Data-Driven Adaptive Selection of Rules Quality Measures for Improving the Rules Induction Algorithm

  • Marek Sikora
  • Łukasz Wróbel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6743)

Abstract

The proposition of adaptive selection of rule quality measures during rules induction is presented in the paper. In the applied algorithm the measures decide about a form of elementary conditions in a rule premise and monitor a pruning process. An influence of filtration algorithms on classification accuracy and a number of obtained rules is also presented. The analysis has been done on twenty one benchmark data sets.

Keywords

rules induction rules quality measures classification 

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References

  1. 1.
    An, A., Cercone, N.: Rule quality measures for rule induction systems: description and evaluation. Computational Intelligence 17(3), 409–424 (2001)CrossRefGoogle Scholar
  2. 2.
    Fürnkranz, J., Flach, P.A.: Roc‘n‘ Rule Learning - Towards a Better understanding of covering Algorithms. Machine Learning 58, 39–77 (2005)CrossRefMATHGoogle Scholar
  3. 3.
    Clark, P., Niblett, T.: The CN2 Induction Algorithm. Machine Learning 3(4), 261–283 (1989)Google Scholar
  4. 4.
    Cohen, W.W.: Fast effective rule induction. In: Proc. of the 12th Int. Conference ICML 1995, pp. 115–123 (1995)Google Scholar
  5. 5.
    Grzymaa-Busse, J.W., Ziarko, W.: Data mining based on rough sets. In: Wang, J. (ed.) Data Mining Opportunities and Challenges, pp. 142–173. IGI Publishing, Hershey (2003)CrossRefGoogle Scholar
  6. 6.
    Janssen, F., Fürnkranz, J.: On the quest for optimal rule learning heuristics. Machine Learning 78, 343–379 (2010)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Michalski, R.S., Mozetic, I., Hong, J., Lavrac, N.: The AQ15 inductive learning system: An overview and experiments. ISG Report No. 20. Department of Computer Sciences, University of Illinois at Urbana-Champaign (1986)Google Scholar
  8. 8.
    Mozina, M., Zabkar, J., Bratko, I.: Argument based machine learning. Artificial Intelligence 171, 922–937 (2007)MathSciNetCrossRefMATHGoogle Scholar
  9. 9.
    Sikora, M.: An algorithm for generalization of decision rules by joining. Foundation on Computing and Decision Sciences 30(3), 227–239 (2005)Google Scholar
  10. 10.
    Sikora, M.: Rule quality measures in creation and reduction of data rule models. In: Greco, S., Hata, Y., Hirano, S., Inuiguchi, M., Miyamoto, S., Nguyen, H.S., Słowiński, R. (eds.) RSCTC 2006. LNCS (LNAI), vol. 4259, pp. 716–725. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  11. 11.
    Sikora, M.: Decision rule-based data models using TRS and netTRS – methods and algorithms. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets XI. LNCS, vol. 5946, pp. 130–160. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  12. 12.
    Sikora, M., Gruca, A.: Induction and selection of the most interesting Gene Ontology based multiattribute rules for descriptions of gene groups. Pattern Recognition Letters 32, 258–269 (2011)CrossRefGoogle Scholar
  13. 13.
    Stefanowski, J.: Rough set based rule induction techniques for classification problems. In: Proc. 6th European Congress of Intelligent Techniques and Soft Computing, Achen, September 7-10, vol. 1, pp. 107–119 (1998)Google Scholar
  14. 14.
    Stefanowski, J., Vanderpooten, D.: Induction of Decision Rules in Classification and Discovery Oriented Perspectives. International Journal of Intelligent Systems 16, 13–27 (2001)CrossRefMATHGoogle Scholar
  15. 15.
    Webb, G.I.: Further experimental evidence against the utility of Occam‘s razor. Journal of Artificial Intelligence Research 4, 397–417 (1996)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Marek Sikora
    • 1
    • 2
  • Łukasz Wróbel
    • 1
  1. 1.Silesian University of TechnologyGliwicePoland
  2. 2.Institute of Innovative Technologies EMAGKatowicePoland

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