A novel feature selection approach and its application

Authors:
Gexiang Zhang;Weidong Jin;Laizhao Hu
Affiliations:
National EW Laboratory, Chengdu, Sichuan, China;School of Electrical Engineering, Southwest Jiaotong University, Chengdu, Sichuan, China;National EW Laboratory, Chengdu, Sichuan, China
Venue:
CIS'04 Proceedings of the First international conference on Computational and Information Science
Year:
2004

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Abstract

Feature selection is a satisfactory optimization problem. Most feature selection methods did not consider the cost of feature extraction and the automatic decision of feature subset dimension. So a novel approach called satisfactory feature selection method (SFSM) was proposed. SFSM integrated feature extraction with feature selection and considered classification performance of feature samples, the dimension of feature subset and the complexity of feature extraction simultaneously. Experimental results show that SFSM selects more satisfying feature subset than sequential forward selection using distance criterion (SFSDC) and the method presented by Tiejun Lü (GADC). Also, SFSM achieves higher accurate recognition rate than original feature set, SFSDC and GADC, which verifies the validity of the proposed method.