Genetic Algorithm Based Feature Selection Method Development for Pattern Recognition

Author

Kim, Ho-Duck ; Park, Chang-Hyun ; Yang, Hyun-Chang ; Sim, Kwee-Bo

Author_Institution

Dept. of Electr. & Electron. Eng., Chungang Univ., Seoul

fYear

2006

Firstpage

1020

Lastpage

1025

Abstract

An important problem of pattern recognition is to extract or select feature set, which is included in the pre-processing stage. In order to extract feature set, principal component analysis has been usually used and SFS (sequential forward selection) and SBS (sequential backward selection) have been used as a feature selection method. This paper applies genetic algorithm which is a popular method for nonlinear optimization problem to the feature selection problem. So, we call it genetic algorithm feature selection (GAFS) and this algorithm is compared to other methods in the performance aspect

Keywords

feature extraction; genetic algorithms; principal component analysis; feature extraction; feature selection method; genetic algorithm; nonlinear optimization problem; pattern recognition; principal component analysis; sequential backward selection; sequential forward selection; Emotion recognition; Feature extraction; Genetic algorithms; Genetic engineering; Genetic programming; Optimization methods; Pattern recognition; Principal component analysis; Search methods; Speech; Feature Selection; Feature extraction; Genetic Algorithm; Pattern Recognition; SFS;

fLanguage

English

Publisher

ieee

Conference_Titel

SICE-ICASE, 2006. International Joint Conference

Conference_Location

Busan

Print_ISBN

89-950038-4-7

Electronic_ISBN

89-950038-5-5

Type

conf

DOI

10.1109/SICE.2006.315742

Filename

4109107