A Possibilistic Rule-Based Classifier

  • Myriam Bounhas
  • Henri Prade
  • Mathieu Serrurier
  • Khaled Mellouli
Part of the Communications in Computer and Information Science book series (CCIS, volume 297)


Rule induction algorithms have gained a high popularity among machine learning techniques due to the “intelligibility” of their output, when compared to other “black-box” classification methods. However, they suffer from two main drawbacks when classifying test examples: i) the multiple classification problem when many rules cover an example and are associated with different classes, and ii) the choice of a default class, which concerns the non-covering case. In this paper we propose a family of Possibilistic Rule-based Classifiers (PRCs) to deal with such problems which are an extension and a modification of the Frank and Witten’ PART algorithm. The PRCs keep the same rule learning step as PART, but differ in other respects. In particular, the PRCs learn fuzzy rules instead of crisp rules, consider weighted rules at deduction time in an unordered manner instead of rule lists. They also reduce the number of examples not covered by any rule, using a fuzzy rule set with large supports. The experiments reported show that the PRCs lead to improve the accuracy of the classical PART algorithm.


possibilistic rule-based classifier fuzzy rules decision list 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Clark, P., Niblett, T.: The cn2 induction algorithm. Mach. Learn. J. 3, 261–283 (1989)Google Scholar
  2. 2.
    Cohen, W.: Fast effective rule induction. In: ICML, pp. 115–123 (1995)Google Scholar
  3. 3.
    Demsar, J.: Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research, 1–30 (2006)Google Scholar
  4. 4.
    Duch, W., Setiono, R., Zurada, J.: Computational intelligence methods for rule-based data understanding. Proceeding of IEEE 92 (2004)Google Scholar
  5. 5.
    Eineborg, M.: Boström. Classifying uncovered examples by rule stretching. In: Proceedings of the ICILP 2001, pp. 41–50 (2001)Google Scholar
  6. 6.
    Fawcett, T.: Prie: a system for generating rule lists to maximize roc performance. Data Mining and Knowledge Discovery 17, 207–224 (2008)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Frank, E., Witten, I.H.: Generating accurate rule sets without global optimization. In: Proc. of Int. Conf. in Mach. Learning, pp. 144–151 (1998)Google Scholar
  8. 8.
    Fürnkranz, J.: Separate-and-conquer rule learning. Art. Intel. Review 13, 3–54 (1999)zbMATHCrossRefGoogle Scholar
  9. 9.
    Hühn, J., Hüllermeier, E.: Furia: An algorithm for unordered fuzzy rule induction. Data Mining and Knowledge Discovery 19, 293–319 (2009)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Ishibuchi, H., Yamamoto, T.: Rule weight specification in fuzzy rule-based classification systems. IEEE Transactions on Fuzzy Systems 13, 428–436 (2005)CrossRefGoogle Scholar
  11. 11.
    Manchi, K.K., Wu, X.: Dynamic refinement of classification rules. In: 14th IEEE ICTAI 2002, p. 189 (2002)Google Scholar
  12. 12.
    Martin-Munoz, P., Moreno-Velo, F.J.: Fuzzycn2: An algorithm for extracting fuzzy classification rule lists. In: FUZZ-IEEE, pp. 1–7 (2010)Google Scholar
  13. 13.
    Mertz, J., Murphy, P.M.: Uci repository of machine learning databases,
  14. 14.
    Michalski, R.S., Mozetic, I., Hong, J., Lavrac, N.: The multi-purpose incremental learning system aq15 and its testing application to three medical domains. In: Proceedings of AAAI, pp. 1041–1045 (1986)Google Scholar
  15. 15.
    Quinlan, J.: C4.5: Programs for machine learning. Morgan Kaufmann (1993)Google Scholar
  16. 16.
    Dubois, D., Benferhat, S., Prade, H.: Representing default rules in possibilistic logic. In: Proceedings of the KRR 1992, pp. 673–684 (1992)Google Scholar
  17. 17.
    Serrurier, M., Prade, H.: Coping with exceptions in multiclass ilp problems using possibilistic logic. In: Proc of the IJCAI 2005, pp. 1761–1764 (2005)Google Scholar
  18. 18.
    Witten, I., Frank, E.: Data mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Myriam Bounhas
    • 1
  • Henri Prade
    • 2
  • Mathieu Serrurier
    • 2
  • Khaled Mellouli
    • 1
  1. 1.LARODEC LaboratoryISG de TunisLe BardoTunisie
  2. 2.IRIT, UPS-CNRSToulouse Cedex 09France

Personalised recommendations