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H$^2$EDL: Hyper Evidential Deep Learning for Hierarchical Classification
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hierarchical classificationevidential reasoninguncertainty quantification
2608.18185
Builder Relevance
1h ago70%
Abstract
H$^2$EDL introduces a model that effectively captures uncertainty in hierarchical classification by combining evidential and hierarchical approaches.
Reality Card
Core Claim
H$^2$EDL reduces calibration error by approximately half compared to cross-entropy baselines, especially at deeper hierarchy levels.
Method / Result
Calibration error reduction by approximately 50% on FGVC-Aircraft and DERM12345 datasets.
Limitations
The model's reliance on the taxonomy structure may limit its applicability to datasets without a clear hierarchical organization.
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