aiqbench analysis
This study introduces a weighted loss objective that targets rare hierarchical nodes directly rather than just rare data points, significantly boosting recall and F1 scores in multi-label classification tasks. By integrating node-wise imbalance weighting with focal components based on ensemble uncertainty, the method effectively improves deep-level predictions even when facing suboptimal encoders or limited datasets. The approach offers a practical solution for models requiring fine-grained classification where child nodes are naturally less frequent than their parents.
Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning
arXiv:2602.08986v2 Announce Type: replace Abstract: In hierarchical multi-label classification, a persistent challenge is enabling model predictions to reach deeper levels of the hierarchy for more detailed or fine-grained classifications. This difficulty partly arises from the natural rarity of certain classes (or hierarchical nodes) and the hierarchical constraint that ensures child nodes are almost always less frequent than their parents. To address this, we propose a weighted loss objective for neural networks that combines node-wise imbalance weighting with focal weighting components, the latter leveraging modern quantification of ensemble uncertainties. By emphasizing rare nodes rather than rare observations (data points), and focusing on uncertain nodes for each model output distribution during training, we observe improvements in recall by up to a factor of five on benchmark datasets, along with statistically significant gains in $F_{1}$ score. We also show our approach aids convolutional networks on challenging tasks, as in situations with suboptimal encoders or limited data.
Abstract:In hierarchical multi-label classification, a persistent challenge is enabling model predictions to reach deeper levels of the hierarchy for more detailed or fine-grained classifications. This difficulty partly arises from the natural rarity of certain classes (or hierarchical nodes) and the hierarchical constraint that ensures child nodes are almost always less frequent than their parents. To address this, we propose a weighted loss objective for neural networks that combines node-wise imbalance weighting with focal weighting components, the latter leveraging modern quantification of ensemble uncertainties. By emphasizing rare nodes rather than rare observations (data points), and focusing on uncertain nodes for each model output distribution during training, we observe improvements in recall by up to a factor of five on benchmark datasets, along with statistically significant gains in $F_{1}$ score. We also show our approach aids convolutional networks on challenging tasks, as in situations with suboptimal encoders or limited data.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2602.08986 [cs.LG]
(or arXiv:2602.08986v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2602.08986 Focus to learn more arXiv-issued DOI via DataCite
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