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Generalizations of Rough Sets: From Crisp to Fuzzy Cases

  • Masahiro Inuiguchi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3066)

Abstract

Rough sets can be interpreted in two ways: classification of objects and approximation of a set. In this paper, we discuss the differences and similarities of generalized rough sets based on those two different interpretations. We describe the relations between generalized rough sets and types of extracted decision rules. Moreover, we extend the discussion to fuzzy rough sets. Through this paper, the relations among generalized crisp rough sets and fuzzy rough sets are clarified and two different directions of applications in rule extraction are suggested.

Keywords

Decision Rule Extensive Relation Granular Computing Conceivable Region Fuzzy Case 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Masahiro Inuiguchi
    • 1
  1. 1.Department of Systems Innovation, Graduate School of Engineering ScienceOsaka UniversityOsakaJapan

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