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A Note on Attribute Reduction in the Decision-Theoretic Rough Set Model

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Rough Sets and Current Trends in Computing (RSCTC 2008)

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Abstract

This paper considers two groups of studies on attribute reduction in the decision-theoretic rough set model. Attribute reduction can be interpreted based on either decision preservation or region preservation. According to the fact that probabilistic regions are non-monotonic with respect to set inclusion of attributes, attribute reduction for region preservation is different from the classical interpretation of reducts.

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Author information

Authors and Affiliations

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

    Y. Zhao, S. K. M. Wong & Y. Y. Yao

Authors
  1. Y. Zhao
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  2. S. K. M. Wong
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  3. Y. Y. Yao
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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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Zhao, Y., Wong, S.K.M., Yao, Y.Y. (2008). A Note on Attribute Reduction in the Decision-Theoretic Rough Set Model. 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_7

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  • DOI: https://doi.org/10.1007/978-3-540-88425-5_7

  • 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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