提出层次分类的置信预测方法，可生成更小且可靠的预测集合。
Conformal Prediction in Hierarchical Classification with Constrained Representation Complexity
- 基于表示复杂度约束，设计两种高效推理算法
- 在多个数据集上实现名义覆盖率，预测集更小
- 适合需要可靠置信集合的层次分类场景
置信预测已成为分类与回归任务中构建有效预测集的常用框架。本文将分裂置信预测框架扩展至层次分类，其中预测集通常被限制为预定义层次结构中的内部节点，并提出了两种计算高效的推理算法。第一种算法返回内部节点作为预测集，第二种算法放宽此限制。利用表示复杂度的概念，后者在牺牲一定计算复杂性的情况下获得更小的预测集。在多个基准数据集上的实证评估表明，所提算法能有效实现名义覆盖率。
原文摘要 · Abstract (English)
Conformal prediction has emerged as a widely used framework for constructing valid prediction sets in classification and regression tasks. In this work, we extend the split conformal prediction framework to hierarchical classification, where prediction sets are commonly restricted to internal nodes of a predefined hierarchy, and propose two computationally efficient inference algorithms. The first algorithm returns internal nodes as prediction sets, while the second one relaxes this restriction. Using the notion of representation complexity, the latter yields smaller set sizes at the cost of a more general and combinatorial inference problem. Empirical evaluations on several benchmark datasets demonstrate the effectiveness of the proposed algorithms in achieving nominal coverage.
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