  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.
