Parameterized rough set model using rough membership and bayesian confirmation measures

Salvatore Greco, B. Matarazzo, R. Slowinski

Research output: Contribution to journalArticlepeer-review

Abstract

A generalization of the original definition 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 definition of rough sets. However, differently from the 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 to the variable precision rough set model.
Original languageEnglish
Pages (from-to)285-300
Number of pages16
JournalInternational Journal of Approximate Reasoning
Volume49
Issue number2
DOIs
Publication statusPublished - Oct 2008

Fingerprint

Dive into the research topics of 'Parameterized rough set model using rough membership and bayesian confirmation measures'. Together they form a unique fingerprint.

Cite this