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Decision-Theoretic Rough Set Models

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Book cover Rough Sets and Knowledge Technology (RSKT 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4481))

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Abstract

Decision-theoretic rough set models are a probabilistic extension of the algebraic rough set model. The required parameters for defining probabilistic lower and upper approximations are calculated based on more familiar notions of costs (risks) through the well-known Bayesian decision procedure. We review and revisit the decision-theoretic models and present new results. It is shown that we need to consider additional issues in probabilistic rough set models.

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Authors and Affiliations

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

    Yiyu Yao

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  1. Yiyu Yao
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Editor information

JingTao Yao Pawan Lingras Wei-Zhi Wu Marcin Szczuka Nick J. Cercone Dominik Ślȩzak

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Yao, Y. (2007). Decision-Theoretic Rough Set Models. In: Yao, J., Lingras, P., Wu, WZ., Szczuka, M., Cercone, N.J., Ślȩzak, D. (eds) Rough Sets and Knowledge Technology. RSKT 2007. Lecture Notes in Computer Science(), vol 4481. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72458-2_1

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  • DOI: https://doi.org/10.1007/978-3-540-72458-2_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72457-5

  • Online ISBN: 978-3-540-72458-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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