A Classifier Ensemble Method for Fuzzy Classifiers

  • Ai-min Yang
  • Yong-mei Zhou
  • Min Tang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4223)


In this paper, a classifier ensemble method based on fuzzy integral for fuzzy classifiers is proposed. The object of this method is to reduce subjective factor in building a fuzzy classifier, and to improve the classification recognition rate and stability for classification system. For this object, a method of determining fuzzy integral density based on membership matrix is proposed, and the classifier ensemble algorithm based on fuzzy integral is introduced. The method of selecting classifier sets is also presented. The proposed method is evaluated by the comparison of experiments with standard data sets and the existed classifier ensemble methods.


Recognition Rate Individual Classifier Training Pattern Classifier Ensemble Fuzzy Measure 
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  1. 1.
    Ludmila, I., Kuncheva: How good are fuzzy If-Then classifiers? IEEE Transactions on Systems, Man, and Cybernetics 30(4), 501–509 (2000)CrossRefGoogle Scholar
  2. 2.
    Cordon, O., del Jesus, M.J., Herrera, F.: A proposal on reasoning methods in fuzzy rule-based classification systems. Int. J. of Approximate Reasoning 20(1), 21–45 (1999)Google Scholar
  3. 3.
    Wierzechon, S.T.: On fuzzy measure and fuzzy integral. In: Fuzzy information and decision processes, pp. 78–86. North-Holland, New York (1982)Google Scholar
  4. 4.
    Schapire, R.E.: The strength of weak learn ability. Machine Learning 5(2), 197–227 (1990)Google Scholar
  5. 5.
    Breiman, L.: Bagging predictors. Machine Learning 24(2), 123–140 (1996)MATHMathSciNetGoogle Scholar
  6. 6.
    Turner, K., Gosh, J.: Error correlation and error reduction in ensemble classifiers. Dept. of ECE, University of Texas, Texas (1996)Google Scholar
  7. 7.
    Partridge, D.: Network generalization differences quantified. Neural Networks (9), 263–271 (1996)CrossRefGoogle Scholar
  8. 8.
    Partridge, D., Yates, W.B.: Engineering Multiversion Neural-Net Systems. Neural Computation (8), 869–893 (1998)CrossRefGoogle Scholar
  9. 9.
    Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
  10. 10.
    Zhi-Hua, Z., Shi-Fu, C.: Neural Network Ensemble. Chinese Journal of Computers 25(1), 1–8 (2005)Google Scholar
  11. 11.
    Xie-Dong, Z.: Fuzzy Information Managing and Application, pp. 174–176. Science Publishing Company, Beijing (2003)Google Scholar
  12. 12.
    Ai-Min, Y.: The Model Research on Fuzzy Classification. Doctor’s Degree Paper of Fudan University (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ai-min Yang
    • 1
  • Yong-mei Zhou
    • 1
    • 2
  • Min Tang
    • 1
  1. 1.Department of Computer ScienceHunan University of TechnologyZhuZhouChina
  2. 2.College of Information Science & EngineeringCentral South UniversityChangShaChina

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