GRS: a generalized rough sets model

Authors:
Xiaohua Hu;Nick Cercone;Jianchao Han;Wojciech Ziarko
Affiliations:
Knowledge Stream Partner, 148 State St., Boston, MA;Dept. of Computer Science, Unvi. of Waterloo, Waterloo, Ont., Canada;Dept. of Computer Science, Unvi. of Waterloo, Waterloo, Ont., Canada;Dept. of Computer Science, Univ. of Regina, Regina, Sask., Canada
Venue:
Data mining, rough sets and granular computing
Year:
2002

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Abstract

Rough sets extends classical set theory by incorporating the set model into the notion of classification in the form of an indiscernibility relation. Rough sets serves as a tool for data analysis and knowledge discovery from databases. A generalized rough sets model, based on the concept of the VPRS-model, is proposed in this paper. Our approach modifies the traditional rough sets model and is aimed at handling uncertain objects by considering the importance of each object while reducing the influence of noise in modelling the classification process.