Parameterized rough set model using rough membership and Bayesian confirmation measures

  • Authors:

  • Salvatore Greco;Benedetto Matarazzo;Roman Słowiński

  • Affiliations:

  • Faculty of Economics, University of Catania, Corso Italia, 55, 95129 Catania, Italy;Faculty of Economics, University of Catania, Corso Italia, 55, 95129 Catania, Italy;Institute of Computing Science, Poznań University of Technology, 60-965 Poznań, Poland and Systems Research Institute, Polish Academy of Sciences, 01-447 Warsaw, Poland

  • Venue:
  • International Journal of Approximate Reasoning
  • Year:
  • 2008

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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.