Rough membership and Bayesian confirmation measures for parameterized rough sets

Salvatore Greco, B. Matarazzo, R. Slowinski

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


    A generalization of the original idea of rough sets and variable precision rough sets is introduced. This generalization is based on the concept of absolute and relative rough membership. Similarly to variable precision rough set model, the generalization called parameterized rough set model, is aimed at modeling data relationships expressed in terms of frequency distribution rather than in terms of a full inclusion relation used in the classical rough set approach. However, differently from variable precision rough set model, one or more parameters modeling the degree to which the condition attribute values confirm the decision attribute value, are considered. The properties of this extended model are investigated and compared to the classical rough set model and the variable precision rough set model.
    Original languageEnglish
    Title of host publicationRough Sets, Fuzzy Sets, Data Mining, and Granular Computing: Proceedings of the 10th International Conference. Vol. 1
    EditorsD. Slezak, G. Wang, M. Szczuka, I. Duntsch, Y. Yao
    Place of PublicationBerlin
    Number of pages11
    ISBN (Print)9783540286530
    Publication statusPublished - Sept 2005

    Publication series

    NameLecture Notes in Computer Science
    ISSN (Print)0302-9743


    Dive into the research topics of 'Rough membership and Bayesian confirmation measures for parameterized rough sets'. Together they form a unique fingerprint.

    Cite this