Dependence-space-based attribute reductions in inconsistent decision information systems

  • Authors:

  • Yee Leung;Jian-Min Ma;Wen-Xiu Zhang;Tong-Jun Li

  • Affiliations:

  • Department of Geography and Resource Management, Center for Environmental Policy and Resource Management, and Institute of Space and Earth Information Science, The Chinese University of Hong Kong, ...;Department of Mathematics and Information Science, Faculty of Science, Chang'an University, Xi'an, Shaan'xi 710064, PR China;Institute for Information and System Sciences, Faculty of Science, Xi'an Jiaotong University, Xi'an, Shaan'xi 710049, PR China;Institute for Information and System Sciences, Faculty of Science, Xi'an Jiaotong University, Xi'an, Shaan'xi 710049, PR China

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

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Abstract

In rough set theory, attribute reduction is an important mechanism for knowledge discovery. This paper mainly deals with attribute reductions of an inconsistent decision information system based on a dependence space. Through the concept of inclusion degree, a generalized decision distribution function is first constructed. A decision distribution relation is then defined. On the basis of this decision distribution relation, a dependence space is proposed, and an equivalence congruence based on the indiscernibility attribute sets is also obtained. Applying the congruences on a dependence space, new approaches to find a distribution consistent set are formulated. The judgement theorems for judging distribution consistent sets are also established by using these congruences and the decision distribution relation.