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2008

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

10 years 1 months ago
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.
Jung-Yi Lin, Hao-Ren Ke, Been-Chian Chien, Wei-Pan
Added 26 Dec 2010
Updated 26 Dec 2010
Type Journal
Year 2008
Where ESWA
Authors Jung-Yi Lin, Hao-Ren Ke, Been-Chian Chien, Wei-Pang Yang
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