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Proposal for a Statistical Reduct Method for Decision Tables

  • Yuichi Kato
  • Tetsuro Saeki
  • Shoutarou Mizuno
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9436)

Abstract

Rough Sets theory is widely used as a method for estimating and/or inducing the knowledge structure of if-then rules from a decision table after a reduct of the table. The concept of the reduct is that of constructing the decision table by necessary and sufficient condition attributes to induce the rules. This paper retests the reduct by the conventional methods by the use of simulation datasets after summarizing the reduct briefly and points out several problems of their methods. Then a new reduct method based on a statistical viewpoint is proposed. The validity and usefulness of the method is confirmed by applying it to the simulation datasets and a UCI dataset. Particularly, this paper shows a statistical local reduct method, very useful for estimating if-then rules hidden behind the decision table of interest.

References

  1. 1.
    Pawlak, Z.: Rough sets. Int. J. Inf. Comput. Sci. 11(5), 341–356 (1982)zbMATHMathSciNetCrossRefGoogle Scholar
  2. 2.
    Grzymala-Busse, J.W.: LERS – A system for learning from examples based on rough sets. In: Słowiński, R. (ed.) Handbook of Applications and Advances of the Rough Sets Theory. Theory and Decision Library, vol. 11, pp. 3–18. Kluwer Academic Publishers, Netherlands (1992)CrossRefGoogle Scholar
  3. 3.
    Skowron, A., Rauser, C.M.: The Discernibility Matrix and Functions in Information Systems. In: Słowiński, R. (ed.) Handbook of Application and Advances of Rough Sets Theory. Theory and Decision Library, vol. 11, pp. 331–362. Kluwer Academic Publishers, Netherlands (1992)CrossRefGoogle Scholar
  4. 4.
    Pawlak, Z.: Rough set fundamentals; KFIS Autumn Coference Tutorial, pp. 1–32 (1996)Google Scholar
  5. 5.
    Ślęzak, D.: Various approaches to reasoning with frequency based decision reducts: a survey. In: Polkowski, L., Tsumoto, S., Lin, T.Y. (eds.) Rough Set Method and Applications, vol. 56, pp. 235–285. Physical-Verlag, Heidelberg (2000)CrossRefGoogle Scholar
  6. 6.
    Bao, Y.G., Du, X.Y., Deng, M.G., Ishii, N.: An efficient method for computing all reducts. Trans. Jpn. Soc. Artif. Intell. 19(3), 166–173 (2004)CrossRefGoogle Scholar
  7. 7.
    Asunction, A., Newman, D.J.: UCI Machine Learning Repository, University of California, School of Information and Computer Science, Irvine (2007). http://www.ics.edu/~mlearn/MlRepository.html
  8. 8.
    Matsubayashi, T., Kato, Y., Saeki, T.: A new rule induction method from a decision table using a statistical test. In: Li, T., Nguyen, H.S., Wang, G., Grzymala-Busse, J., Janicki, R., Hassanien, A.E., Yu, H. (eds.) RSKT 2012. LNCS, vol. 7414, pp. 81–90. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  9. 9.
    Kato, Y., Saeki, T., Mizuno, S.: Studies on the necessary data size for rule induction by STRIM. In: Lingras, P., Wolski, M., Cornelis, C., Mitra, S., Wasilewski, P. (eds.) RSKT 2013. LNCS, vol. 8171, pp. 213–220. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  10. 10.
    Kato, Y., Saeki, T., Mizuno, S.: Considerations on rule induction procedures by STRIM and their relationship to VPRS. In: Kryszkiewicz, M., Cornelis, C., Ciucci, D., Medina-Moreno, J., Motoda, H., Raś, Z.W. (eds.) RSEISP 2014. LNCS, vol. 8537, pp. 198–208. Springer, Heidelberg (2014) Google Scholar
  11. 11.
    Kato, Y., Saeki, T., Mizuno, S.: Proposal of a statistical test rule induction method by use of the decision table. Appl. Soft Comput. 28, 160–166 (2015). ElsevierCrossRefGoogle Scholar
  12. 12.
    Kotu, V., Deshpande, B.: Predictive Analytics and Data Mining, 1st edn. Elsevier, Amsterdam (2014)Google Scholar
  13. 13.
    Walpole, R.E., Myers, R.H., Myers, S.L., Ye, K.: Probability and Statistics for Engineers and Scientists, 8th edn. Pearson Prentice Hal, New Jersey (2007)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Shimane UniversityMatsue CityJapan
  2. 2.Yamaguchi UniversityUbe CityJapan

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