On the Error-Reject Trade-Off in Biometric Verification Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Modern Coding Theory
A Fast Approach to Improve Classification Performance of ECOC Classification Systems
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
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Error Correcting Output Coding (ECOC) is an established technique to face a classification problem with many possible classes decomposing it into a set of two class subproblems. In this paper, we propose an ECOC system with a reject option that is performed by taking into account the confidence degree of the dichotomizers. Such a scheme makes use of a coding matrix based on Low Density Parity Check (LDPC) codes that can also be usefully employed to implement an iterative recovery strategy for the binary rejects. The experimental results have confirmed the effectiveness of the proposed approach.