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IAPR International Conference on Pattern Recognition in Bioinformatics

PRIB 2012: Pattern Recognition in Bioinformatics pp 38–48Cite as

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A Framework of Gene Subset Selection Using Multiobjective Evolutionary Algorithm

A Framework of Gene Subset Selection Using Multiobjective Evolutionary Algorithm

  • Yifeng Li23,
  • Alioune Ngom23 &
  • Luis Rueda23 
  • Conference paper
  • 1617 Accesses

  • 3 Citations

Part of the Lecture Notes in Computer Science book series (LNBI,volume 7632)

Abstract

Microarray gene expression technique can provide snap shots of gene expression levels of samples. This technique is promising to be used in clinical diagnosis and genomic pathology. However, the curse of dimensionality and other problems have been challenging researchers for a decade. Selecting a few discriminative genes is an important choice. But gene subset selection is a NP hard problem. This paper proposes an effective gene selection framework. This framework integrates gene filtering, sample selection, and multiobjective evolutionary algorithm (MOEA). We use MOEA to optimize four objective functions taking into account of class relevance, feature redundancy, classification performance, and the number of selected genes. Experimental comparison shows that the proposed approach is better than a well-known recursive feature elimination method in terms of classification performance and time complexity.

Keywords

  • gene selection
  • sample selection
  • non-negative matrix factorization
  • multiobjective evolutionary algorithm

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Author information

Authors and Affiliations

  1. School of Computer Sciences, University of Windsor, 5115 Lambton Tower, 401 Sunset Avenue, Windsor, Ontario, N9B 3P4, Canada

    Yifeng Li, Alioune Ngom & Luis Rueda

Authors
  1. Yifeng Li
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  2. Alioune Ngom
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  3. Luis Rueda
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Editor information

Editors and Affiliations

  1. Institute of Medical Science, University of Tokyo, 4-6-1, Shirokanedai, 108-8639, Minato-ku, Tokyo, Japan

    Tetsuo Shibuya

  2. Department of Mathematical Informatics, The University of Tokyo, 7-3-1 Hongo, 113-8654, Bunkyo-ku, Tokyo, Japan

    Hisashi Kashima

  3. Department of Comouter Science, Tokyo Institute of Technology, 2-12-1 Ookayamama, 152-8550, Meguro-ku, Tokyo, Japan

    Jun Sese

  4. Bioinformatics Project, National Institute of Biomedical Innovation, 7-6-8 Saito-Asagi, 567-0085, Suita, Osaka, Japan

    Shandar Ahmad

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© 2012 Springer-Verlag Berlin Heidelberg

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Li, Y., Ngom, A., Rueda, L. (2012). A Framework of Gene Subset Selection Using Multiobjective Evolutionary Algorithm. In: Shibuya, T., Kashima, H., Sese, J., Ahmad, S. (eds) Pattern Recognition in Bioinformatics. PRIB 2012. Lecture Notes in Computer Science(), vol 7632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34123-6_4

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  • DOI: https://doi.org/10.1007/978-3-642-34123-6_4

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