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International Conference on Rough Sets and Knowledge Technology

RSKT 2011: Rough Sets and Knowledge Technology pp 447–456Cite as

Analysis of Data-Driven Parameters in Game-Theoretic Rough Sets

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6954))

Abstract

The game-theoretic rough set (GTRS) model provides an alternative approach to the derivation of probabilistic rough set regions. Whereas other models rely on either user-provided parameters or notions of cost for the date set in question, the GTRS model learns these parameters through a game-theoretic process. The parameters can be of the form of probabilities that determine the rough set region bounds or they can be superseded by classification measures whose values represent the current health of the classification system. In this article, we will be analyzing the relationship between the calculated parameters and the learned values of the loss functions. We demonstrate the effectiveness of the game-theoretic rough set model in performing data analysis.

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Author information

Authors and Affiliations

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

    Joseph P. Herbert & JingTao Yao

Authors
  1. Joseph P. Herbert
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  2. JingTao Yao
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Editor information

Editors and Affiliations

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

    JingTao Yao

  2. Department of Applied Computer Science, University of Winnipeg, R3B 2E9, Winnipeg, Canada

    Sheela Ramanna

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

    Guoyin Wang

  4. Chair of Computer Science, University of Rzeszów, Poland

    Zbigniew Suraj

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© 2011 Springer-Verlag Berlin Heidelberg

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Herbert, J.P., Yao, J. (2011). Analysis of Data-Driven Parameters in Game-Theoretic Rough Sets. In: Yao, J., Ramanna, S., Wang, G., Suraj, Z. (eds) Rough Sets and Knowledge Technology. RSKT 2011. Lecture Notes in Computer Science(), vol 6954. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24425-4_59

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  • DOI: https://doi.org/10.1007/978-3-642-24425-4_59

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24424-7

  • Online ISBN: 978-3-642-24425-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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