Parameters selection in gene selection using Gaussian kernel support vector machines by genetic algorithm

Yong Mao, Xiao Bo Zhou, Dao Ying Pi, You Xian Sun, Stephen T C Wong

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


In microarray-based cancer classification, gene selection is an important issue owing to the large number of variables and small number of samples as well as its non-linearity. It is difficult to get satisfying results by using conventional linear statistical methods. Recursive feature elimination based on support vector machine (SVM RFE) is an effective algorithm for gene selection and cancer classification, which are integrated into a consistent framework. In this paper, we propose a new method to select parameters of the aforementioned algorithm implemented with Gaussian kernel SVMs as better alternatives to the common practice of selecting the apparently best parameters by using a genetic algorithm to search for a couple of optimal parameter. Fast implementation issues for this method are also discussed for pragmatic reasons. The proposed method was tested on two representative hereditary breast cancer and acute leukaemia datasets. The experimental results indicate that the proposed method performs well in selecting genes and achieves high classification accuracies with these genes.

Original languageEnglish (US)
Pages (from-to)961-973
Number of pages13
JournalJournal of Zhejiang University: Science
Volume6 B
Issue number10
StatePublished - Oct 2005


  • Gene selection
  • Genetic algorithm (GA)
  • Parameter selection
  • Recursive feature elimination (RFE)
  • Support vector machine (SVM)

ASJC Scopus subject areas

  • Engineering(all)


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