Gene selection using Gaussian kernel support vector machine based recursive feature elimination with adaptive kernel width strategy

Yong Mao, Xiaobo Zhou, Zheng Yin, Daoying Pi, Youxian Sun, Stephen T C Wong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Recursive feature elimination based on non-linear kernel support vector machine (SVM-RFE) with parameter selection by genetic algorithm is an effective algorithm to perform gene selection and cancer classification in some degree, but its calculating complexity is too high for implementation. In this paper, we propose a new strategy to use adaptive kernel parameters in the recursive feature elimination algorithm implemented with Gaussian kernel SVMs as a better alternatives to the aforementioned algorithm for pragmatic reasons. The proposed method performs well in selecting genes and achieves high classification accuracies with these genes on two cancer datasets.

Original languageEnglish (US)
Title of host publicationRough Sets and Knowledge Technology - First International Conference, RSKT 2006, Proceedings
PublisherSpringer-Verlag
Pages799-806
Number of pages8
ISBN (Print)3540362975, 9783540362975
DOIs
StatePublished - 2006
EventFirst International Conference on Rough Sets and Knowledge Technology, RSKT 2006 - Chongqing, China
Duration: Jul 24 2006Jul 26 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4062 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherFirst International Conference on Rough Sets and Knowledge Technology, RSKT 2006
CountryChina
CityChongqing
Period7/24/067/26/06

Keywords

  • Feature selection
  • Machine learning
  • Recursive feature elimination
  • Support vector machine

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

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