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.

翻译：保形预测已成为分类与回归任务中构建有效预测集的广泛使用框架。本文通过将分割保形预测框架扩展至分层分类场景（其中预测集通常受限于预定义层次结构的内部节点），提出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，第二种算法则放宽此限制。基于表示复杂度的概念，后者以更通用的组合推理问题为代价，获得了更小的集合规模。在多个基准数据集上的实验评估证明了所提算法在实现名义覆盖方面的有效性。