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Combining Support Vector Machines and the t-statistic for Gene Selection in DNA Microarray Data Analysis

  • Tao Yang
  • Vojislave Kecman
  • Longbing Cao
  • Chengqi Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6119)

Abstract

This paper proposes a new gene selection (or feature selection) method for DNA microarray data analysis. In the method, the t-statistic and support vector machines are combined efficiently. The resulting gene selection method uses both the data intrinsic information and learning algorithm performance to measure the relevance of a gene in a DNA microarray. We explain why and how the proposed method works well. The experimental results on two benchmarking microarray data sets show that the proposed method is competitive with previous methods. The proposed method can also be used for other feature selection problems.

Keywords

Support Vector Machine Gene Selection Linear Support Vector Machine Ranking Criterion Recursive Feature Elimination 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Tao Yang
    • 1
  • Vojislave Kecman
    • 2
  • Longbing Cao
    • 1
  • Chengqi Zhang
    • 1
  1. 1.Faculty of Engineering and Information TechnologyUniversity of TechnologySydneyAustralia
  2. 2.Department of Computer ScienceVirginia Commonwealth UniversityRichmondUSA

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