Why the study?
Deep learning classifiers lack sound mechanisms to refrain from predicting, and existing risk-control methods overlook the differing significance of different classes.
SCRIB provides a novel machine learning approach to control class-specific prediction risks by allowing the model to reject predictions when multiple labels are returned, improving reliability in medical applications like ECG-based AFib detection.
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SCRIB may enhance calibrated ML predictions in AF detection; leaves open clinical utility pending prospective validation.
Despite deep learning (DL) success in classification problems, DL classifiers do not provide a sound mechanism to decide when to refrain from predicting. Recent works tried to control the overall prediction risk with classification with rejection options. However, existing works overlook the different significance of different classes. We introduce Set-classifier with class-specific RIsk Bounds (SCRIB) to tackle this problem, assigning multiple labels to each example. Given the output of a black-box model on the validation set, SCRIB constructs a set-classifier that controls the class-specific prediction risks. The key idea is to reject when the set classifier returns more than one label. We validated SCRIB on several medical applications, including sleep staging on electroencephalogram(EEG) data, X-ray COVID image classification, and atrial fibrillation detection based on electrocardiogram (ECG) data.SCRIB obtained desirable class-specific risks, which are 35%-88% closer to the target risks than baseline methods.
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Lin et al. (2022) studied this question.
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