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This paper considers the problem of Hierarchical Multi-Label Classification(HMC), where (i) several labels can be present for each example, and (ii)labels are related via a domain-specific hierarchy tree. Guided by theintuition that all mistakes are not equal, we present Comprehensive HierarchyAware Multi-label Predictions (CHAMP), a framework that penalizes amisprediction depending on its severity as per the hierarchy tree. While therehave been works that apply such an idea to single-label classification, to thebest of our knowledge, there are limited such works for multilabelclassification focusing on the severity of mistakes. The key reason is thatthere is no clear way of quantifying the severity of a misprediction a prioriin the multilabel setting. In this work, we propose a simple but effectivemetric to quantify the severity of a mistake in HMC, naturally leading toCHAMP. Extensive experiments on six public HMC datasets across modalities(image, audio, and text) demonstrate that incorporating hierarchicalinformation leads to substantial gains as CHAMP improves both AUPRC (2.6%median percentage improvement) and hierarchical metrics (2.85% medianpercentage improvement), over stand-alone hierarchical or multilabelclassification methods. Compared to standard multilabel baselines, CHAMPprovides improved AUPRC in both robustness (8.87% mean percentage improvement )and less data regimes. Further, our method provides a framework to enhanceexisting multilabel classification algorithms with better mistakes (18.1% meanpercentage increment).

All Mistakes Are Not Equal: Comprehensive Hierarchy Aware Multi-label Predictions (CHAMP)