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International Conference on Modeling Decisions for Artificial Intelligence

MDAI 2007: Modeling Decisions for Artificial Intelligence pp 282–294Cite as

Applying Rough Sets to Information Tables Containing Probabilistic Values

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4617))

Abstract

Rough sets are applied to information tables containing imprecise values that are expressed in a probability distribution. A family of weighted equivalence classes is obtained where each equivalence class is accompanied by the probability to which it is an actual one. By using the family of weighted equivalence classes, we derive lower and upper approximations. The lower and upper approximations coincide with ones obtained from methods of possible worlds. Therefore, the method of weighted equivalence classes is justified. In addition, this method is applied to missing values interpreted probabilistically. Using weighted equivalence classes correctly derives a lower approximation, even in the case where the method of Kryszkiewicz does not derive any lower approximation.

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Authors and Affiliations

  1. Faculty of Management and Information Science, Josai International University, 1 Gumyo, Togane, Chiba, 283-8555, Japan

    Michinori Nakata

  2. Department of Mathematics and Computer Aided Sciences, Faculty of Engineering, Kyushu Institute of Technology, Tobata, Kitakyushu, 804-8550, Japan

    Hiroshi Sakai

Authors
  1. Michinori Nakata
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  2. Hiroshi Sakai
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Vicenç Torra Yasuo Narukawa Yuji Yoshida

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Nakata, M., Sakai, H. (2007). Applying Rough Sets to Information Tables Containing Probabilistic Values . In: Torra, V., Narukawa, Y., Yoshida, Y. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2007. Lecture Notes in Computer Science(), vol 4617. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73729-2_27

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  • DOI: https://doi.org/10.1007/978-3-540-73729-2_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73728-5

  • Online ISBN: 978-3-540-73729-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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