Advertisement

A Heuristic Algorithm for Attribute Reduction Based on Discernibility and Equivalence by Attributes

  • Yasuo Kudo
  • Tetsuya Murai
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5861)

Abstract

In this paper, we consider a heuristic method to partially calculate relative reducts with better evaluation by the evaluation criterion proposed by the authors. By considering discernibility and equivalence of elements with respect to values of condition attributes that appear in relative reducts, we introduce an evaluation criterion of condition attributes, and consider a heuristic method for calculating a relative reduct with better evaluation.

Keywords

Heuristic Algorithm Relative Reducts Conjunctive Normal Form Decision Table Decision Class 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Hedar, A.H., Wang, J., Fukushima, M.: Tabu search for attribute reduction in rough set theory. Soft Couping 12(9), 909–918 (2008)zbMATHCrossRefGoogle Scholar
  2. 2.
    Hu, F., Wang, G., Feng, L.: Fast Knowledge Reduction Algorithms Based on Quick Sort. In: Wang, G., Li, T., Grzymala-Busse, J.W., Miao, D., Skowron, A., Yao, Y. (eds.) RSKT 2008. LNCS (LNAI), vol. 5009, pp. 72–79. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  3. 3.
    Kudo, Y., Murai, T.: A Heuristic Algorithm for Selective Calculation of a Better Relative Reduct in Rough Set Theory. In: Nakamatsu, K., et al. (eds.) New Advances in Intelligent Decision Technologies. SCI, vol. 199, pp. 555–564. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  4. 4.
    Kudo, Y., Murai, T.: On an Approximate Calculation Method of a Relative Reduct Based on Classification by Condition Attributes. In: Proc. of the 25th Fuzzy System Symposium, SOFT (2009) (in Japanese)Google Scholar
  5. 5.
    Mori, N., Tanaka, H., Inoue, K.: Rough Sets and Kansei —Knowledge Acquisition and Reasoning from Kansei Data—. Kaibundo (2004) (in Japanese)Google Scholar
  6. 6.
    Nakaura, T., Kudo, Y., Murai, T.: On an Evaluation Method of Relative Reducts Based on Classification by Condition Attributes. In: Proc. of the 25th Fuzzy System Symposium, SOFT (2009) (in Japanese)Google Scholar
  7. 7.
    Pawlak, Z.: Rough Sets. International Journal of Computer and Information Science 11, 341–356 (1982)zbMATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Pawlak, Z.: On Rough Dependency of Attributes in Information Systems. Bulletin of the Polish Academy of Sciences, Technical Sciences 33, 481–485 (1985)zbMATHMathSciNetGoogle Scholar
  9. 9.
    Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1991)zbMATHGoogle Scholar
  10. 10.
    Pawlak, Z., Słowiński, R.: Rough Set Approach to Multi-Attribute Decision Analysis. European Journal of Operation Research 74, 443–459 (1994)CrossRefGoogle Scholar
  11. 11.
    Polkowski, L.: Rough Sets: Mathematical Foundations. In: Advances in Soft Computing. Physica-Verlag, New York (2002)Google Scholar
  12. 12.
    Skowron, A., Rauszer, C.M.: The discernibility matrix and functions in information systems. In: Słowiński, R. (ed.) Intelligent Decision Support: Handbook of Application and Advance of the Rough Set Theory, pp. 331–362. Kluwer Academic Publishers, Dordrecht (1992)Google Scholar
  13. 13.
    Ślȩzak, D.: Approximate Entropy Reducts. Fundamenta Informaticae 53(3-4), 365–387 (2002)MathSciNetGoogle Scholar
  14. 14.
  15. 15.
    Xu, J., Sun, L.: New Reduction Algorithm Based on Decision Power of Decision Table. In: Wang, G., Li, T., Grzymala-Busse, J.W., Miao, D., Skowron, A., Yao, Y. (eds.) RSKT 2008. LNCS (LNAI), vol. 5009, pp. 180–188. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  16. 16.
    Xu, Z., Zhang, C., Zhang, S., Song, W., Yang, B.: Efficient Attribute Reduction Based on Discernibility Matrix. In: Yao, J., Lingras, P., Wu, W.-Z., Szczuka, M.S., Cercone, N.J., Ślȩzak, D. (eds.) RSKT 2007. LNCS (LNAI), vol. 4481, pp. 13–21. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  17. 17.
    Yamaguchi, D.: On the Improvement of Pawlak’s Attribute Dependency Model. In: Proc. of the 2nd International Conference on Kansei Engineering and Affective Systems, JSKE, pp. 83–88 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Yasuo Kudo
    • 1
  • Tetsuya Murai
    • 2
  1. 1.Dept. of Computer Science and Systems Eng.Muroran Institute of TechnologyMuroranJapan
  2. 2.Graduate School of Information Science and TechnologyHokkaido UniversitySapporoJapan

Personalised recommendations