# Multi-label Classification under Uncertainty: A Tree-based Conformal Prediction Approach

arXiv: https://arxiv.org/abs/2404.19472 · HTML: https://mlsanity.com/paper/2404.19472/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2024.

## Built on by (most cited first, 7 of 7)

- [Trustworthy Classification through Rank-Based Conformal Prediction Sets](https://mlsanity.com/paper/2407.04407/) (2024)
- [Conformal Language Model Reasoning with Coherent Factuality](https://mlsanity.com/paper/2505.17126/) (2025)
- [Conformal Calibration: Ensuring the Reliability of Black-Box AI in Wireless Systems](https://mlsanity.com/paper/2504.09310/) (2025)
- [Hierarchical Selective Classification](https://mlsanity.com/paper/2405.11533/) (2024)
- [Hierarchical Conformal Classification](https://mlsanity.com/paper/2508.13288/) (2025)
- [Conformal Prediction for Multi-Source Detection on a Network](https://mlsanity.com/paper/2511.08867/) (2025)
- [Questioning the Coverage-Length Metric in Conformal Prediction: When Shorter Intervals Are Not Better](https://mlsanity.com/paper/2601.21455/) (2026)

## Abstract (arXiv)

Multi-label classification is a common challenge in various machine learning applications, where a single data instance can be associated with multiple classes simultaneously. The current paper proposes a novel tree-based method for multi-label classification using conformal prediction and multiple hypothesis testing. The proposed method employs hierarchical clustering with labelsets to develop a hierarchical tree, which is then formulated as a multiple-testing problem with a hierarchical structure. The split-conformal prediction method is used to obtain marginal conformal p-values for each tested hypothesis, and two textithierarchical testing procedures are developed based on marginal conformal p-values, including a hierarchical Bonferroni procedure and its modification for controlling the family-wise error rate. The prediction sets are thus formed based on the testing outcomes of these two procedures. We establish a theoretical guarantee of valid coverage for the prediction sets through proven family-wise error rate control of those two procedures. We demonstrate the effectiveness of our method in a simulation study and two real data analysis compared to other conformal methods for multi-label classification.