JACIII Vol.10 No.5 pp. 657-665
doi: 10.20965/jaciii.2006.p0657


Structure-Based Attribute Reduction in Variable Precision Rough Set Models

Masahiro Inuiguchi

Department of Systems Innovation, Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama, Toyonaka, Osaka 560-8531, Japan

January 1, 2006
February 20, 2006
September 20, 2006
variable precision rough set, reduct, lower approximation, upper approximation, boundary region
In this paper, structure-enhancing approaches to attribute reduction are proposed. Ten kinds of meaningful reducts are defined. The relations among them are clarified. Moreover their relations to reducts by structure-preserving approaches are also investigated. A few computational approaches to the proposed reducts are briefly described.
Cite this article as:
M. Inuiguchi, “Structure-Based Attribute Reduction in Variable Precision Rough Set Models,” J. Adv. Comput. Intell. Intell. Inform., Vol.10 No.5, pp. 657-665, 2006.
Data files:
  1. [1] M. Beynon, “Reducts within the variable precision rough sets model: A further investigation,” European J. Oper. Res., Vol.134, pp. 592-605, 2001.
  2. [2] M. Inuiguchi, “Several approaches to attribute reduction in variable precision rough set model,” Modeling Decisions for Artificial Intelligence (MDAI 2005), Tsukuba, Japan, July 25-27, 2005, Berlin, Germany: Springer-Verlag, pp. 215-226, 2005.
  3. [3] M. Inuiguchi and M. Tsurumi, “On utilization of upper approximations in rough set analysis,” in Pro. Int. Workshop of Fuzzy Syst. & Innovational Comput., Fukuoka, Japan, June 2-3, 2004.
  4. [4] R. Jensen and Q. Shen, “Semantics-preserving dimensionality reduction: Rough and fuzzy-rough-based approaches,” IEEE Trans. Knowledge and Data Engineering, Vol.16, No.12, pp. 1457-1471, 2004.
  5. [5] Z. Pawlak, “Rough sets,” Int. J. of Inform. & Comput. Sci., Vol.11, No.5, pp. 341-356, 1982.
  6. [6] A. Skowron and C. M. Rauser, “The discernibility matrix and functions in information systems,” in R. Słowiński (Ed.), “Intelligent Decision Support,” Dordrecht, The Netherlands: Kluwer Academic Publishers, pp. 331-362, 1992.
  7. [7] D. Ślęzak, “Various approaches to reasoning with frequency based decision reducts: a survey,” in L. Polkowski, S. Tsumoto, T. Y. Lin (Eds.), “Rough Set Methods and Applications,” Heidelberg, Germany: Physica-Verlag, pp. 235-285, 2000.
  8. [8] D. Ślęzak and W. Ziarko, “Attribute reduction in the Bayesian version of variable precision rough set model,” Electr. Notes Theor. Comput. Sci., Vol.82, No.4, 2003.
  9. [9] S. Tsumoto, R. Słowiński, J. Komorowski, and J. W. Grzymala-Busse (Eds.), “Rough Sets and Current Trends in Computing,” Berlin, Germany: Springer-Verlag, 2004.
  10. [10] W. Ziarko, “Variable precision rough set model,” J. Comput. Syst. Sci., Vol.46, No.1, pp. 39-59, 1993.

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