Epistemic Wrapping for Uncertainty Quantification

• URL: http://arxiv.org/abs/2505.02277v1
• Date: Sun, 04 May 2025 22:15:56 GMT
• Title: Epistemic Wrapping for Uncertainty Quantification
• Authors: Maryam Sultana, Neil Yorke-Smith, Kaizheng Wang, Shireen Kudukkil Manchingal, Muhammad Mubashar, Fabio Cuzzolin,
• Abstract summary: Uncertainty estimation is pivotal in machine learning, especially for classification tasks.<n>We introduce a novel Epistemic Wrapping' methodology aimed at improving uncertainty estimation in classification.
• Score: 6.967551514640606
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Uncertainty estimation is pivotal in machine learning, especially for classification tasks, as it improves the robustness and reliability of models. We introduce a novel `Epistemic Wrapping' methodology aimed at improving uncertainty estimation in classification. Our approach uses Bayesian Neural Networks (BNNs) as a baseline and transforms their outputs into belief function posteriors, effectively capturing epistemic uncertainty and offering an efficient and general methodology for uncertainty quantification. Comprehensive experiments employing a Bayesian Neural Network (BNN) baseline and an Interval Neural Network for inference on the MNIST, Fashion-MNIST, CIFAR-10 and CIFAR-100 datasets demonstrate that our Epistemic Wrapper significantly enhances generalisation and uncertainty quantification.

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