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

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.

• Type: Journal
• Year: 2008
• Where: ESWA
• Authors: Jung-Yi Lin, Hao-Ren Ke, Been-Chian Chien, Wei-Pang Yang
• Keywords: ESWA 2006, ESWA 2008, Feature Selection, Genetic Programming, Layered Genetic Programming