Weighted Aggregation of Conformity Scores for Classification

• URL: http://arxiv.org/abs/2407.10230v1
• Date: Sun, 14 Jul 2024 14:58:03 GMT
• Title: Weighted Aggregation of Conformity Scores for Classification
• Authors: Rui Luo, Zhixin Zhou,
• Abstract summary: Conformal prediction is a powerful framework for constructing prediction sets with valid coverage guarantees. We propose a novel approach that combines multiple score functions to improve the performance of conformal predictors.
• Score: 9.559062601251464
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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