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Communication Dans Un Congrès Année : 2022

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

Résumé

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.
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Dates et versions

hal-03830648 , version 1 (26-10-2022)

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Thierry Denœux. An Evidential Neural Network Model for Regression Based on Random Fuzzy Numbers. 7th International Conference on Belief Functions (BELIEF 2022), Oct 2022, Paris, France. pp.57-66, ⟨10.1007/978-3-031-17801-6_6⟩. ⟨hal-03830648⟩
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