Weighted Aggregation of Conformity Scores for Classification
This work addresses a specific bottleneck in conformal prediction for classification, offering a principled enhancement that is incremental but data-driven.
The paper tackles the problem of limited efficiency and informativeness in conformal prediction for multi-class classification by proposing a weighted aggregation of multiple score functions to minimize prediction set size, achieving consistent performance improvements over single-score methods while maintaining valid coverage.
Conformal prediction is a powerful framework for constructing prediction sets with valid coverage guarantees in multi-class classification. However, existing methods often rely on a single score function, which can limit their efficiency and informativeness. We propose a novel approach that combines multiple score functions to improve the performance of conformal predictors by identifying optimal weights that minimize prediction set size. Our theoretical analysis establishes a connection between the weighted score functions and subgraph classes of functions studied in Vapnik-Chervonenkis theory, providing a rigorous mathematical basis for understanding the effectiveness of the proposed method. Experiments demonstrate that our approach consistently outperforms single-score conformal predictors while maintaining valid coverage, offering a principled and data-driven way to enhance the efficiency and practicality of conformal prediction in classification tasks.