Conformal Prediction in Hierarchical Classification with Constrained Representation Complexity

Thomas Mortier, Alireza Javanmardi, Yusuf Sale, Eyke Hüllermeier, Willem Waegeman
Proceedings of The 29th International Conference on Artificial Intelligence and Statistics, PMLR 300:1324-1332, 2026.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v300-mortier26a, title = { Conformal Prediction in Hierarchical Classification with Constrained Representation Complexity }, author = {Mortier, Thomas and Javanmardi, Alireza and Sale, Yusuf and H{\"u}llermeier, Eyke and Waegeman, Willem}, booktitle = {Proceedings of The 29th International Conference on Artificial Intelligence and Statistics}, pages = {1324--1332}, year = {2026}, editor = {Khan, Emtiyaz and Li, Yingzhen and Solin, Arno and Ramdas, Aaditya}, volume = {300}, series = {Proceedings of Machine Learning Research}, month = {02--05 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v300/main/assets/mortier26a/mortier26a.pdf}, url = {https://proceedings.mlr.press/v300/mortier26a.html}, abstract = { 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. } }
Endnote
%0 Conference Paper %T Conformal Prediction in Hierarchical Classification with Constrained Representation Complexity %A Thomas Mortier %A Alireza Javanmardi %A Yusuf Sale %A Eyke Hüllermeier %A Willem Waegeman %B Proceedings of The 29th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2026 %E Emtiyaz Khan %E Yingzhen Li %E Arno Solin %E Aaditya Ramdas %F pmlr-v300-mortier26a %I PMLR %P 1324--1332 %U https://proceedings.mlr.press/v300/mortier26a.html %V 300 %X 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.
APA
Mortier, T., Javanmardi, A., Sale, Y., Hüllermeier, E. & Waegeman, W.. (2026). Conformal Prediction in Hierarchical Classification with Constrained Representation Complexity . Proceedings of The 29th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 300:1324-1332 Available from https://proceedings.mlr.press/v300/mortier26a.html.

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