Statistical model for rough set approach to multicriteria classification

K. Dembczynski, Salvatore Greco, W. Kotlowski, R. Slowinski

    Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review


    In order to discover interesting patterns and dependencies in data, an approach based on rough set theory can be used. In particular, Dominance-based Rough Set Approach (DRSA) has been introduced to deal with the problem of multicriteria classification. However, in real-life problems, in the presence of noise, the notions of rough approximations were found to be excessively restrictive, which led to the proposal of the Variable Consistency variant of DRSA. In this paper, we introduce a new approach to variable consistency that is based on maximum likelihood estimation. For two-class (binary) problems, it leads to the isotonic regression problem. The approach is easily generalized for the multi-class case. Finally, we show the equivalence of the variable consistency rough sets to the specific risk-minimizing decision rule in statistical decision theory.
    Original languageEnglish
    Title of host publicationKnowledge discovery in databases: proceedings of the 11th european conference
    EditorsJ. Kok, J. Koronacki, R. Lopez de Mantaras, S. Matwin, D. Mladenic, A. Skowron
    Place of PublicationBerlin
    Number of pages12
    ISBN (Print)9783540749752
    Publication statusPublished - 2007

    Publication series

    NameLecture notes in computer science
    ISSN (Print)0302-9743


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