An Optimal Reject Rule for Binary Classifiers
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Multiple Reject Thresholds for Improving Classification Reliability
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Novelty detection: a review—part 2: neural network based approaches
Signal Processing
A ROC-based reject rule for dichotomizers
Pattern Recognition Letters
Rejection schemes for graded multiclass problems
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Journal of Visual Languages and Computing
A ROC-based reject rule for support vector machines
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
A rejection option for the multilayer perceptron using hyperplanes
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part I
Shaping the error-reject curve of error correcting output coding systems
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
Design of reject rules for ECOC classification systems
Pattern Recognition
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Criteria for evaluating the classification reliability of a neural classifier and for accordingly making a reject option are proposed. Such an option, implemented by means of two rules which can be applied independently of topology, size, and training algorithms of the neural classifier, allows one to improve the classification reliability. It is assumed that a performance function P is defined which, taking into account the requirements of the particular application, evaluates the quality of the classification in terms of recognition, misclassification, and reject rates. Under this assumption the optimal reject threshold value, determining the best trade-off between reject rate and misclassification rate, is the one for which the function P reaches its absolute maximum. No constraints are imposed on the form of P, but the ones necessary in order that P actually measures the quality of the classification process. The reject threshold is evaluated on the basis of some statistical distributions characterizing the behavior of the classifier when operating without reject option; these distributions are computed once the training phase of the net has been completed. The method has been tested with a neural classifier devised for handprinted and multifont printed characters, by using a database of about 300000 samples. Experimental results are discussed