Robust Classification for Imprecise Environments
Machine Learning
Face detection by aggregated Bayesian network classifiers
Pattern Recognition Letters - In memory of Professor E.S. Gelsema
Reducing the classification cost of support vector classifiers through an ROC-based reject rule
Pattern Analysis & Applications
On optimum recognition error and reject tradeoff
IEEE Transactions on Information Theory
Meta methods for model sharing in personal information systems
ACM Transactions on Information Systems (TOIS)
Classification with a Reject Option using a Hinge Loss
The Journal of Machine Learning Research
Tunnel Hunter: Detecting application-layer tunnels with statistical fingerprinting
Computer Networks: The International Journal of Computer and Telecommunications Networking
Pattern Recognition Approaches for Classifying IP Flows
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
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
A sorting optimization curve with quality and yield requirements
Pattern Recognition Letters
On the Foundations of Noise-free Selective Classification
The Journal of Machine Learning Research
Computational Statistics & Data Analysis
Local bayesian based rejection method for HSC ensemble
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
The data replication method for the classification with reject option
AI Communications
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Consider the class of problems in which a target class is well-defined, and an outlier class is ill-defined. In these cases new outlier classes can appear, or the class-conditional distribution of the outlier class itself may be poorly sampled. A strategy to deal with this problem involves a two-stage classifier, in which one stage is designed to perform discrimination between known classes, and the other stage encloses known data to protect against changing conditions. The two stages are, however, interrelated, implying that optimising one may compromise the other. In this paper the relation between the two stages is studied within an ROC analysis framework. We show how the operating characteristics can be used for both model selection, and in aiding in the choice of the reject threshold. An analytic study on a controlled experiment is performed, followed by some experiments on real-world datasets with the distance-based reject-option classifier.