A Note on Attribute Reduction in the Decision-Theoretic Rough Set Model

  • Y. Zhao
  • S. K. M. Wong
  • Y. Y. Yao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5306)


This paper considers two groups of studies on attribute reduction in the decision-theoretic rough set model. Attribute reduction can be interpreted based on either decision preservation or region preservation. According to the fact that probabilistic regions are non-monotonic with respect to set inclusion of attributes, attribute reduction for region preservation is different from the classical interpretation of reducts.


Equivalence Class Positive Region Attribute Reduction Decision Class Positive Decision 
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  1. 1.
    Beaubouef, T., Petry, F.E., Arora, G.: Information-theoretic measures of uncertainty for rough sets and rough relational databases. Information Sciences 109, 185–195 (1998)CrossRefGoogle Scholar
  2. 2.
    Beynon, M.: Reducts within the variable precision rough sets model: a further investigation. European Journal of Operational Research 134, 592–605 (2001)CrossRefMATHGoogle Scholar
  3. 3.
    Hu, Q., Yu, D., Xie, Z.: Information-preserving hybrid data reduction based on fuzzy-rough techniques. Pattern Recognition Letters 27, 414–423 (2006)CrossRefGoogle Scholar
  4. 4.
    Inuiguchi, M.: Attribute reduction in variable precision rough set model. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 14, 461–479 (2006)MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    Inuiguchi, M.: Structure-based attribute reduction in variable precision rough set models. Journal of Advanced Computational Intelligence and Intelligent Informatics 10, 657–665 (2006)CrossRefGoogle Scholar
  6. 6.
    Katzberg, J.D., Ziarko, W.: Variable precision rough sets with asymmetric bounds. In: Ziarko, W. (ed.) Rough Sets, Fuzzy Sets and Knowledge Discovery, pp. 167–177. Springer, London (1994)CrossRefGoogle Scholar
  7. 7.
    Kryszkiewicz, M.: Maintenance of reducts in the variable precision rough sets model, ICS Research Report 31/94, Warsaw University of Technology (1994)Google Scholar
  8. 8.
    Mi, J.S., Wu, W.Z., Zhang, W.X.: Approaches to knowledge reduction based on variable precision rough set model. Information Sciences 159, 255–272 (2004)MathSciNetCrossRefMATHGoogle Scholar
  9. 9.
    Miao, D.Q., Hu, G.R.: A heuristic algorithm for reduction of knowledge. Chinese Journal of Computer Research and Development 36, 681–684 (1999)Google Scholar
  10. 10.
    Pawlak, Z.: Rough sets. International Journal of Computer and Information Sciences 11, 341–356 (1982)MathSciNetCrossRefMATHGoogle Scholar
  11. 11.
    Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishers, Boston (1991)CrossRefMATHGoogle Scholar
  12. 12.
    Pawlak, Z., Wong, S.K.M., Ziarko, W.: Rough sets: probabilistic versus deterministic approach. International Journal of Man-Machine Studies 29, 81–95 (1988)CrossRefMATHGoogle Scholar
  13. 13.
    Skowron, A.: Boolean reasoning for decision rules generation. In: Proceedings of the International Symposium on Methodologies for Intelligent Systems, pp. 295–305 (1993)Google Scholar
  14. 14.
    Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In: Slowiński, R. (ed.) Intelligent Decision Support, Handbook of Applications and Advances of the Rough Sets Theory. Kluwer, Dordrecht (1992)Google Scholar
  15. 15.
    Slezak, D.: Approximate reducts in decision tables. Proceedings of Information Processing and Management of Uncertainty, 1159–1164 (1996)Google Scholar
  16. 16.
    Slezak, D.: Normalized decision functions and measures for inconsistent decision tables analysis. Fundamenta Informaticae 44, 291–319 (2000)MathSciNetMATHGoogle Scholar
  17. 17.
    Swiniarski, R.W.: Rough sets methods in feature reduction and classification. International Journal of Applied Mathematics and Computer Science 11, 565–582 (2001)MathSciNetMATHGoogle Scholar
  18. 18.
    Wang, G.Y., Zhao, J., Wu, J.: A comparitive study of algebra viewpoint and information viewpoint in attribute reduction. Foundamenta Informaticae 68, 1–13 (2005)Google Scholar
  19. 19.
    Yao, Y.Y.: Decision-theoretic rough set models. In: Yao, J., Lingras, P., Wu, W.-Z., Szczuka, M.S., Cercone, N.J., Ślȩzak, D. (eds.) RSKT 2007. LNCS (LNAI), vol. 4481, pp. 1–12. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  20. 20.
    Yao, Y.Y., Wong, S.K.M.: A decision theoretic framework for approximating concepts. International Journal of Man-machine Studies 37, 793–809 (1992)CrossRefGoogle Scholar
  21. 21.
    Yao, Y.Y., Wong, S.K.M., Lingras, P.: A decision-theoretic rough set model. In: Ras, Z.W., Zemankova, M., Emrich, M.L. (eds.) Methodologies for Intelligent Systems, vol. 5, pp. 17–24. North-Holland, New York (1990)Google Scholar
  22. 22.
    Yao, Y.Y., Zhao, Y.: Attribute reduction in decision-theoretic rough set models. Information Sciences 178, 3356–3373 (2008)MathSciNetCrossRefMATHGoogle Scholar
  23. 23.
    Zhang, W.X., Mi, J.S., Wu, W.Z.: Knowledge reduction in inconsistent information systems. Chinese Journal of Computers 1, 12–18 (2003)MathSciNetGoogle Scholar
  24. 24.
    Ziarko, W.: Variable precision rough set model. Journal of Computer and System Sciences 46, 39–59 (1993)MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Y. Zhao
    • 1
  • S. K. M. Wong
    • 1
  • Y. Y. Yao
    • 1
  1. 1.Department of Computer ScienceUniversity of ReginaReginaCanada

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