Making large-scale support vector machine learning practical
Advances in kernel methods
Multiclass learning, boosting, and error-correcting codes
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
Reducing the classification cost of support vector classifiers through an ROC-based reject rule
Pattern Analysis & Applications
Classification with a Reject Option using a Hinge Loss
The Journal of Machine Learning Research
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Exploiting System Knowledge to Improve ECOC Reject Rules
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
To reject or not to reject: that is the question-an answer in caseof neural classifiers
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A method for improving classification reliability of multilayer perceptrons
IEEE Transactions on Neural Networks
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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.