Evidential Uncertainty Sets in Deep Classifiers Using Conformal Prediction

• URL: http://arxiv.org/abs/2406.10787v3
• Date: Tue, 30 Jul 2024 04:00:44 GMT
• Title: Evidential Uncertainty Sets in Deep Classifiers Using Conformal Prediction
• Authors: Hamed Karimi, Reza Samavi,
• Abstract summary: We propose Evidential Conformal Prediction (ECP) method for image classifiers to generate conformal prediction sets. Our method is based on a non-conformity score function that has its roots in Evidential Deep Learning (EDL)
• Score: 1.2430809884830318
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we propose Evidential Conformal Prediction (ECP) method for image classifiers to generate the conformal prediction sets. Our method is designed based on a non-conformity score function that has its roots in Evidential Deep Learning (EDL) as a method of quantifying model (epistemic) uncertainty in DNN classifiers. We use evidence that are derived from the logit values of target labels to compute the components of our non-conformity score function: the heuristic notion of uncertainty in CP, uncertainty surprisal, and expected utility. Our extensive experimental evaluation demonstrates that ECP outperforms three state-of-the-art methods for generating CP sets, in terms of their set sizes and adaptivity while maintaining the coverage of true labels.

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