Shaping the Error-Reject Curve of Error Correcting Output Coding Systems

  • Paolo Simeone
  • Claudio Marrocco
  • Francesco Tortorella
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6978)

Abstract

A common approach in many classification tasks consists in reducing the costs by turning as many errors as possible into rejects. This can be accomplished by introducing a reject rule which, working on the reliability of the decision, aims at increasing the performance of the classification system. When facing multiclass classification, Error Correcting Output Coding is a diffused and successful technique to implement a system by decomposing the original problem into a set of two class problems. The novelty in this paper is to consider different levels where the reject can be applied in the ECOC systems. A study for the behavior of such rules in terms of Error-Reject curves is also proposed and tested on several benchmark datasets.

Keywords

Error-Reject Curve reject option multiclass problem Error Correcting Output Coding 

References

  1. 1.
    Allwein, E.L., Schapire, R.E., Singer, Y.: Reducing multiclass to binary: A unifying approach for margin classifiers. Journal of Machine Learning Research 1, 113–141 (2000)MathSciNetMATHGoogle Scholar
  2. 2.
    Asuncion, A., Newman, D.J.: UCI machine learning repository (2007)Google Scholar
  3. 3.
    Bartlett, P.L., Wegkamp, M.H.: Classification with a reject option using a hinge loss. J. Mach. Learn. Res. 9, 1823–1840 (2008)MathSciNetMATHGoogle Scholar
  4. 4.
    Chow, C.: On optimum recognition error and reject tradeoff. IEEE Transactions on Information Theory 16(1), 41–46 (1970)MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    Cordella, L.P., De Stefano, C., Tortorella, F., Vento, M.: A method for improving classification reliability of multilayer perceptrons. IEEE Transactions on Neural Networks 6(5), 1140–1147 (1995)CrossRefGoogle Scholar
  6. 6.
    Dietterich, T.G., Bakiri, G.: Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research 2, 263–286 (1995)MATHGoogle Scholar
  7. 7.
    Guruswami, V., Sahai, A.: Multiclass learning, boosting, and error-correcting codes. In: Proceedings of the Twelfth Annual Conference on Computational Learning Theory, COLT 1999, pp. 145–155. ACM, New York (1999)CrossRefGoogle Scholar
  8. 8.
    Joachims, T.: Making large-scale SVM learning practical. In: Schölkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods - Support Vector Learning, ch. 11. MIT Press, Cambridge (1999)Google Scholar
  9. 9.
    Pietraszek, T.: On the use of ROC analysis for the optimization of abstaining classifiers. Machine Learning 68(2), 137–169 (2007)CrossRefGoogle Scholar
  10. 10.
    Simeone, P., Marrocco, C., Tortorella, F.: Exploiting system knowledge to improve ecoc reject rules. In: International Conference on Pattern Recognition, pp. 4340–4343. IEEE Computer Society, Los Alamitos (2010)Google Scholar
  11. 11.
    De Stefano, C., Sansone, C., Vento, M.: To reject or not to reject: that is the question-an answer in case of neural classifiers. IEEE Transactions on Systems, Man, and Cybernetics, Part C 30(1), 84–94 (2000)CrossRefGoogle Scholar
  12. 12.
    Tortorella, F.: Reducing the classification cost of support vector classifiers through an roc-based reject rule. Pattern Anal. Appl. 7(2), 128–143 (2004)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Paolo Simeone
    • 1
  • Claudio Marrocco
    • 1
  • Francesco Tortorella
    • 1
  1. 1.DAEIMI, Università degli Studi di CassinoCassinoItaly

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