# Hierarchical Conformal Classification

arXiv: https://arxiv.org/abs/2508.13288 · HTML: https://mlsanity.com/paper/2508.13288/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 22 references that are in the map.
- First posted on arXiv 18 Aug 2025.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Uncertainty Sets for Image Classifiers using Conformal Prediction](https://mlsanity.com/paper/2009.14193/) (2020)
- [Least Ambiguous Set-Valued Classifiers with Bounded Error Levels](https://mlsanity.com/paper/1609.00451/) (2016)
- [Class-Conditional Conformal Prediction with Many Classes](https://mlsanity.com/paper/2306.09335/) (2023)
- [A Cross-Conformal Predictor for Multi-label Classification](https://mlsanity.com/paper/2211.16238/) (2022)
- [Multi-label Classification under Uncertainty: A Tree-based Conformal Prediction Approach](https://mlsanity.com/paper/2404.19472/) (2024)
- [Well-calibrated Confidence Measures for Multi-label Text Classification with a Large Number of Labels](https://mlsanity.com/paper/2312.09304/) (2023)
- [Conformal Intent Classification and Clarification for Fast and Accurate Intent Recognition](https://mlsanity.com/paper/2403.18973/) (2024)
- [Set-valued prediction in hierarchical classification with constrained representation complexity](https://mlsanity.com/paper/2203.06676/) (2022)

## Built on by (most cited first, 2 of 2)

- [Conformal Agent Error Attribution](https://mlsanity.com/paper/2605.06788/) (2026)
- [Enhancing Conformal Prediction via Class Similarity](https://mlsanity.com/paper/2511.19359/) (2025)

## Abstract (arXiv)

Conformal prediction (CP) is a powerful framework for quantifying uncertainty in machine learning models, offering reliable predictions with finite-sample coverage guarantees. When applied to classification, CP produces a prediction set of possible labels that is guaranteed to contain the true label with high probability, regardless of the underlying classifier. However, standard CP treats classes as flat and unstructured, ignoring domain knowledge such as semantic relationships or hierarchical structure among class labels. This paper presents hierarchical conformal classification (HCC), an extension of CP that incorporates class hierarchies into both the structure and semantics of prediction sets. We formulate HCC as a constrained optimization problem whose solutions yield prediction sets composed of nodes at different levels of the hierarchy, while maintaining coverage guarantees. To address the combinatorial nature of the problem, we formally show that a much smaller, well-structured subset of candidate solutions suffices to ensure coverage while upholding optimality. An empirical evaluation on three new benchmarks consisting of audio, image, and text data highlights the advantages of our approach, and a user study shows that annotators significantly prefer hierarchical over flat prediction sets.