Instructor: Thierry Denoeux

Description: This is an introductory course on belief functions, with focus on data analysis, machine learning and statistical inference.

Slides

1. Basic notions
2. Decision-making
3. Statistical inference

Papers

1. T. Denoeux. A k-nearest neighbor classification rule based on Dempster-Shafer theory. IEEE Transactions on Systems, Man and Cybernetics, 25(05):804-813, 1995. pdf
2. T. Denoeux. A neural network classifier based on Dempster-Shafer theory. IEEE Transactions on Systems, Man and Cybernetics A, 30(2):131-150, 2000. pdf
3. T. Denoeux, S. Sriboonchitta and O. Kanjanatarakul. Evidential clustering of large dissimilarity data. Knowledge-Based Systems, vol. 106, pages 179-195, 2016. pdf
4. T. Denoeux. NN-EVCLUS: Neural Network-based Evidential Clustering. Information Sciences, Vol. 572, Pages 297-330, 2021. pdf
5. T. Denoeux. Calibrated model-based evidential clustering using bootstrapping. Information Sciences, Vol. 528, pages 17-45, 2020. pdf
6. Thierry Denoeux. Reasoning with fuzzy and uncertain evidence using epistemic random fuzzy sets: general framework and practical models. Fuzzy Sets and Systems (to appear), 2022.

Solution of exercises

1. Statistical inference

Data

1. Globalization data (2000, 2014)
2. Globalization dataset (full)