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


  • Zhen Lin University of Illinois at Urbana–Champaign
  • Lucas Glass Analytics Center of Excellence - IQVIA
  • M. Brandon Westover Massachusetts General Hospital Harvard Medical School
  • Cao Xiao Amplitude
  • Jimeng Sun University of Illinois at Urbana–Champaign



Machine Learning (ML)


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.




How to Cite

Lin, Z., Glass, L., Westover, M. B., Xiao, C., & Sun, J. (2022). SCRIB: Set-Classifier with Class-Specific Risk Bounds for Blackbox Models. Proceedings of the AAAI Conference on Artificial Intelligence, 36(7), 7497-7505.



AAAI Technical Track on Machine Learning II