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

被引：48

作者：

Lin, Jung-Yi ^{[1
]}

Ke, Hao-Ren ^{[2
]}

Chien, Been-Chian ^{[3
]}

Yang, Wei-Pang ^{[1
]}

机构：

[1] Natl Chiao Tung Univ, Dept Comp Sci, Hsinchu 300, Taiwan

[2] Natl Chiao Tung Univ, Library & Inst Informat Management, Hsinchu, Taiwan

[3] Natl Univ Tainan, Dept Comp Sci & Informat Engn, Tainan, Taiwan

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2008年 / 34卷 / 02期

关键词：

feature generation; feature selection; pattern classification; genetic programming; multi-population genetic programming; layered genetic programming;

D O I：

10.1016/j.eswa.2007.01.006

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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. (C) 2007 Elsevier Ltd. All rights reserved.

引用

页码：1384 / 1393

页数：10

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