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Knowledge Reduction in Inconsistent Decision Tables

  • Qihe Liu
  • Leiting Chen
  • Jianzhong Zhang
  • Fan Min
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4093)

Abstract

In this paper, we introduce a new type of reducts called the λ-Fuzzy-Reduct, where the fuzzy similarity relation is constructed by means of cosine-distances of decision vectors and the parameter λ is used to tune the similarity precision level. The λ-Fuzzy-Reduct can eliminate harsh requirements of the distribution reduct, and it is more flexible than the maximum distribution reduct, the traditional reduct, and the generalized decision reduct. Furthermore, we prove that the distribution reduct, the maximum distribution reduct, and the generalized decision reduct can be converted into the traditional reduct. Thus in practice the implementations of knowledge reductions for the three types of reducts can be unified into efficient heuristic algorithms for the traditional reduct. We illustrate concepts and methods proposed in this paper by an example.

Keywords

Decision Table Decision Vector Decision Class Maximum Distribution Fuzzy Equivalence Relation 
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 2006

Authors and Affiliations

  • Qihe Liu
    • 1
  • Leiting Chen
    • 1
  • Jianzhong Zhang
    • 1
  • Fan Min
    • 1
  1. 1.College of Computer Science and EngineeringUniversity of Electronic Science and, Technology of ChinaChengduChina

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