Classifier design with feature selection and feature extraction using layered genetic programming

Authors:
Jung-Yi Lin;Hao-Ren Ke;Been-Chian Chien;Wei-Pang Yang
Affiliations:
Department of Computer Science, National Chiao Tung University, 1001 Ta Hsueh Road, HsinChu 300, Taiwan;Library and Institute of Information Management, National Chiao Tung University, Taiwan;Department of Computer Science and Information Engineering, National University of Tainan, Taiwan;Department of Computer Science, National Chiao Tung University, 1001 Ta Hsueh Road, HsinChu 300, Taiwan and Department of Information Management, National Dong Hwa University, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2008

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Abstract

This paper proposes a novel method called FLGP to construct a classifier device of capability in feature selection and feature extraction. FLGP is developed with layered genetic programming that is a kind of the multiple-population genetic programming. Populations advance to an optimal discriminant function to divide data into two classes. Two methods of feature selection are proposed. New features extracted by certain layer are used to be the training set of next layer's populations. Experiments on several well-known datasets are made to demonstrate performance of FLGP.