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International Conference on Rough Sets and Current Trends in Computing

RSCTC 2008: Rough Sets and Current Trends in Computing pp 272–282Cite as

Feature Selection Based on the Rough Set Theory and Expectation-Maximization Clustering Algorithm

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5306))

Abstract

We study the Rough Set theory as a method of feature selection based on tolerant classes that extends the existing equivalent classes. The determination of initial tolerant classes is a challenging and important task for accurate feature selection and classification. In this paper the Expectation-Maximization clustering algorithm is applied to determine similar objects. This method generates fewer features with either a higher or the same accuracy compared with two existing methods, i.e., Fuzzy Rough Feature Selection and Tolerance-based Feature Selection, on a number of benchmarks from the UCI repository.

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Author information

Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore

    Farideh Fazayeli & Lipo Wang

  2. Faculty of Mathematics and Information Science, Warsaw University of Technology, Plac Politechniki 1, 00-661, Warsaw, Poland

    Jacek Mandziuk

Authors
  1. Farideh Fazayeli
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  2. Lipo Wang
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  3. Jacek Mandziuk
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Editor information

Editors and Affiliations

  1. Department of Computer Science, The University of Akron, OH 44325-4003, Akron, USA

    Chien-Chung Chan

  2. Department of Electrical Engineering and Computer Science, University of Kansas, KS 66045, Lawrence, USA

    Jerzy W. Grzymala-Busse

  3. Department of Computer Science, University of Regina,, S4S 0A2, Regina, Saskatchewan, Canada

    Wojciech P. Ziarko

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© 2008 Springer-Verlag Berlin Heidelberg

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Fazayeli, F., Wang, L., Mandziuk, J. (2008). Feature Selection Based on the Rough Set Theory and Expectation-Maximization Clustering Algorithm. In: Chan, CC., Grzymala-Busse, J.W., Ziarko, W.P. (eds) Rough Sets and Current Trends in Computing. RSCTC 2008. Lecture Notes in Computer Science(), vol 5306. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88425-5_28

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  • DOI: https://doi.org/10.1007/978-3-540-88425-5_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-88423-1

  • Online ISBN: 978-3-540-88425-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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