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Rough Membership and Bayesian Confirmation Measures for Parameterized Rough Sets

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 3641)

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.

The research of the first two authors has been supported by the Italian Ministry of Education, University and Scientific Research (MIUR). The third author wishes to acknowledge financial support from the State Committee for Scientific Research (KBN).

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Greco, S., Matarazzo, B., Słowiński, R. (2005). Rough Membership and Bayesian Confirmation Measures for Parameterized Rough Sets. In: Ślęzak, D., Wang, G., Szczuka, M., Düntsch, I., Yao, Y. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2005. Lecture Notes in Computer Science(), vol 3641. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11548669_33

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  • DOI: https://doi.org/10.1007/11548669_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28653-0

  • Online ISBN: 978-3-540-31825-5

  • eBook Packages: Computer ScienceComputer Science (R0)