A New Rule Induction Method from a Decision Table Using a Statistical Test

  • Tsukasa Matsubayashi
  • Yuichi Kato
  • Tetsuro Saeki
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7414)

Abstract

Rough Sets theory provides a method of estimating and/or inducing knowledge structure of if-then rules from various databases, using approximations of accuracy and coverage indices. Several recent studies have examined the confidence of these indices. In these studies their estimated rules were based on a sample data set obtained from a population, and the sampling affects the confidence of the estimation. However, these studies of the quality of the approximation evaluate the effects on rule estimation indirectly. In this paper, we propose a new rule induction method by statistical testing which directly contains the effect of sampling. The validity and usefulness of our method are confirmed by a computer simulation experiment and comparison of the results with those by other well-known methods.

Keywords

Decision Table Rule Induction Coverage Index Discernibility Matrix Rule Estimation 
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.
    Pawlak, Z.: Rough sets. Internat. J. Inform. Comput. Sci. 11(5), 341–356 (1982)MathSciNetMATHCrossRefGoogle Scholar
  2. 2.
    Skowron, A., Rauser, C.M.: The Discernibility Matrix and Functions in Information Systems. In: Słowiński, R. (ed.) Intelligent Decision Support, Handbook of Application and Advances of Rough Set Theory, pp. 331–362. Kluwer Academic Publishers (1992)Google Scholar
  3. 3.
    Bao, Y.G., Du, X.Y., Deng, M.G., Ishii, N.: An Efficient Method for Computing All Reducts. Transactions of the Japanese Society for Artificial Intelligence 19(3), 166–173 (2004)CrossRefGoogle Scholar
  4. 4.
    Grzymala-Busse, J.W.: LERS- A system for learning from examples based on rough sets. In: Słowiński, R. (ed.) Intelligent Decision Support. Handbook of Applications and Advances of the Rough Sets Theory, pp. 3–18. Kluwer Academic Publishers (1992)Google Scholar
  5. 5.
    Ziarko, W.: Variable precision rough set model. Journal of Computer and System Science 46, 39–59 (1993)MathSciNetMATHCrossRefGoogle Scholar
  6. 6.
    Shan, N., Ziarko, W.: Data-based acquisition and incremental modification of classification rules. Computational Intelligence 11(2), 357–370 (1995)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Nishimura, T., Kato, Y., Saeki, T.: Studies on an Effective Algorithm to Reduce the Decision Matrix. In: Kuznetsov, S.O., Ślęzak, D., Hepting, D.H., Mirkin, B.G. (eds.) RSFDGrC 2011. LNCS (LNAI), vol. 6743, pp. 240–243. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  8. 8.
    Gediga, G., Düntsch, I.: Statistical technique for rough set data analysis. In: Polkowski, L., et al. (eds.) Rough Set Methods and Applications: New Developments in Knowledge Discovery in Information System, pp. 545–565. Physica Verlag, Heidelberg (2000)Google Scholar
  9. 9.
    Tsumoto, S.: Accuracy and Coverage in Rough Set Rule Induction. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds.) RSCTC 2002. LNCS (LNAI), vol. 2475, pp. 373–380. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  10. 10.
    Gillet, F., Hamilton, H. (eds.): Quality Measures in Data Mining. SCI, vol. 43. Springer, Heidelberg (2007)Google Scholar
  11. 11.
    Jaworski, W.: Rule Induction: Combining Rough Set and Statistical Approaches. In: Chan, C.-C., Grzymala-Busse, J.W., Ziarko, W.P. (eds.) RSCTC 2008. LNCS (LNAI), vol. 5306, pp. 170–180. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  12. 12.
    Laboratory of Intelligent Decision Support System (IDSS), http://idss.cs.put.poznan.pl/site/idss-en.html
  13. 13.
    Asuncion, A., Newman, D.J.: UCI Machine Learning Repository, University of California, School of Information and Computer Science, Irvine (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Tsukasa Matsubayashi
    • 1
  • Yuichi Kato
    • 1
  • Tetsuro Saeki
    • 2
  1. 1.Shimane UniversityMatsue cityJapan
  2. 2.Yamaguchi UniversityUbe cityJapan

Personalised recommendations