Decision-theoretic rough set models

  • Authors:
  • Yiyu Yao

  • Affiliations:
  • Department of Computer Science, University of Regina, Regina, Saskatchewan, Canada

  • Venue:
  • RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
  • Year:
  • 2007

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Abstract

Decision-theoretic rough set models are a probabilistic extension of the algebraic rough set model. The required parameters for defining probabilistic lower and upper approximations are calculated based on more familiar notions of costs (risks) through the well-known Bayesian decision procedure. We review and revisit the decision-theoretic models and present new results. It is shown that we need to consider additional issues in probabilistic rough set models.