Entropy Reweighted Conformal Classification

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 c...

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Bibliographic Details
Published in:arXiv.org 2024-07
Main Authors: Luo, Rui, Colombo, Nicolo
Format: Article
Language:English
Subjects:
Classification
Efficiency
Entropy
Online Access:Get full text
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Summary: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.
ISSN:2331-8422