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Entropy Reweighted Conformal Classification
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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.
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Classification uncertainty for transient gravitational-wave noise artefacts with optimised conformal prediction
For Gravity Spy glitch classification, the optimal conformal prediction nonconformity measure depends on the chosen metric: baseline wins for F1 and average set size, maxscore2 wins for singleton count.
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