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Applying Rough Sets to Information Tables Containing Possibilistic Values

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Transactions on Computational Science II

Part of the book series: Lecture Notes in Computer Science ((TCOMPUTATSCIE,volume 5150))

Abstract

Rough sets are applied to information tables containing imprecise values that are expressed in a normal possibility distribution. A method of weighted equivalence classes is proposed, where each equivalence class is accompanied by a possibilistic degree to which it is an actual one. By using a 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. When this method is applied to missing values interpreted possibilistically, it creates the same relation for indiscernibility as the method of Kryszkiewicz that gave an assumption for indiscernibility of missing values. Using weighted equivalence classes correctly derives a lower approximation from the viewpoint of possible worlds, although using a class of objects that is not an equivalence class does not always derive a lower approximation.

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Author information

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

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  1. Michinori Nakata
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  2. Hiroshi Sakai
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Calgary, 2500 University Drive N.W., Calgary, T2N 1N4, AB, Canada

    Marina L. Gavrilova

  2. OptimaNumerics Ltd., Cathedral House, 23-31 Waring Street, BT1 2DX, Belfast, UK

    C. J. Kenneth Tan

  3. Schulich School of Engineering, Department of Electrical and Computer Engineering, University of Calgary, 2500 University Drive, T2N 1N4, NW Calgary, AB, Canada

    Yingxu Wang

  4. Department of Computer Science, University of Regina, S4S 0A2, Regina, Saskatchewan, Canada

    Yiyu Yao

  5. School of Information Science & Technology, Southwest Jiaotong University, 610031, Chengdu, China

    Guoyin Wang

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Nakata, M., Sakai, H. (2008). Applying Rough Sets to Information Tables Containing Possibilistic Values. In: Gavrilova, M.L., Tan, C.J.K., Wang, Y., Yao, Y., Wang, G. (eds) Transactions on Computational Science II. Lecture Notes in Computer Science, vol 5150. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87563-5_11

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  • DOI: https://doi.org/10.1007/978-3-540-87563-5_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87562-8

  • Online ISBN: 978-3-540-87563-5

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