通过融合全局与局部信号，实现多标签分类的自适应阈值调整。
Adaptive Thresholding for Multi-Label Classification via Global-Local Signal Fusion
- 结合全局逆文档频率与局部K近邻信号生成动态阈值
- 在AmazonCat-13K上达宏F1 0.1712，优于树模型和预训练模型
- 轻量可解释，适合处理类别不平衡与噪声数据场景
多标签分类（MLC）需对每个样本预测多个标签，常面临严重类别不平衡与噪声问题。传统方法使用固定阈值或独立处理标签，忽略上下文与全局稀有性。本文提出一种自适应阈值机制，融合基于逆文档频率（IDF）的全局信号与基于K近邻（KNN）的局部信号，生成针对每个标签和每个实例的阈值。不将阈值作为硬性裁剪，而是将其作为损失函数中的可微惩罚项，提供平滑监督并改善校准效果。该架构轻量、可解释且高度模块化。在AmazonCat-13K基准测试中，宏F1达0.1712，显著优于树基方法与预训练Transformer方法。代码已公开，便于复现与扩展。
原文摘要 · Abstract (English)
Multi-label classification (MLC) requires predicting multiple labels per sample, often under heavy class imbalance and noisy conditions. Traditional approaches apply fixed thresholds or treat labels independently, overlooking context and global rarity. We introduce an adaptive thresholding mechanism that fuses global (IDF-based) and local (KNN-based) signals to produce per-label, per-instance thresholds. Instead of applying these as hard cutoffs, we treat them as differentiable penalties in the loss, providing smooth supervision and better calibration. Our architecture is lightweight, interpretable, and highly modular. On the AmazonCat-13K benchmark, it achieves a macro-F1 of 0.1712, substantially outperforming tree-based and pretrained transformer-based methods. We release full code for reproducibility and future extensions.
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