GRS: A Generalized Rough Sets Model

  • Xiaohua Hu
  • Nick Cercone
  • Jianchao Han
  • Wojciech. Ziarko
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 95)


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.


Equivalence Class Decision Attribute Negative Class Positive Class Classification Error Rate 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Xiaohua Hu
    • 1
  • Nick Cercone
    • 2
  • Jianchao Han
    • 2
  • Wojciech. Ziarko
    • 3
  1. 1.Knowledge Stream PartnerBostonUSA
  2. 2.Dept. of Computer ScienceUnvi. of WaterlooWaterlooCanada
  3. 3.Dept. of Computer ScienceUniv. of ReginaReginaCanada

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