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Classification with Rejection Based on Cost-sensitive Classification
Nontawat Charoenphakdee · Zhenghang Cui · Yivan Zhang · Masashi Sugiyama

Wed Jul 21 07:35 PM -- 07:40 PM (PDT) @

The goal of classification with rejection is to avoid risky misclassification in error-critical applications such as medical diagnosis and product inspection. In this paper, based on the relationship between classification with rejection and cost-sensitive classification, we propose a novel method of classification with rejection by learning an ensemble of cost-sensitive classifiers, which satisfies all the following properties: (i) it can avoid estimating class-posterior probabilities, resulting in improved classification accuracy. (ii) it allows a flexible choice of losses including non-convex ones, (iii) it does not require complicated modifications when using different losses, (iv) it is applicable to both binary and multiclass cases, and (v) it is theoretically justifiable for any classification-calibrated loss. Experimental results demonstrate the usefulness of our proposed approach in clean-labeled, noisy-labeled, and positive-unlabeled classification.

Author Information

Nontawat Charoenphakdee (The University of Tokyo / RIKEN)
Zhenghang Cui (The University of Tokyo / RIKEN)
Yivan Zhang (The University of Tokyo / RIKEN)
Masashi Sugiyama (RIKEN / The University of Tokyo)

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