All Mistakes Are Not Equal: Comprehensive Hierarchy Aware Multi-label Predictions (CHAMP)
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 the intuition that all mistakes are not equal, we present Comprehensive Hierarchy A...
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Main Authors | , , , |
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Format | Journal Article |
Language | English |
Published |
17.06.2022
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Subjects | |
Online Access | Get full text |
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Summary: | 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 the
intuition that all mistakes are not equal, we present Comprehensive Hierarchy
Aware Multi-label Predictions (CHAMP), a framework that penalizes a
misprediction depending on its severity as per the hierarchy tree. While there
have been works that apply such an idea to single-label classification, to the
best of our knowledge, there are limited such works for multilabel
classification focusing on the severity of mistakes. The key reason is that
there is no clear way of quantifying the severity of a misprediction a priori
in the multilabel setting. In this work, we propose a simple but effective
metric to quantify the severity of a mistake in HMC, naturally leading to
CHAMP. Extensive experiments on six public HMC datasets across modalities
(image, audio, and text) demonstrate that incorporating hierarchical
information leads to substantial gains as CHAMP improves both AUPRC (2.6%
median percentage improvement) and hierarchical metrics (2.85% median
percentage improvement), over stand-alone hierarchical or multilabel
classification methods. Compared to standard multilabel baselines, CHAMP
provides improved AUPRC in both robustness (8.87% mean percentage improvement )
and less data regimes. Further, our method provides a framework to enhance
existing multilabel classification algorithms with better mistakes (18.1% mean
percentage increment). |
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DOI: | 10.48550/arxiv.2206.08653 |