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

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

研究成果: 雜誌貢獻文章

50 引文 斯高帕斯(Scopus)

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.

原文英語
頁(從 - 到)1384-1393
頁數10
期刊Expert Systems with Applications
34
發行號2
DOIs
出版狀態已發佈 - 2008 二月 1

    指紋

ASJC Scopus subject areas

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

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