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Using Fuzzy Dependency-Guided Attribute Grouping in Feature Selection

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Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (RSFDGrC 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2639))

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Abstract

Feature selection has become a vital step in many machine learning techniques due to their inability to handle high dimensional descriptions of input features. This paper demonstrates the applicability of fuzzy-rough attribute reduction and fuzzy dependencies to the problem of learning classifiers, resulting in simpler rules with little loss in classification accuracy.

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References

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Author information

Authors and Affiliations

  1. Centre for Intelligent Systems and their Applications, School of Informatics, The University of Edinburgh, UK

    Richard Jensen & Qiang Shen

Authors
  1. Richard Jensen
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  2. Qiang Shen
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Editor information

Editors and Affiliations

  1. Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, P.R. China

    Guoyin Wang

  2. Department of Computer Science, Nanchang University, Nanchang, 330029, P.R. China

    Qing Liu

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

    Yiyu Yao

  4. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron

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© 2003 Springer-Verlag Berlin Heidelberg

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Jensen, R., Shen, Q. (2003). Using Fuzzy Dependency-Guided Attribute Grouping in Feature Selection. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2003. Lecture Notes in Computer Science(), vol 2639. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39205-X_32

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  • DOI: https://doi.org/10.1007/3-540-39205-X_32

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-14040-5

  • Online ISBN: 978-3-540-39205-7

  • eBook Packages: Springer Book Archive

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