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A New Classifier Design with Fuzzy Functions

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4482)

Abstract

This paper presents a new fuzzy classifier design, which constructs one classifier for each fuzzy partition of a given system. The new approach, namely Fuzzy Classifier Functions (FCF), is an adaptation of our generic design on Fuzzy Functions to classification problems. This approach couples any fuzzy clustering algorithm with any classification method, in a unique way. The presented model derives fuzzy functions (rules) from data to classify patterns into number of classes. Fuzzy c-means clustering is used to capture hidden fuzzy patterns and a linear or a non-linear classifier function is used to build one classifier model for each pattern identified. The performance of each classifier is enhanced by using corresponding membership values of the data vectors as additional input variables. FCF is proposed as an alternate representation and reasoning schema to fuzzy rule base classifiers. The proposed method is evaluated by the comparison of experiments with the standard classifier methods using cross validation on test patterns.

Keywords

Fuzzy classification fuzzy c-means clustering SVM 
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References

  1. 1.
    Abe, S., Thawonmas, R.: A fuzzy classifier with ellipsoidal regions. IEEE Trans. Fuzzy Syst. 5, 358–368 (1997)CrossRefGoogle Scholar
  2. 2.
    Bezdek, J.-C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)CrossRefzbMATHGoogle Scholar
  3. 3.
    Bradzil, P.B., Soares, C.: A Comparison of Ranking Methods for Classification Algorithm Selection. In: Lopez de Mantaras, R., Plaza, E. (eds.) ECML 2000. LNCS (LNAI), vol. 1810, Springer, Heidelberg (2000)Google Scholar
  4. 4.
    Chang, X., Lilly, J.H.: Evolutionary Design of a fuzzy classifier from data. IEEE Trans. On System, Man and Cyber. B. 34, 1894–1906 (2004)CrossRefGoogle Scholar
  5. 5.
    Chang, C.-C., Lin, C.-J.: LIBSVM: A library for support vector machines (2001), Software available http://www.csie.ntu.edu.tw/~cjlin/libsvm
  6. 6.
    Çelikyilmaz, A., Türkşen, I.B.: Fuzzy Functions with Support Vector Machines. Information Sciences (2007)Google Scholar
  7. 7.
    Demsar, J.: Statistical Comparisons of Classifiers over Multiple Data Sets. Journal of Machine Learning Research 7, 1–30 (2006)MathSciNetzbMATHGoogle Scholar
  8. 8.
    Doganay, M.M., Ceylan, N.B., Aktas, R.: Predicting Financial Failure of the Turkish Banks. Annals of Financial Economics 2 (forthcoming)Google Scholar
  9. 9.
    Ducker, H.D., Wu, D., Vapnik, V.: Support Vector Machines for spam categorization. IEEE Transaction on Neural Networks 10(5), 1048–1054 (1999)CrossRefGoogle Scholar
  10. 10.
    Hanley, J.A., McNeil, B.J.: The meaning and use of area under a receiver operating characteristic (ROC) curve. Radiology (1992)Google Scholar
  11. 11.
    Huerta, E.B., Duval, B., Hao, J.-K.: A Hybrid Bayesian Optimal Classifier Based on Neuro-fuzzy Logic. In: Applications of Evolutionary Computing, vol. 3907, pp. 34–77. Springer, Heidelberg (2000)Google Scholar
  12. 12.
    Huang, M.-L., Chen, H.-Y., Huang, J.-J.: Glaucoma detection using adaptive neuro-fuzzy inference system. Expert Systems with Applications 32, 458–468 (2007)CrossRefGoogle Scholar
  13. 13.
    Kecman, V.: Learning and Soft Computing: Support Vector Machines. Neural Networks, and Fuzzy Logic Models. MIT Press, Cambridge (2001)zbMATHGoogle Scholar
  14. 14.
    Klawonn, F., Nauck, D., Kruse, R.: Generating rules from data by fuzzy and neuro-fuzzy methods. In: Proc. Fuzzy Neuro-System, pp. 223–230 (1995)Google Scholar
  15. 15.
    Kuncheva, L.I.: Fuzzy Classifier Design. Studies in Fuzziness and Soft Computing, vol. 49. Springer, Heidelberg (2000)zbMATHGoogle Scholar
  16. 16.
    Lin, H.-T., Lin, C.-J., Weng, R.C.: A note on Platt’s probabilistic outputs for support vector machines. Technical report (2003)Google Scholar
  17. 17.
    Newman, D.J., et al.: UCI Repository of machine learning databases. Department of Information and Computer Science, University of California, Irvine, CA (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
  18. 18.
    Platt, J.: Probabilistic outputs for support vector machines and comparison to regularized likelihood methods. MIT Press, Cambridge (2000)Google Scholar
  19. 19.
    Setnes, M., Babuska, R.: Fuzzy relational classifier trained by fuzzy clustering. IEEE Trans. Syst. Man, Cybern. B 29, 619–625 (1999)CrossRefGoogle Scholar
  20. 20.
    Türkşen, I.B., Çelikyılmaz, A.: Comparison of Fuzzy Functions with Fuzzy Rule Base Approaches. Int. Journal of Fuzzy Systems 8(3), 137–149 (2006)MathSciNetGoogle Scholar
  21. 21.
    Türkşen, I.B.: Fuzzy Functions with LSE. Applied Soft Computing (forthcoming)Google Scholar
  22. 22.
    Wang, L.P. (ed.): Support Vector Machines: Theory and Application. Studies in Fuzziness and Soft Computing. Springer, Heidelberg (2005)Google Scholar
  23. 23.
    Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  1. 1.Dept. of Mechanical and Industrial Engineering, University of TorontoCanada
  2. 2.Dept. of Industrial Engineering TOBB-Economics and Technology UniversityTurkey
  3. 3.Dept. of Business Administration TOBB-Economics and Technology UniversityTurkey
  4. 4.Dept. of Business Administration Çankaya UniversityTurkey
  5. 5.Dept. of Business Administration Atılım UniversityTurkey

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