firstpage

963

Lastpage

968

Abstract

Feature selection is an effective data preprocessing step to reduce the dimension of feature space and save storage space. Binary particle swarm optimization (BPSO) has been applied successfully to solve feature selection problem. But it was easy to fall into local optimal point. M2BPSO was an improved BPSO algorithm. The particles of M2BPSO were updated by using various evolutionary strategies according to the performance of them, in addition, the mutation was used to overcome premature. In this paper, we adopted M2BPSO to solve feature selection problem. To test the validity of the algorithm, we compared it with various versions of BPSO methods. Experimental results showed that M2BPSO could effectively solve the feature selection problem.

Keywords

evolutionary computation; feature extraction; particle swarm optimisation; binary particle swarm optimization; data preprocessing step; feature selection; feature space; local optimal point; multiple evolutionary strategy; storage space; Accuracy; Educational institutions; Encoding; Ionosphere; Particle swarm optimization; Probability; Support vector machines; Binary Particle swarm optimal; feature selection; multiple evolutionary strategies; support vector machine; wrapper method;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Fusion (FUSION), 2012 15th International Conference on

Conference_Location

Singapore

Print_ISBN

978-1-4673-0417-7

Electronic_ISBN

978-0-9824438-4-2

Type

conf

Filename

6289906