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Feature Selection for Microarray Data Analysis Using Mutual Information and Rough Set Theory

  • Wengang Zhou
  • Chunguang Zhou
  • Hong Zhu
  • Guixia Liu
  • Xiaoyu Chang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4115)

Abstract

Cancer classification is one major application of microarray data analysis. Due to the ultra high dimension of gene expression data, efficient feature selection methods are in great needs for selecting a small number of informative genes. In this paper, we propose a novel feature selection method MIRS based on mutual information and rough set. First, we select some top-ranked features which have higher mutual information with the target class to predict. Then rough set theory is applied to remove the redundancy among these selected genes. Binary particle swarm optimization (BPSO) is first proposed for attribute reduction in rough set. Finally, the effectiveness of the proposed method is evaluated by the classification accuracy of SVM classifier. Experiment results show that MIRS is superior to some other classical feature selection methods and can get higher prediction accuracy with small number of features. Generally, the results are highly promising.

Keywords

Support Vector Machine Feature Selection Mutual Information Support Vector Machine Classifier Feature Selection Method 
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

  • Wengang Zhou
    • 1
  • Chunguang Zhou
    • 1
  • Hong Zhu
    • 1
  • Guixia Liu
    • 1
  • Xiaoyu Chang
    • 1
  1. 1.College of Computer Science and TechnologyJilin UniversityChangchunP.R. China

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