AUC Optimization with a Reject Option


  • Song-Qing Shen Nanjing University
  • Bin-Bin Yang Nanjing University
  • Wei Gao Nanjing University



Making an erroneous decision may cause serious results in diverse mission-critical tasks such as medical diagnosis and bioinformatics. Previous work focuses on classification with a reject option, i.e., abstain rather than classify an instance of low confidence. Most mission-critical tasks are always accompanied with class imbalance and cost sensitivity, where AUC has been shown a preferable measure than accuracy in classification. In this work, we propose the framework of AUC optimization with a reject option, and the basic idea is to withhold the decision of ranking a pair of positive and negative instances with a lower cost, rather than mis-ranking. We obtain the Bayes optimal solution for ranking, and learn the reject function and score function for ranking, simultaneously. An online algorithm has been developed for AUC optimization with a reject option, by considering the convex relaxation and plug-in rule. We verify, both theoretically and empirically, the effectiveness of the proposed algorithm.




How to Cite

Shen, S.-Q., Yang, B.-B., & Gao, W. (2020). AUC Optimization with a Reject Option. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 5684-5691.



AAAI Technical Track: Machine Learning