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

Abstract

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
PublisherSpringer
Pages314-324
Number of pages11
Volume3641
Edition3641
ISBN (Print)9783540286530
DOIs
Publication statusPublished - Sep 2005

Publication series

NameLecture notes in computer science
PublisherSpringer
Number3641
ISSN (Print)0302-9743

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