Enhancing Conformal Prediction via Class Similarity

TL;DR

This paper introduces a method to improve conformal prediction by incorporating class similarity, leading to smaller and more semantically coherent prediction sets across various datasets and models.

Contribution

It proposes augmenting CP score functions with class similarity penalties, providing theoretical analysis and a model-specific variant that enhances prediction efficiency without requiring semantic class partitions.

Findings

Consistently improves CP methods across datasets

Reduces average prediction set size

Enhances semantic coherence of prediction sets

Abstract

Conformal Prediction (CP) has emerged as a powerful statistical framework for high-stakes classification applications. Instead of predicting a single class, CP generates a prediction set, guaranteed to include the true label with a pre-specified probability. The performance of different CP methods is typically assessed by their average prediction set size. In setups where the classes can be partitioned into semantic groups, e.g., diseases that require similar treatment, users can benefit from prediction sets that are not only small on average, but also contain a small number of semantically different groups. This paper begins by addressing this problem and ultimately offers a widely applicable tool for boosting any CP method on any dataset. First, given a class partition, we propose augmenting the CP score function with a term that penalizes predictions with out-of-group errors. We theoretically analyze this strategy and prove its advantages for group-related metrics. Surprisingly, we show mathematically that, for common class partitions, it can also reduce the average set size of any CP score function. Our analysis reveals the class similarity factors behind this improvement and motivates us to propose a model-specific variant, which does not require any human semantic partition and can further reduce the prediction set size. Finally, we present an extensive empirical study, encompassing prominent CP methods, multiple models, and several datasets, which demonstrates that our class-similarity-based approach consistently enhances CP methods.