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Gene Feature Extraction Using T-Test Statistics and Kernel Partial Least Squares

  • Shutao Li
  • Chen Liao
  • James T. Kwok
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4234)

Abstract

In this paper, we propose a gene extraction method by using two standard feature extraction methods, namely the T-test method and kernel partial least squares (KPLS), in tandem. First, a preprocessing step based on the T-test method is used to filter irrelevant and noisy genes. KPLS is then used to extract features with high information content. Finally, the extracted features are fed into a classifier. Experiments are performed on three benchmark datasets: breast cancer, ALL/AML leukemia and colon cancer. While using either the T-test method or KPLS does not yield satisfactory results, experimental results demonstrate that using these two together can significantly boost classification accuracy, and this simple combination can obtain state-of-the-art performance on all three datasets.

Keywords

Support Vector Machine Kernel Matrix Gene Selection Method Gene Extraction Leukemia Dataset 
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 2006

Authors and Affiliations

  • Shutao Li
    • 1
  • Chen Liao
    • 1
  • James T. Kwok
    • 2
  1. 1.College of Electrical and Information EngineeringHunan UniversityChangshaChina
  2. 2.Department of Computer ScienceHong Kong University of Science and TechnologyHong Kong

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