Conformal Prediction in Hierarchical Classification with Constrained Representation Complexity
This work addresses hierarchical classification for applications requiring structured predictions, but it is incremental as it builds on existing conformal prediction frameworks.
The paper tackles the problem of constructing valid prediction sets in hierarchical classification by extending split conformal prediction, proposing two efficient inference algorithms that achieve nominal coverage on benchmark datasets.
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.