Adapting cost-sensitive learning for reject option

Authors:
Jun Du;Eileen A. Ni;Charles X. Ling
Affiliations:
The University of Western Ontario, London, Canada;The University of Western Ontario, London, Canada;The University of Western Ontario, London, Canada
Venue:
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Year:
2010

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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 reject 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 reject cost. We therefore analyse three categories of popular cost-sensitive learning approaches, and provide generic methods to adapt them for reject option.