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.
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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)
Asuncion, A., Newman, D.J.: UCI machine learning repository (2007)
Bartlett, P.L., Wegkamp, M.H.: Classification with a reject option using a hinge loss. J. Mach. Learn. Res. 9, 1823–1840 (2008)
Chow, C.: On optimum recognition error and reject tradeoff. IEEE Transactions on Information Theory 16(1), 41–46 (1970)
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)
Dietterich, T.G., Bakiri, G.: Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research 2, 263–286 (1995)
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)
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)
Pietraszek, T.: On the use of ROC analysis for the optimization of abstaining classifiers. Machine Learning 68(2), 137–169 (2007)
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)
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)
Tortorella, F.: Reducing the classification cost of support vector classifiers through an roc-based reject rule. Pattern Anal. Appl. 7(2), 128–143 (2004)
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Simeone, P., Marrocco, C., Tortorella, F. (2011). Shaping the Error-Reject Curve of Error Correcting Output Coding Systems. In: Maino, G., Foresti, G.L. (eds) Image Analysis and Processing – ICIAP 2011. ICIAP 2011. Lecture Notes in Computer Science, vol 6978. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24085-0_13
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DOI: https://doi.org/10.1007/978-3-642-24085-0_13
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