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Applying Rough Sets to Data Tables Containing Possibilistic Information

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Rough Sets and Knowledge Technology (RSKT 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4062))

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Abstract

Rough sets are applied to data tables containing possibilistic information. A family of weighted equivalence classes is obtained, in which each equivalence class is accompanied by a possibilistic degree to which it is an actual one. By using the family of weighted equivalence classes we can derive a lower approximation and an upper approximation. The lower approximation and the upper approximation coincide with those obtained from methods of possible worlds. Therefore, the method of weighted equivalence classes is justified.

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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

Authors
  1. Michinori Nakata
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  2. Hiroshi Sakai
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Editor information

Editors and Affiliations

  1. College of Computer Science and Technology, Chongqing, University of Posts and Telecommunication, 400065, Chongqing, P.R. China

    Guo-Ying Wang

  2. Department of Electrical and Computer Engineering, University of Manitoba, R3T 5V6, Winnipeg, Manitoba, Canada

    James F. Peters

  3. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron

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

    Yiyu Yao

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© 2006 Springer-Verlag Berlin Heidelberg

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Nakata, M., Sakai, H. (2006). Applying Rough Sets to Data Tables Containing Possibilistic Information. In: Wang, GY., Peters, J.F., Skowron, A., Yao, Y. (eds) Rough Sets and Knowledge Technology. RSKT 2006. Lecture Notes in Computer Science(), vol 4062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11795131_22

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  • DOI: https://doi.org/10.1007/11795131_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-36297-5

  • Online ISBN: 978-3-540-36299-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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