Comparison of Two Models of Probabilistic Rough Sets

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• 作者：Bing Zhou (24)
  Yiyu Yao (25)
  
• 关键词：rough sets ; probabilistic approximations ; Bayesian inference ; decision ; theoretic rough sets ; confirmation ; theoretic rough sets
• 刊名：Lecture Notes in Computer Science
• 出版年：2013
• 出版时间：2013
• 年：2013
• 卷：8171
• 期：1
• 页码：133-144
• 全文大小：180KB
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• 作者单位：Bing Zhou (24)
  Yiyu Yao (25)
  
  24. Department of Computer Science, Sam Houston State University, Huntsville, Texas, USA, 77340
  25. Department of Computer Science, University of Regina, Regina, Saskatchewan, Canada, S4S 0A2
  
• ISSN：1611-3349

文摘

To generalize the classical rough set model, several proposals have been made by considering probabilistic information. Each of the proposed probabilistic models uses three regions for approximating a concept. Although the three regions are similar in form, they have different semantics and therefore are appropriate for different applications. In this paper, we present a comparative study of a decision-theoretic rough set model and a confirmation-theoretic rough set model. We argue that the former deals with drawing conclusions based on available evidence and the latter concerns evaluating difference pieces of evidence. By considering both models, we can obtain a more comprehensive understanding of probabilistic rough sets.