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Bayesian Confirmation Measures within Rough Set Approach

  • Salvatore Greco
  • Zdzisław Pawlak
  • Roman Słowiński
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3066)

Abstract

Bayesian confirmation theory considers a variety of non-equivalent confirmation measures quantifying the degree to which a piece of evidence supports a hypothesis. In this paper, we apply some of the most relevant confirmation measures within the rough set approach. Moreover, we discuss interesting properties of these confirmation measures and we propose a new property of monotonicity that is particularly relevant within rough set approach. The main result of this paper states which one of the confirmation measures considered in the literature have the desirable properties from the viewpoint of the rough set approach.

Keywords

Decision Rule Monotonicity Property Decision Algorithm Causal Decision Theory Decision Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Salvatore Greco
    • 1
  • Zdzisław Pawlak
    • 2
    • 3
  • Roman Słowiński
    • 4
    • 5
  1. 1.Faculty of EconomicsUniversity of CataniaCataniaItaly
  2. 2.Institute of Theoretical and Applied InformaticsPolish Academy of SciencesGliwicePoland
  3. 3.Warsaw School of Information TechnologyWarsawPoland
  4. 4.Institute of Computing SciencePoznań University of TechnologyPoznańPoland
  5. 5.Institute for Systems ResearchPolish Academy of SciencesWarsawPoland

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