[2501.19038] Conformal Prediction in Hierarchical Classification with Constrained Representation Complexity

Statistics > Machine Learning arXiv:2501.19038 (stat) [Submitted on 31 Jan 2025 (v1), last revised 10 Apr 2026 (this version, v3)] Title:Conformal Prediction in Hierarchical Classification with Constrained Representation Complexity Authors:Thomas Mortier, Alireza Javanmardi, Yusuf Sale, Eyke Hüllermeier, Willem Waegeman View a PDF of the paper titled Conformal Prediction in Hierarchical Classification with Constrained Representation Complexity, by Thomas Mortier and 4 other authors View PDF HTML (experimental) 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. Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG) Cite as: arXiv:2501.19038 [stat.ML]   (or arXiv:2501.19038v3 [stat.ML] for this version)   https://doi.org/10.48550/ar...