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In many real-world settings, the critical class is rare and a misseddetection carries a disproportionately high cost. For example, tumors are rareand a false negative diagnosis could have severe consequences on treatmentoutcomes; fraudulent banking transactions are rare and an undetected occurrencecould result in significant losses or legal penalties. In such contexts,systems are often operated at a high true positive rate, which may requiretolerating high false positives. In this paper, we present a novel approach toaddress the challenge of minimizing false positives for systems that need tooperate at a high true positive rate. We propose a ranking-based regularization(RankReg) approach that is easy to implement, and show empirically that it notonly effectively reduces false positives, but also complements conventionalimbalanced learning losses. With this novel technique in hand, we conduct aseries of experiments on three broadly explored datasets (CIFAR-10&100 andMelanoma) and show that our approach lifts the previous state-of-the-artperformance by notable margins.

Ranking Regularization for Critical Rare Classes: Minimizing False Positives at a High True Positive Rate