Quality of rough approximation in multi-criteria classification problems

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

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


    Dominance-based Rough Set Approach (DRSA) has been proposed to deal with multi-criteria classification problems, where data may be inconsistent with respect to the dominance principle. In this paper, we consider different measures of the quality of approximation, which is the value indicating how much inconsistent the decision table is. We begin with the classical definition, based on the relative number of inconsistent objects. Since this measure appears to be too restrictive in some cases, a new approach based on the concept of generalized decision is proposed. Finally, motivated by emerging problems in the presence of noisy data, the third measure based on the object reassignment is introduced. Properties of these measures are analysed in light of rough set theory.
    Original languageEnglish
    Title of host publicationRough sets and current trends in computing: proceedings of the 5th international conference
    EditorsSalvatore Greco, Y. Hata, S. Hirano, M. Inuiguchi, S. Miyamoto, H. Nguyen, R. Slowinski
    Place of PublicationBerlin
    Number of pages10
    ISBN (Print)9783540476931
    Publication statusPublished - 2006

    Publication series

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


    Dive into the research topics of 'Quality of rough approximation in multi-criteria classification problems'. Together they form a unique fingerprint.

    Cite this