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Rule Measures Tradeoff Using Game-Theoretic Rough Sets

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Brain Informatics (BI 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7670))

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Abstract

The game-theoretic rough sets (GTRS) is a recent proposal to determine optimal thresholds in probabilistic rough sets by setting up a game for trading-off between different criteria. Different competitions or cooperations can be formulated depending on objectives of users. In this article, five approaches for applying GTRS are reviewed. The GTRS for formulating competition or cooperation among measures is described. We investigate potential players of GTRS, in the form of measures for evaluating a set of immediate decision rules. The definition and properties of these measures are discussed. We demonstrate that GTRS meet the challenge for determining a balanced and optimal threshold pair that leads to a moderate, cost effective or efficient level of acceptance, rejection or deferment decision by providing a game mechanism.

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Author information

Authors and Affiliations

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

    Yan Zhang & JingTao Yao

Authors
  1. Yan Zhang
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  2. JingTao Yao
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Editor information

Editors and Affiliations

  1. Department of Enterprise Engineering, University of Rome “Tor Vergata”, Viale del Politecnico, 1, 00133, Roma, Italy

    Fabio Massimo Zanzotto

  2. Department of Medical Informatics, Faculty of Medicine, Shimane University, 89-1 Enya-cho, 693-8501, Izumo-city, Shimane, Japan

    Shusaku Tsumoto

  3. Department of Artificial Intelligence, Faculty of Mathematics and Natural Sciences, University of Groningen, Nijenborgh 9, 9747 AG, Groningen, Netherlands

    Niels Taatgen

  4. Department of Computer Science, University of Regina, 3737 Wascana Parkway, S4S 0A2, Regina, SK, Canada

    Yiyu Yao

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© 2012 Springer-Verlag Berlin Heidelberg

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Zhang, Y., Yao, J. (2012). Rule Measures Tradeoff Using Game-Theoretic Rough Sets. In: Zanzotto, F.M., Tsumoto, S., Taatgen, N., Yao, Y. (eds) Brain Informatics. BI 2012. Lecture Notes in Computer Science(), vol 7670. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35139-6_33

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  • DOI: https://doi.org/10.1007/978-3-642-35139-6_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35138-9

  • Online ISBN: 978-3-642-35139-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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