SCRIB: Set-Classifier with Class-Specific Risk Bounds for Blackbox Models.

ABSTRACT: 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 electroen

SUBMITTER: Lin Z 

PROVIDER: S-EPMC10155818 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature