Conformal Prediction in Hierarchical Classification with Constrained Representation Complexity

TL;DR

This paper extends conformal prediction to hierarchical classification, proposing two algorithms that balance set size and computational complexity, validated on benchmark datasets.

Contribution

It introduces two efficient algorithms for hierarchical conformal prediction, one restricting to internal nodes and the other allowing more general sets.

Findings

Algorithms achieve nominal coverage on benchmark datasets.

Representation complexity controls the size of prediction sets.

Relaxed restriction algorithm produces smaller sets with higher complexity.

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.