An Information-Theoretic Interpretation of Thresholds in Probabilistic Rough Sets

  • Xiaofei Deng
  • Yiyu Yao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7414)


In a probabilistic rough set model, the positive, negative and boundary regions are associated with classification errors or uncertainty. The uncertainty is controlled by a pair of thresholds defining the three regions. The problem of searching for optimal thresholds can be formulated as the minimization of uncertainty induced by the three regions. By using Shannon entropy as a measure of uncertainty, we present an information-theoretic approach to the interpretation and determination of thresholds.


Equivalence Class Boundary Region Shannon Entropy Conditional Entropy Entropy Minimization 
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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© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Xiaofei Deng
    • 1
  • Yiyu Yao
    • 1
  1. 1.Department of Computer ScienceUniversity of ReginaReginaCanada

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