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Evolutionary Tolerance-Based Gene Selection in Gene Expression Data

  • Conference paper
Transactions on Rough Sets XIV

Part of the book series: Lecture Notes in Computer Science ((TRS,volume 6600))

Abstract

Gene selection is to select the most informative genes from the whole gene set, which is a key step of the discriminant analysis of microarray data. Rough set theory is an efficient mathematical tool for further reducing redundancy. The main limitation of traditional rough set theory is the lack of effective methods for dealing with real-valued data. However, gene expression data sets are always continuous. This has been addressed by employing discretization methods, which may result in information loss. This paper investigates one approach combining feature ranking together with features selection based on tolerance rough set theory. Moreover, this paper explores the other method which can utilize the information contained within the boundary region to improve classification accuracy in gene expression data. Compared with gene selection algorithm based on rough set theory, the proposed methods are more effective for selecting high discriminative genes in cancer classification.

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Authors and Affiliations

  1. Department of Information Science and Technology, East China University of Political Science and Law, Shanghai, 201620, P.R. China

    Na Jiao

Authors
  1. Na Jiao
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Editor information

Editors and Affiliations

  1. University of Manitoba, R3T 5V6, Winnipeg, MB, Canada

    James F. Peters

  2. University of Warsaw, 02-097, Warsaw, Poland

    Andrzej Skowron  & Dominik Slezak  & 

  3. Kyushu Institute of Technology, 804, Tobata, Kitakyushu, Japan

    Hiroshi Sakai

  4. Jadavpur University, Kolkata, Indian Statistical Institute, Kolkata, India

    Mihir Kumar Chakraborty

  5. Cairo University, Orman, Giza, Egypt

    Aboul Ella Hassanien

  6. Zhangzhou Normal University, Fujian, China

    William Zhu

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© 2011 Springer-Verlag Berlin Heidelberg

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Jiao, N. (2011). Evolutionary Tolerance-Based Gene Selection in Gene Expression Data. In: Peters, J.F., et al. Transactions on Rough Sets XIV. Lecture Notes in Computer Science, vol 6600. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21563-6_6

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  • DOI: https://doi.org/10.1007/978-3-642-21563-6_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21562-9

  • Online ISBN: 978-3-642-21563-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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