  In many real-world settings, the critical class is rare and a missed
detection carries a disproportionately high cost. For example, tumors are rare
and a false negative diagnosis could have severe consequences on treatment
outcomes; fraudulent banking transactions are rare and an undetected occurrence
could result in significant losses or legal penalties. In such contexts,
systems are often operated at a high true positive rate, which may require
tolerating high false positives. In this paper, we present a novel approach to
address the challenge of minimizing false positives for systems that need to
operate at a high true positive rate. We propose a ranking-based regularization
(RankReg) approach that is easy to implement, and show empirically that it not
only effectively reduces false positives, but also complements conventional
imbalanced learning losses. With this novel technique in hand, we conduct a
series of experiments on three broadly explored datasets (CIFAR-10&100 and
Melanoma) and show that our approach lifts the previous state-of-the-art
performance by notable margins.
