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

  • Authors:

  • Yong Mao;Xiaobo Zhou;Zheng Yin;Daoying Pi;Youxian Sun;Stephen T. C Wong

  • Affiliations:

  • National Laboratory of Industrial, Control Technology, Institute of Industrial Process Control, Zhejiang University, Hangzhou, P.R. China;Harvard Center for Neurodegeneration and, Repair, Harvard Medical School and Brigham and Women's Hospital, Harvard Medical School, Harvard University, Boston, MA;National Laboratory of Industrial, Control Technology, Institute of Industrial Process Control, Zhejiang University, Hangzhou, P.R. China;National Laboratory of Industrial, Control Technology, Institute of Industrial Process Control, Zhejiang University, Hangzhou, P.R. China;National Laboratory of Industrial, Control Technology, Institute of Industrial Process Control, Zhejiang University, Hangzhou, P.R. China;Harvard Center for Neurodegeneration and, Repair, Harvard Medical School and Brigham and Women's Hospital, Harvard Medical School, Harvard University, Boston, MA

  • Venue:
  • RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
  • Year:
  • 2006

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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