Conformal Prediction in Hierarchical Classification

• URL: http://arxiv.org/abs/2501.19038v1
• Date: Fri, 31 Jan 2025 11:10:19 GMT
• Title: Conformal Prediction in Hierarchical Classification
• Authors: Thomas Mortier, Alireza Javanmardi, Yusuf Sale, Eyke Hüllermeier, Willem Waegeman,
• Abstract summary: We extend the split conformal prediction framework to hierarchical classification, where prediction sets are commonly restricted to internal nodes of a predefined hierarchy. The first algorithm returns internal nodes as prediction sets, while the second relaxes this restriction, using the notion of complexity. Empirical evaluations on several benchmark datasets demonstrate the effectiveness of the proposed algorithms.
• Score: 18.730305100193927
• License:
• 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 relaxes this restriction, using the notion of representation complexity, yielding a more general and combinatorial inference problem, but smaller set sizes. Empirical evaluations on several benchmark datasets demonstrate the effectiveness of the proposed algorithms in achieving nominal coverage.

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