UMR CNRS 7253

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Introduction to belief functions

Instructor: Thierry Denoeux

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

Slides

Lecture notes

Videos

Exercises

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. L. M. Zouhal and T. Denoeux. An evidence-theoretic k-NN rule with parameter optimization. IEEE Transactions on Systems, Man and Cybernetics - Part C, 28(2):263-271,1998. pdf
  3. 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
  4. T. Denoeux, S. Sriboonchitta and O. Kanjanatarakul. Evidential clustering of large dissimilarity data. Knowledge-Based Systems, vol. 106, pages 179-195, 2016. pdf
  5. T. Denoeux. NN-EVCLUS: Neural Network-based Evidential Clustering. Information Sciences, Vol. 572, Pages 297-330, 2021. pdf
  6. T. Denoeux. Calibrated model-based evidential clustering using bootstrapping. Information Sciences, Vol. 528, pages 17-45, 2020. pdf
  7. T. Denoeux. Likelihood-based belief function: justification and some extensions to low-quality data. International Journal of Approximate Reasoning, Volume 55, Issue 7, pages 1535–1547, 2014. pdf
  8. N. Ben Abdallah, N. Mouhous-Voyneau and T. Denoeux. Combining statistical and expert evidence using belief functions: Application to centennial sea level estimation taking into account climate change. International Journal of Approximate Reasoning, Vol. 55, Issue 1, Part 3, pages 341–354, 2014. pdf
  9. O. Kanjanatarakul, T. Denoeux and S. Sriboonchitta. Prediction of future observations using belief functions: a likelihood-based approach. International Journal of Approximate Reasoning, Vol. 72, pages 71-94, 2016.
  10. Ph. Xu, F. Davoine, H. Zha and T. Denoeux. Evidential calibration of binary SVM classifiers. International Journal of Approximate Reasoning, Vol 72, pages 55-70, 2016. pdf
  11. T. Denoeux. Maximum likelihood estimation from Uncertain Data in the Belief Function Framework. IEEE Transactions on Knowledge and Data Engineering, Vol. 25, Issue 1, pages 119-130, 2013. pdf
  12. B. Quost, T. Denoeux and S. Li. Parametric Classification with Soft Labels using the Evidential EM Algorithm. Linear Discriminant Analysis vs. Logistic Regression. Advances in Data Analysis and Classification, Vol. 11, Issue 4, pp 659–690, 2017. pdf
  13. 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. pdf

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