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Rough Sets and Bayes Factor

  • Conference paper
Transactions on Rough Sets III

Part of the book series: Lecture Notes in Computer Science ((TRS,volume 3400))

Abstract

We present a novel approach to understanding the concepts of the theory of rough sets in terms of the inverse probabilities derivable from data. It is related to the Bayes factor known from the Bayesian hypothesis testing methods. The proposed Rough Bayesian model (RB) does not require information about the prior and posterior probabilities in case they are not provided in a confirmable way. We discuss RB with respect to its correspondence to the original Rough Set model (RS) introduced by Pawlak and Variable Precision Rough Set model (VPRS) introduced by Ziarko. We pay a special attention on RB’s capability to deal with multi-decision problems. We also propose a method for distributed data storage relevant to computational needs of our approach.

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Author information

Authors and Affiliations

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

    Dominik Ślęzak

Authors
  1. Dominik Ślęzak
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Manitoba, R3T 5V6, Winnipeg, Manitoba, Canada

    James F. Peters

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

    Andrzej Skowron

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© 2005 Springer-Verlag Berlin Heidelberg

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Ślęzak, D. (2005). Rough Sets and Bayes Factor. In: Peters, J.F., Skowron, A. (eds) Transactions on Rough Sets III. Lecture Notes in Computer Science, vol 3400. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427834_10

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  • DOI: https://doi.org/10.1007/11427834_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25998-5

  • Online ISBN: 978-3-540-31850-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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