An extension of rough approximation quality to fuzzy classification

  • Authors:

  • Van-Nam Huynh;Tetsuya Murai;Tu-Bao Ho;Yoshiteru Nakamori

  • Affiliations:

  • Japan Advanced Institute of Science and Technology, Ishikawa, Japan;Graduate School of Information Science and Engineering, Hokkaido University, Sapporo, Japan;Japan Advanced Institute of Science and Technology, Ishikawa, Japan;Japan Advanced Institute of Science and Technology, Ishikawa, Japan

  • Venue:
  • RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
  • Year:
  • 2005

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Abstract

In this paper, to deal with practical situations where a fuzzy classification must be approximated by available knowledge expressed in terms of a Pawlak's approximation space, we investigate an extension of approximation quality measure to a fuzzy classification aimed at providing a numerical characteristic for such situations. Furthermore, extensions of related coefficients such as the precision measure and the significance measure are also discussed. A simple example is given to illustrate the proposed notions.