A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support Vector Machines with Embedded Reject Option
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
Data & Knowledge Engineering
A multi-objective optimisation approach for class imbalance learning
Pattern Recognition
Polichotomies on imbalanced domains by one-per-class compensated reconstruction rule
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
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In this paper we propose to face the rejection problem as a new classification problem. In order to do that, we introduce a trainable classifier, that we call reject classifier, to distinguish it from the classifier to which the reject option is applied (termed primary classifier). This idea yields a reject option that is largely independent of the approach used for the primary classifier, working also for systems providing as their only output the guess class. The whole classification system can be seen as a serial multiple classifier system: given an input patter x, the primary classifier limits to two the number of possible classes (i.e., its guess class and the reject class), while the reject classifier attributes x to one out of these two classes. The proposed reject method has been tested on three different publicly available databases. We also compared it with other reject rules and the results demonstrated the effectiveness of the proposed approach.