针对层次多标签中稀有节点检测难问题，提出加权损失函数提升细粒度分类效果。
Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning
- 设计节点级不平衡与不确定性聚焦的加权损失，强化稀有节点学习。
- 在基准数据集上召回率最高提升5倍，F1分数显著提高。
- 适用于弱编码器或数据少场景，对卷积网络也有增益。
在层次多标签分类中，模型难以深入层级进行更细致的分类，主要源于某些类别（或层级节点）天然稀有，且子节点频率几乎总是低于父节点。为此，我们提出一种神经网络的加权损失目标，结合节点级不平衡权重与基于集成不确定性量化的新颖焦点权重。该方法强调稀有节点而非稀有样本，并在训练中聚焦每个输出分布中的不确定节点。实验显示，在基准数据集上召回率最高提升五倍，同时F1分数有统计显著提升。此外，该方法在卷积网络面对困难任务时仍表现良好，尤其在编码器性能不佳或数据有限的情况下。
原文摘要 · Abstract (English)
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.
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