An Evidential Neural Network Model for Regression Based on Random Fuzzy Numbers

• URL: http://arxiv.org/abs/2208.00647v1
• Date: Mon, 1 Aug 2022 07:13:31 GMT
• Title: An Evidential Neural Network Model for Regression Based on Random Fuzzy Numbers
• Authors: Thierry Denoeux
• Abstract summary: We introduce a distance-based neural network model for regression. The model interprets the intersection of the input vector to prototypes as pieces of evidence. Experiments with real datasets demonstrate the very good performance of the method.
• Score: 6.713564212269253
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a distance-based neural network model for regression, in which prediction uncertainty is quantified by a belief function on the real line. The model interprets the distances of the input vector to prototypes as pieces of evidence represented by Gaussian random fuzzy numbers (GRFN's) and combined by the generalized product intersection rule, an operator that extends Dempster's rule to random fuzzy sets. The network output is a GRFN that can be summarized by three numbers characterizing the most plausible predicted value, variability around this value, and epistemic uncertainty. Experiments with real datasets demonstrate the very good performance of the method as compared to state-of-the-art evidential and statistical learning algorithms. \keywords{Evidence theory, Dempster-Shafer theory, belief functions, machine learning, random fuzzy sets.

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