Robust Classification for Imprecise Environments
Machine Learning
Bootstrap Methods for the Cost-Sensitive Evaluation of Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
An Optimal Reject Rule for Binary Classifiers
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
On optimum recognition error and reject tradeoff
IEEE Transactions on Information Theory
A method for improving classification reliability of multilayer perceptrons
IEEE Transactions on Neural Networks
Exploiting AUC for optimal linear combinations of dichotomizers
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Maximizing the area under the ROC curve by pairwise feature combination
Pattern Recognition
An Empirical Comparison of Ideal and Empirical ROC-Based Reject Rules
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
The ROC isometrics approach to construct reliable classifiers
Intelligent Data Analysis
ICCSA '09 Proceedings of the International Conference on Computational Science and Its Applications: Part I
Linear classifier with reject option for the detection of vocal fold paralysis and vocal fold edema
EURASIP Journal on Advances in Signal Processing - Special issue on analysis and signal processing of oesophageal and pathological voices
Estimating the ROC curve of linearly combined dichotomizers
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Multi-label classification with a reject option
Pattern Recognition
The data replication method for the classification with reject option
AI Communications
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Many complex classification tasks involve a discrimination between two classes. Since in such cases a classification error could frequently have serious consequences, the classifiers employed should ensure a very high reliability to avoid erroneous decisions. Unfortunately this is difficult to obtain in real situations where the classifier can meet samples very different from those examined in the training phase. Moreover, the cost for a wrong classification can be so high that it is convenient to reject the sample which gives raise to an unreliable result. However, despite its relevance, a reject option specifically devised for dichotomizers (i.e. two-class classifiers) has not been yet proposed. This paper presents a novel reject rule for dichotomizers, based on the Receiver Operating Characteristic curve. The rule minimizes the expected classification cost, defined on the basis of classification and error costs peculiar for the application at hand. Experiments performed with different classifier architectures on several data sets publicly available confirmed the effectiveness of the proposed reject rule.