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

Jung Yi Lin, Hao-Ren Ke, Been Chian Chien, Wei Pang Yang

Research output: Contribution to journalArticle

50 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1384-1393
Number of pages10
JournalExpert Systems with Applications
Volume34
Issue number2
DOIs
Publication statusPublished - 2008 Feb 1

Fingerprint

Genetic programming
Feature extraction
Classifiers
Experiments

Keywords

  • Feature generation
  • Feature selection
  • Genetic programming
  • Layered genetic programming
  • Multi-population genetic programming
  • Pattern classification

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

Cite this

Classifier design with feature selection and feature extraction using layered genetic programming. / Lin, Jung Yi; Ke, Hao-Ren; Chien, Been Chian; Yang, Wei Pang.

In: Expert Systems with Applications, Vol. 34, No. 2, 01.02.2008, p. 1384-1393.

Research output: Contribution to journalArticle

Lin, Jung Yi ; Ke, Hao-Ren ; Chien, Been Chian ; Yang, Wei Pang. / Classifier design with feature selection and feature extraction using layered genetic programming. In: Expert Systems with Applications. 2008 ; Vol. 34, No. 2. pp. 1384-1393.
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