Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
General solution and learning method for binary classification with performance constraints
Pattern Recognition Letters
Filtering segmentation cuts for digit string recognition
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
Classification with a Reject Option using a Hinge Loss
The Journal of Machine Learning Research
The ROC isometrics approach to construct reliable classifiers
Intelligent Data Analysis
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
An introduction to artificial prediction markets for classification
The Journal of Machine Learning Research
The data replication method for the classification with reject option
AI Communications
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This paper presents a novel reject rule for support vector classifiers, based on the receiver operating characteristic (ROC) curve. The rule minimises the expected classification cost, defined on the basis of classification and the error costs for the particular application at hand. The rationale of the proposed approach is that the ROC curve of the SVM contains all of the necessary information to find the optimal threshold values that minimise the expected classification cost. To evaluate the effectiveness of the proposed reject rule, a large number of tests has been performed on several data sets, and with different kernels. A comparison technique, based on the Wilcoxon rank sum test, has been defined and employed to provide the results at an adequate significance level. The experiments have definitely confirmed the effectiveness of the proposed reject rule.