为层次分类设计最优决策规则，让模型输出更贴合实际评估指标。
To Each Metric Its Decoding: Post-Hoc Optimal Decision Rules of Probabilistic Hierarchical Classifiers
- 基于目标评估指标推导出最优解码策略，取代传统启发式规则。
- 在数据不足时表现更优，显著提升层次分类的准确性和可靠性。
- 特别适合对错误代价敏感的应用，如医疗诊断、法律分类等。
层次分类通过结构化的标签层级引入错误严重性概念。然而，现有解码方法多依赖启发式规则，未必与特定评估指标一致。本文提出一种针对目标指标的最优解码框架，推导出复杂预测场景下的最优决策规则。当候选预测限于节点集合时，提供通用算法；在预测节点子集的最一般情形下，重点优化针对层次 $hF_β$ 分数的规则。通过大量实验验证，所提策略在数据稀疏场景下表现更优，显著提升层次分类器在真实应用中的性能与可靠性。代码已开源。
原文摘要 · Abstract (English)
Hierarchical classification offers an approach to incorporate the concept of mistake severity by leveraging a structured, labeled hierarchy. However, decoding in such settings frequently relies on heuristic decision rules, which may not align with task-specific evaluation metrics. In this work, we propose a framework for the optimal decoding of an output probability distribution with respect to a target metric. We derive optimal decision rules for increasingly complex prediction settings, providing universal algorithms when candidates are limited to the set of nodes. In the most general case of predicting a subset of nodes, we focus on rules dedicated to the hierarchical $hF_β$ scores, tailored to hierarchical settings. To demonstrate the practical utility of our approach, we conduct extensive empirical evaluations, showcasing the superiority of our proposed optimal strategies, particularly in underdetermined scenarios. These results highlight the potential of our methods to enhance the performance and reliability of hierarchical classifiers in real-world applications. The code is available at https://github.com/RomanPlaud/hierarchical_decision_rules
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