Entropy Reweighted Conformal Classification

Understand

Conformal Prediction (CP) is a powerful framework for constructing prediction sets with guaranteed coverage.

• However, recent studies have shown that integrating confidence calibration with CP can lead to a degradation in efficiency.
• In this paper, We propose an adaptive approach that considers the classifier's uncertainty and employs entropy-based reweighting to enhance the efficiency of prediction sets for conformal classification.
• Our experimental results demonstrate that this method significantly improves efficiency.