Abstract

The automatic generation of fuzzy systems have been widely investigated with several proposed approaches in the literature. Since for most methods the generation process complexity increases exponentially with the number of features, a previous feature selection can highly improve the process. Filters, wrappers and embedded methods are used for feature selection. For fuzzy systems it would be desirable to take the fuzzy granulation of the features domains into account for the feature selection process. In this paper a fuzzy wrapper, previously proposed by the authors, and a fuzzy C4.5 decision tree are used to select features. They are compared with three classic filters and the features selected by the original C4.5 decision tree algorithm, as an embedded method. Results using 10 datasets indicate that the use of the fuzzy granulation of features domains is an advantage to select features for the purpose of inducing fuzzy rule bases.

Keywords

Feature selection fuzzy classification methods C4.5 Fuzzy C4.5 machine learning 

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References

  1. 1.
    Cordon, O., Gomide, F.A.C., Herrera, F., Hoffmann, F., Magdalena, L.: Ten years of genetic fuzzy systems: Current framework and new trends. Fuzzy Sets and Systems 141(1), 5–31 (2004)MathSciNetCrossRefMATHGoogle Scholar
  2. 2.
    Dumitrescu, D., Lazzerini, B., Jair, L.: Fuzzy Sets and Their Application to Clustering and Training. Int. Series on Computational Intelligence. CBC Press (2000)Google Scholar
  3. 3.
    Zadeh, L.: Fuzzy sets. Information and Control 8, 338–353 (1965)MathSciNetCrossRefMATHGoogle Scholar
  4. 4.
    Sanchez, L., Villar, J.R., Couso, I.: Genetic feature selection for fuzzy discretized data. In: Proc. of the IPMU 2008, pp. 1159–1166 (2008)Google Scholar
  5. 5.
    Choi, Y.S., Moon, B.R.: Feature selection in genetic fuzzy discretization for pattern classification problems. IEICE - Trans. Inf. Syst. E90-D(7), 1047–1054 (2007)Google Scholar
  6. 6.
    Li, R.P., Mukaidono, M., Turksen, I.B.: A fuzzy neural network for pattern classification and feature selection. Fuzzy Sets and Systems 130(1), 101–108 (2002)MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    Grande, J., del Rosario Suarez, M., Villar, J.R.: Advances in Soft Computing - A Feature Selection Method Using a Fuzzy Mutual Information Measure, vol. 44. Springer, Heidelberg (2007)Google Scholar
  8. 8.
    Sánchez, L., Suárez, M.R., Villar, J.R., Couso, I.: Mutual information-based feature selection and fuzzy discretization of vague data. International Journal of Approximate Reasoning 49(3), 607–622 (2008)CrossRefGoogle Scholar
  9. 9.
    Alonso, J.M., Magdalena, L.: An interpretability-guided modeling process for learning comprehensible fuzzy rule-based classifiers. In: International Conference on Intelligent Systems Design and Applications, pp. 432–437 (2009)Google Scholar
  10. 10.
    Cintra, M.E., de Arruda Camargo, H., Monard, M.C.: Fuzzy feature subset selection using the Wang & Mendel method. In: HIS 2008, vol. 1, pp. 590–595 (2008)Google Scholar
  11. 11.
    Wang, L.: The WM method completed: a flexible fuzzy system approach to data mining. IEEE Transactions on Fuzzy Systems 11, 768–782 (2003)CrossRefGoogle Scholar
  12. 12.
    Yu, L., Liu, H.: Efficient feature selection via analysis of relevance and redundancy. The Journal of Machine Learning Research 5, 1205–1224 (2004)MathSciNetMATHGoogle Scholar
  13. 13.
    Hall, M.A.: Correlation-based feature selection for discrete and numeric class Machine Learning. In: Proceedings of the 17th International Conference on Machine Learning, pp. 359–366. Morgan Kaufmann, San Francisco (2000)Google Scholar
  14. 14.
    Kira, K., Rendell, L.: A practical approach to feature selection. International Conference on Machine Learning 1, 368–377 (1992)Google Scholar
  15. 15.
    Liu, H., Setiono, R.: A probabilistic approach to feature selection - a filter solution. In: Proceedings of the 13th International Conference on Machine Learning, vol. 1, pp. 319–327 (1996)Google Scholar
  16. 16.
    Das, S.: Filters, wrappers and a boosting-based hybrid for feature selection. In: Proceedings of the 18th Int. Conf. on Machine Learning, pp. 74–81 (2001)Google Scholar
  17. 17.
    Cintra, M.E., Martin, T.P., Monard, M.C., Camargo, H.A.: Feature subset selection using a fuzzy method. In: International Conference on Intelligent Human-Machine Systems and Cybernetics, vol. 2, pp. 214–217 (2009)Google Scholar
  18. 18.
    Quinlan, J.R.: C4.5 Programs for Machine Learning. Morgan Kaufmann, CA (1988)Google Scholar
  19. 19.
    Quinlan, J.R.: Bagging, boosting and c4.5. In: Proceedings of the 13th Conf. Artificial Intelligence, pp. 725–730 (1996)Google Scholar
  20. 20.
    Olaru, C., Wehenkel, L.: A complete fuzzy decision tree technique. Fuzzy Sets and Systems 138(2), 221–254 (2003)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Kazunor, H., Motohide, U., Hiroshi, S., Yuushi, U.: Fuzzy C4.5 for generating fuzzy decision trees and its improvement. Faji Shisutemu Shinpojiumu Koen Ronbunshu 15, 515–518 (1999)Google Scholar
  22. 22.
    Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, 2nd edn. Morgan Kaufmann, San Francisco (2005)MATHGoogle Scholar
  23. 23.
    Asuncion, A., Newman, D.: UCI machine learning repository (2007)Google Scholar
  24. 24.
    Cintra, M.E., Camargo, H.A., Martin, T.P.: Optimising the fuzzy granulation of attribute domains. In: International Fuzzy Systems Association World Conference, pp. 742–747 (2009)Google Scholar
  25. 25.
    Demšar, J.: Statistical comparison of classifiers over multiple data sets. Journal of Machine Learning Research 7(1), 1–30 (2006)MathSciNetMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Marcos E. Cintra
    • 1
  • Heloisa A. Camargo
    • 2
  1. 1.Mathematics and Computer Science InstituteUniversity of Sao Paulo (USP)Sao CarlosBrazil
  2. 2.Computer Science DepartmentFederal University of São Carlos (UFSCar)São Carlos

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