Adapting cost-sensitive learning for reject option

Abstract

Traditional cost-sensitive learning algorithms always deterministically predict examples as either positive or negative (in binary setting), to minimize the total misclassification cost. However, in more advanced real-world settings, the algorithms can also have another option to <i>reject</i> examples of high uncertainty. In this paper, we assume that cost-sensitive learning algorithms can reject the examples and obtain their true labels by paying <i>reject cost</i>. We therefore analyse three categories of popular cost-sensitive learning approaches, and provide generic methods to adapt them for reject option.

DOI: 10.1145/1871437.1871749

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Cite this paper

@inproceedings{Du2010AdaptingCL, title={Adapting cost-sensitive learning for reject option}, author={Jun Du and Eileen A. Ni and Charles X. Ling}, booktitle={CIKM}, year={2010} }