A method for improving classification reliability of multilayer perceptrons
IEEE Transactions on Neural Networks
On optimal reject rules and ROC curves
Pattern Recognition Letters
A ROC-based reject rule for dichotomizers
Pattern Recognition Letters
On the Foundations of Noise-free Selective Classification
The Journal of Machine Learning Research
Computational Statistics & Data Analysis
Drift mining in data: A framework for addressing drift in classification
Computational Statistics & Data Analysis
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Binary classifiers are used in many complex classification problems in which the classification result could have serious consequences. Thus, they should ensure a very high reliability to avoid erroneous decisions. Unfortunately, this is rarely the case in real situations where the cost for a wrong classification could be so high that it should be convenient to reject the sample which gives raise to an unreliable result. However, as far as we know, a reject option specifically devised for binary classifiers has not been yet proposed. This paper presents an optimal reject rule for binary classifiers, based on the Receiver Operating Characteristic curve. The rule is optimal since it maximizes a classification utility function, defined on the basis of classification and error costs peculiar for the application at hand. Experiments performed with a data set publicly available confirmed the effectiveness of the proposed reject rule.