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Despite deep learning (DL) success in classification problems, DL classifiersdo not provide a sound mechanism to decide when to refrain from predicting.Recent works tried to control the overall prediction risk with classificationwith rejection options. However, existing works overlook the differentsignificance of different classes. We introduce Set-classifier withClass-specific RIsk Bounds (SCRIB) to tackle this problem, assigning multiplelabels to each example. Given the output of a black-box model on the validationset, SCRIB constructs a set-classifier that controls the class-specificprediction risks with a theoretical guarantee. The key idea is to reject whenthe set classifier returns more than one label. We validated SCRIB on severalmedical applications, including sleep staging on electroencephalogram (EEG)data, X-ray COVID image classification, and atrial fibrillation detection basedon electrocardiogram (ECG) data. SCRIB obtained desirable class-specific risks,which are 35\%-88\% closer to the target risks than baseline methods.

SCRIB: Set-classifier with Class-specific Risk Bounds for Blackbox Models