Instructor: Thierry Denoeux

The theory of epistemic random fuzzy sets has been recently proposed as a very general formalism for uncertain reasoning, encompassing both the Dempster-Shafer theory of evidence and possibility theory as special cases. This tutorial is intended to provide an introduction to this new framework and to demonstrate its application to uncertainty quantification in machine learning.

Slides

Slides (draft, to be updated)

Related papers

1. Thierry Denoeux. Reasoning with fuzzy and uncertain evidence using epistemic random fuzzy sets: general framework and practical models. Fuzzy Sets and Systems, Vol. 453, Pages 1-36, 2023. pdf
2. T. Denoeux. Parametric families of continuous belief functions based on generalized Gaussian random fuzzy numbers. Preprint hal-04060251, 2023.
3. Thierry Denoeux. Belief Functions on the Real Line defined by Transformed Gaussian Random Fuzzy Numbers. 2023 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2023), Songdo Incheon, Korea, August 13-17, 2023. pdf
4. T. Denoeux. Quantifying Prediction Uncertainty in Regression using Random Fuzzy Sets: the ENNreg model. IEEE Transactions on Fuzzy Systems (to appear), 2023. pdf
5. T. Denoeux. Belief functions induced by random fuzzy sets: A general framework for representing uncertain and fuzzy evidence. Fuzzy Sets and Systems, Vol. 424, pages 63-91, 2021. pdf

Software

evreg 1.0.2 on CRAN