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--- en:bf [2022/06/14 14:55] – tdenoeux
+++ en:bf [2023/10/21 05:38] (current) – tdenoeux
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-====== Introduction to belief functions ======
+====== Theory of belief functions: Application to machine learning and statistical inderence======
 **Instructor:** Thierry Denoeux
@@ Line 5: / Line 5: @@
 Description: This is an introductory course on belief functions, with focus on data analysis, machine learning and statistical inference.
+** Course outline **
+  - Belief functions on finite frames
+  - Decision analysis
+  - Evidential k-NN classifier
+  - Evidential neural network classifier
+  - Predictive belief functions for categorical and ordinal variables
+  - Random sets and belief functions in a general mathematical framework
+  - Possibility theory and epistemic random fuzzy sets
+  - Statistical prediction using belief functions: application to linear and logistic regression
+  - The ENNreg model
+  - Uncertain data and the evidential EM algorithm
 **Slides**
-  - {{ :en:bf_cmu_2019_chapter1.pdf |Basic notions}}
+  - {{ :en:bf2023_lecture1.pdf |Belief functions on finite frames. Dempster's rule}}
-  - {{ :en:bf_cmu_2019_chapter2.pdf |Decision-making}}
+  - {{ :en:bf2023_lecture2.pdf |Decision analysis and classification}}
-  - {{ :en:bf_cmu_2019_chapter3.pdf |Statistical inference}}
+  - {{ :en:bf2023_lecture3.pdf |Multinomial predictive belief functions}}
+  - {{ :en:bf2023_lecture4.pdf |Statistical inference}}
+**Exercises**
+  - {{ :en:bf2023_ex_lecture1.pdf |Exercises on Chapter 1}}
+    - {{ :en:bf2023_ex_lecture1sol.pdf |Solutions}}
+  - {{ :en:bf2023_ex_lecture2.pdf |Exercises on Chapter 2}}
+    - {{ :en:bf2023_ex_lecture2sol.pdf |Solutions}}
+  - {{ :en:bf2023_ex_classification.pdf |Exercises on classification}}
+    - {{ :en:bf2023_ex_classif_sol.pdf |Solutions}}
+  - {{ :en:bf2023_ex_pbf.pdf |Exercise on multinomial predictive belief functions}}
+    - {{ :en:bf2023_ex_pbf_sol.pdf |Solutions}}
+  - {{ :en:bf2023_ex_inference.pdf |Exercises on statistical inference}}
+    - {{ :en:bf2023_ex_inference_sol.pdf |Solutions}}
+  - {{ :en:bf2023_projets.pdf |Projects}}
+    - {{ :en:bf2023_projects_sol.pdf |Solutions}}
 **Papers**
-  - 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. {{en:revues: smc95.pdf|pdf}}
+  - 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. {{ :en:smc95_final.pdf |pdf}}
   - T. Denoeux. A neural network classifier based on Dempster-Shafer theory. IEEE Transactions on Systems, Man and Cybernetics A, 30(2):131-150, 2000. {{en:revues: smc2000.pdf|pdf}}
-  - T. Denoeux, S. Sriboonchitta and O. Kanjanatarakul. Evidential clustering of large dissimilarity data. Knowledge-Based Systems, vol. 106, pages 179-195,  2016. {{:en:publi:evclus_kbs_v2_clean.pdf|pdf}}
+  - T. Denoeux. Constructing belief functions from sample data using multinomial confidence regions. International Journal of Approximate Reasoning, Vol. 42, pages 228-252, 2006. {{ :en:pbf_final.pdf |pdf}}
-  - T. Denoeux. NN-EVCLUS: Neural Network-based Evidential Clustering. Information Sciences, Vol. 572, Pages 297-330, 2021. {{ :en:publi:nn_evclus_insv2.pdf |pdf}}
+  - 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. {{ :en:ijar_2016_final.pdf |pdf}}
-  - T. Denoeux. Calibrated model-based evidential clustering using bootstrapping. Information Sciences, Vol. 528, pages 17-45, 2020. {{ :en:publi:bootclus_v2_clean.pdf |pdf}}
+  - 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. {{ :en:rfs2_final.pdf |pdf}}
-  - 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. {{en:publi:likelihood_v2.pdf|pdf}}
+  - T. Denoeux. Quantifying Prediction Uncertainty in Regression using Random Fuzzy Sets: the ENNreg model. IEEE Transactions on Fuzzy Systems (to appear), 2023. {{ :en:publi:ennreg_tfs_final.pdf |pdf}}
-  - 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.
+  - 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. {{en:publi:tkde2011.pdf|pdf}}
-  - 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. {{ :en:publi:random_fs_v2clean.pdf |pdf}}
+  - 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. {{ :en:adac2017_final.pdf |pdf}}
-**Solution of exercises**
-  - {{ :en:inference.r.zip |Statistical inference}}
 **Data**
@@ Line 33: / Line 58: @@
   -{{:en:global2000_2014.txt|Globalization data (2000, 2014)}}
   -{{:en:globalization_2017_short.xlsx.zip|Globalization dataset (full)}}
+  -{{ :en:gdp.csv.zip |GDP data}}
+  -{{ :en:credit_approval.zip |Credit approval dataset}}

UMR CNRS 7253

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