CDAN: Cost Dependent Deep Abstention Network

This paper proposes deep architectures for learning instance-specific abstain (reject) option multiclass classifiers. The proposed approach uses novel bounded multiclass abstention loss for multiclass classification as a performance measure. This approach uses rejection cost as the rejection parameter in contrast to coverage-based approaches. To show the effectiveness of the proposed approach, we experiment with several real-world datasets and compare them with state-of-the-art coverage-based and cost-of-rejection-based techniques. The experimental results show that the proposed method improves performance over the state-of-the-art approaches.