Belief Functions and Machine Learning

  1. F. Pichon, S. Diène, T. Denoeux, S. Ramel, D. Mercier. r-ERBFN: An Extension of the Evidential RBFN Accounting for the Dependence Between Positive and Negative Evidence. In: Destercke, S., Martinez, M.V., Sanfilippo, G. (eds) Scalable Uncertainty Management. SUM 2024. Lecture Notes in Computer Science, vol 15350. Springer, Cham, 2024. pdf
  2. T. Denoeux. Uncertainty quantification in regression neural networks using likelihood-based belief functions. In: Bi, Y., Jousselme, AL., Denoeux, T. (eds) Belief Functions: Theory and Applications. BELIEF 2024. Lecture Notes in Computer Science, vol 14909, pages 40-48. Springer, Cham, 2024. pdf
  3. Denoeux, T. (2024). Combination of Dependent Gaussian Random Fuzzy Numbers. In: Bi, Y., Jousselme, AL., Denoeux, T. (eds) Belief Functions: Theory and Applications. BELIEF 2024. Lecture Notes in Computer Science, vol 14909, pages 264-272. Springer, Cham, 2024. pdf
  4. T. Denoeux and V. Kreinovich. Algebraic Product Is the Only “And-like” Operation for Which Normalized Intersection Is Associative: A Proof. In: Hoang Phuong, N., Huyen Chau, N.T., Kreinovich, V. (eds) Machine Learning and Other Soft Computing Techniques: Biomedical and Related Applications. Studies in Systems, Decision and Control, vol 543. Springer, Cham, 2024. pdf
  5. 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
  6. Thierry Denoeux. An Evidential Neural Network Model for Regression Based on Random Fuzzy Numbers. In: Le Hégarat-Mascle, S., Bloch, I., Aldea, E. (eds) Belief Functions: Theory and Applications. BELIEF 2022. Lecture Notes in Computer Science, vol 13506. Springer, Cham, 2022, pp.57-66 pdf
  7. Andrea Campagner, Davide Ciucci, Thierry Denoeux. A Distributional Approach for Soft Clustering Comparison and Evaluation. In: Le Hégarat-Mascle, S., Bloch, I., Aldea, E. (eds) Belief Functions: Theory and Applications. BELIEF 2022. Lecture Notes in Computer Science, vol 13506. Springer, Cham, 2022, pp. 3-12. pdf
  8. H. Fu, X. Yue, W. Liu and T. Denoeux, “Stable Clustering Ensemble Based on Evidence Theory,” 2022 IEEE International Conference on Image Processing (ICIP), Bordeaux, France, 16-19 October 2022, pp. 2046-2050. pdf
  9. Wei Liu, Xiaodong Yue, Yufei Chen and Thierry Denoeux. Trusted Multi-View Deep Learning with Opinion Aggregation. In Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22), Vancouver, Canada, February 22-March 1, 2022. pdf
  10. Zh. Tong, Ph. Xu and T. Denoeux. Fusion of Evidential CNN Classifiers for Image Classification. In T. Denoeux, E. Lefèvre, Zh. Liu and F. Pichon (Eds), Belief Functions: Theory and Applications, Springer International Publishing, Cham, pages 168-176, 2021. pdf
  11. B. Yuan, X. Yue, Y. Lv and T. Denoeux. Evidential Deep Neural Networks for Uncertain Data Classification. In G. Li et al. (Eds), Knowledge Science, Engineering and Management part II (Proceedings of KSEM 2020), Springer, LNAI 12275, Hangzhou, China, August 28–30, pages 427-437, 2020. pdf
  12. Zheng Tong, Philippe Xu, and Thierry Denoeux. ConvNet and Dempster-Shafer Theory for Object Recognition. In N. Ben Amor, B. Quost and M. Theobald (Eds), Scalable Uncertainty Management, Springer International Publishing, Cham, pages 368-381, 2019. pdf
  13. T. Denoeux and P. Shenoy. An Axiomatic Utility Theory for Dempster-Shafer Belief Functions. International Symposium on Imprecise Probabilities: Theories and Applications (ISIPTA 2019), Ghent, Belgium, July 3-6, 2019. Proceedings of Machine Learning Research 103:145–155, 2019. pdf
  14. L. Ma and T. Denoeux. Making Set-valued Predictions in Evidential Classification: A Comparison of Different Approaches. International Symposium on Imprecise Probabilities: Theories and Applications (ISIPTA 2019), Ghent, Belgium, July 3-6, 2019. Proceedings of Machine Learning Research 103:276–285, 2019. pdf
  15. T. Denoeux and O. Kanjanatarakul. Multistep Prediction using Point-Cloud Approximation of Continuous Belief Functions. 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2019), New Orleans, USA, June 23-26, 2019. pdf
  16. Y. Qiao, S. Li and T. Denoeux. Collaborative Evidential Clustering. In R. B. Kearfott, I. Batyrshin, M. Reformat, M. Ceberio and V. Kreinovich (Eds), Fuzzy Techniques: Theory and Applications (Proceedings of NAFIPS 2019), Springer, pages 518-530, June 2019. pdf
  17. T. Denoeux. Logistic Regression Revisited: Belief Function Analysis. In: Destercke S., Denoeux T., Cuzzolin F., Martin A. (eds), Belief Functions: Theory and Applications. BELIEF 2018. Lecture Notes in Computer Science, vol 11069. Springer, pages 57-64, September 2018. pdf
  18. O. Kanjanatarakul, S. Kuson and T. Denoeux. An Evidential K-Nearest Neighbor Classifier based on Contextual Discounting. In: Destercke S., Denoeux T., Cuzzolin F., Martin A. (eds), Belief Functions: Theory and Applications. BELIEF 2018. Lecture Notes in Computer Science, vol 11069. Springer, pages 155-162, September 2018. pdf
  19. O. Kanjanatarakul and T. Denoeux. Distributed Data Fusion in the Dempster-Shafer framework. 12th System of Systems Engineering Conference (SoSE), IEEE, Waikoloa, Hawaii, June 2017. pdf
  20. T. Denoeux and O. Kanjanatarakul. Evidential Clustering: A Review. In V.-N. Huynh et al. (Eds.), Proceedings of the 5th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making (IUKM 2016), LNAI 9978, Springer-Verlag, Da Nang, Vietnam, November 30- December 2, pp. 24–35, 2016. pdf
  21. O. Kanjanatarakul, S. Sriboonchitta and Thierry Denoeux. k-EVCLUS: Clustering Large Dissimilarity Data in the Belief Function Framework. In J. Vejnarova and V. Kratochvil (Eds), Proceedings of the Fourth International Conference Belief Functions: Theory and Applications (BELIEF 2016), Springer-Verlag, LNAI 9861, Prague, Czech Republic, pp. 87-94, September 21-23, pages 105-112, 2016. pdf
  22. L. Sui and P. Feissel and T. Denoeux. Identification of Elastic Properties Based on Belief Function Inference. In J. Vejnarova and V. Kratochvil (Eds), Proceedings of the Fourth International Conference Belief Functions: Theory and Applications (BELIEF 2016), Springer-Verlag, LNAI 9861, Prague, Czech Republic, pp. 87-94, September 21-23, pages 182-189, 2016. pdf
  23. T. Denoeux and O. Kanjanatarakul. Beyond Fuzzy, Possibilistic and Rough: An Investigation of Belief Functions in Clustering. In M. B. Ferraro et al. (Eds), Soft Methods for Data Science, Proc. of the 8th International Conference on Soft Methods in Probability and Statistics (SMPS 2016), Springer-Verlag, AISC 456, Rome, Italy, September 12-14, pages 157-164, 2016. pdf
  24. C. Lian, S. Ruan and T. Denoeux. Joint feature transformation and selection based on dempster-shafer theory. 16th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU-2016), Part I, CCIS 610, pp. 253–261, Springer, Eindhoven, The Netherlands, June 2016. pdf
  25. Ph. Xu, F. Davoine and T. Denœux. Evidential Multinomial Logistic Regression for Multiclass Classifier Calibration. In Proceedings of the 18th International Conference on Information Fusion, pages 1106-1112, Washington, D.C., July 6-9, 2015. pdf
  26. L. Jiao, T. Denoeux and Q. Pan. Evidential Editing K-Nearest Neighbor Classifier. In S. Destercke and T. Denoeux (Eds), Symbolic and Quantitative Approaches to Reasoning with Uncertainty, Proc. of the 13th European Conference, ECSQARU 2015, Compiègne, France, July 15-17, pages 461-471, LNAI 9161, 2015. pdf
  27. O. Kanjanatarakul, N. Kaewsompong, S. Sriboonchitta and T. Denoeux. Estimation and Prediction Using Belief Functions: Application to Stochastic Frontier Analysis. In V.-N. Huynh et al. (eds.), Econometrics of Risk, Studies in Computational Intelligence 583, Springer, pages 171-184, 2015. pdf
  28. S. Leurcharusmee, J. Sirisrisakulchai, S. Sriboonchitta and T. Denoeux. The Classifier Chain Generalized Maximum Entropy Model for Multi-label Choice Problems. In V.-N. Huynh et al. (eds.), Econometrics of Risk, Studies in Computational Intelligence 583, Springer, pages 185-199, 2015. pdf
  29. N. Sutton-Charani, S. Destercke and T. Denoeux. Training and Evaluating Classifiers from Evidential Data: Application to E2M Decision Tree Pruning. In F. Cuzzolin (Ed.), Proceedings of the Third International Conference Belief Functions: Theory and Applications (BELIEF 2014), Springer-Verlag, LNAI 8764, Oxford, UK, pp. 87-94, September 26-28, 2014. pdf
  30. Ph. Xu, F. Davoine and T. Denoeux. Evidential Logistic Regression for Binary SVM Classifier Calibration. In F. Cuzzolin (Ed.), Proceedings of the Third International Conference Belief Functions: Theory and Applications (BELIEF 2014), Springer-Verlag, LNAI 8764, Oxford, UK, pp. 49–57, September 26-28, 2014. Best student paper award pdf
  31. S. Leurcharusmee, P. Jatukannyaprateep, S. Sriboonchitta and T. Denoeux. The Evidence-Theoretic k-NN Rule for Rank-Ordered Data: Application to Predict an Individual’s Source of Loan. In F. Cuzzolin (Ed.), Proceedings of the Third International Conference Belief Functions: Theory and Applications (BELIEF 2014), Springer-Verlag, LNAI 8764, Oxford, UK, pp. 58–67, September 26-28, 2014. pdf
  32. K. Autchariyapanitkul, S. Chanaim, S. Sriboonchitta and T. Denoeux. Predicting Stock Returns in the Capital Asset Pricing Model Using Quantile Regression and Belief Functions. In F. Cuzzolin (Ed.), Proceedings of the Third International Conference Belief Functions: Theory and Applications (BELIEF 2014), Springer-Verlag, LNAI 8764, Oxford, UK, pp. 219–226, September 26-28, 2014. pdf
  33. Ph. Xu, F. Davoine and T. Denoeux. Evidential combination of pedestrian detectors. In Proceedings of the British Machine Vision Conference (BMVC), pages 1-14, Nottingham, England, Sep 2014. pdf
  34. L. Jiao, T. Denoeux and Q. Pan. Fusion of pairwise nearest-neighbor classifiers based on pairwise-weighted distance metric and Dempster-Shafer theory. Proc. 17th International Conference on Information Fusion, Salamanca, Spain, 7-10th July 2014. pdf
  35. Ph. Xu, F. Davoine and T. Denoeux. Transformation de scores SVM en fonctions de croyance. Dix-neuvième congrès national sur la Reconnaissance de Formes et l'Intelligence Artificielle (RFIA'14), Rouen, France, Juin 2014. pdf
  36. N. Sutton-Charani, S. Destercke and T. Denoeux. Learning decision trees from uncertain data with an evidential EM approach. Proc. 12th International Conference on Machine Learning and Applications (ICMLA'13), Miami, Florida, USA, December 4-7, 2013. pdf
  37. N. El Zoghby, V. Cherfaoui and T. Denoeux. Optimal object association from pairwise evidential mass functions. Proc. 16th International Conference on Information Fusion, Istanbul, Turkey, 9-12 July 2013. pdf
  38. N. Ben Abdallah, N. Mouhous-Voyneau and T. Denoeux. Using Dempster-Shafer Theory to model uncertainty in climate change and environmental impact assessments. Proc. 16th International Conference on Information Fusion, Istanbul, Turkey, 9-12 July 2013. pdf
  39. J.-B. Bordes, F. Davoine, Ph. Xu and T. Denoeux. Evidential grammars for image interpretation - Application to multimodal traffic scene understanding. In Z. Qin and V. N. Huyn (Eds), Integrated Uncertainty in Knowledge Modelling and Decision Making (IUKM 2013), LNAI 8032, Beijing, China, pages 65-78, 2013. pdf
  40. Ph. Xu, F. Davoine, J.-B. Bordes, H. Zhao and T. Denoeux. Information Fusion on Oversegmented Images: An Application for Urban Scene Understanding. Proceedings of the thirteenth IAPR International Conference on Machine Vision Applications, May 20-23, 2013, Kyoto, Japan, pages 189–193. pdf
  41. Ph. Xu, F. Davoine, T. Denoeux and J.-B. Bordes. Fusion d’informations sur des images sursegmentées : Une application à la compréhension de scènes routières. Orasis, Congrès des jeunes chercheurs en vision par ordinateur, Cluny, France, Juin 2013. pdf
  42. N. Ben Abdallah, N. Mouhous Voyneau et T. Denoeux. Méthodologie de combinaison d'informations statistiques et d'avis d'experts dans le cadre de la théorie de Dempster et Shafer : Application au dimensionnement des ouvrages côtiers. In Rencontres Francophones sur la Logique Floue et ses Applications (LFA 2012), pages 79-86, Compiègne, France, November 2012, Cépaduès-Editions. pdf
  43. N. Sutton-Charani, S. Destercke et T. Denoeux. Arbres de classification construits à  partir de fonctions de croyance. In Rencontres Francophones sur la Logique Floue et ses Applications (LFA 2012), pages 237-244, Compiègne, France, November 2012, Cépaduès-Editions. pdf
  44. N. El Zoghby, V. Cherfaoui, B. Ducourthial et T. Denoeux. Fusion distribuée évidentielle pour la détection d'attaques sybil dans un réseau de véhicules. In Rencontres Francophones sur la Logique Floue et ses Applications (LFA 2012), pages 63-70, Compiègne, France, November 2012, Cépaduès-Editions. pdf
  45. S. Kanj, F. Abdallah et T. Denoeux. La méthode RAkEL évidentielle pour la classification multi-label. In Rencontres Francophones sur la Logique Floue et ses Applications (LFA 2012), pages 185-192, Compiègne, France, November 2012, Cépaduès-Editions. pdf
  46. B. Ducourthial, V. Cherfaoui and T. Denoeux. Self-stabilizing distributed data fusion. In Stabilization, Safety, and Security of Distributed Systems (Proceedings of SSS 2012, Toronto, October 2012), Lecture Notes in Computer Science Volume 7596, 2012, pp 148-162. pdf
  47. R. J. Almeida, T. Denoeux and U. Kaymak. Constructing rule-based models using the belief functions framework. In S. Greco et al. (Eds), Proceedings of IPMU 2012, Part III, Springer, CCIS 299, Catania, Italy, July 2012, pages 554-563. pdf
  48. S. Kanj, F. Abdallah and T. Denoeux. Purifying training data to improve performance of multi-label classification algorithms. 15th Int. Conf. on Information Fusion (FUSION 2012), Singapore, 9-12 July, 2012, pages 1784-1792. pdf
  49. M.-H. Masson and T. Denoeux. Ranking from pairwise comparisons in the belief functions framework. In T. Denoeux and M.-H. Masson (Eds), Belief functions: theory and applications. Proc. of the 2nd Int. Conf. on Belief Functions, Springer, AISC 164, Compiègne, France, 9-11 May 2012, pages 311-318. pdf
  50. N. Ben Abdallah, N. Mouhous Voyneau and T. Denoeux. Combining statistical and expert evidence within the D-S framework: Application to hydrological return level estimation. In T. Denoeux and M.-H. Masson (Eds), Belief functions: theory and applications. Proc. of the 2nd Int. Conf. on Belief Functions, Springer, AISC 164, Compiègne, France, 9-11 May 2012, pages 393-400. pdf
  51. D. Dubois and T. Denoeux. Conditioning in Dempster-Shafer theory: prediction vs. revision. In T. Denoeux and M.-H. Masson (Eds), Belief functions: theory and applications. Proc. of the 2nd Int. Conf. on Belief Functions, Springer, AISC 164, Compiègne, France, 9-11 May 2012, pages 385-392. pdf
  52. N. El Zoghby, V. Cherfaoui, B. Ducourthial and T. Denoeux. Distributed Data fusion for detecting Sybil attacks in VANETs. In T. Denoeux and M.-H. Masson (Eds), Belief functions: theory and applications. Proc. of the 2nd Int. Conf. on Belief Functions, Springer, AISC 164, Compiègne, France, 9-11 May 2012, pages 351-358. pdf
  53. E. Ramasso, T. Denoeux and N. Zerhouni. Partially-Hidden Markov Models. In T. Denoeux and M.-H. Masson (Eds), Belief functions: theory and applications. Proc. of the 2nd Int. Conf. on Belief Functions, Springer, AISC 164, Compiègne, France, 9-11 May 2012, pages 359-366. pdf
  54. S. Kanj, F. Abdallah and T. Denoeux. Evidential Multi-label classification using the Random k-Label sets approach. In T. Denoeux and M.-H. Masson (Eds), Belief functions: theory and applications. Proc. of the 2nd Int. Conf. on Belief Functions, Springer, AISC 164, Compiègne, France, 9-11 May 2012, pages 21-28. pdf
  55. N. Sutton-Charani, S. Destercke and T. Denoeux. Classification trees based on belief functions. In T. Denoeux and M.-H. Masson (Eds), Belief functions: theory and applications. Proc. of the 2nd Int. Conf. on Belief Functions, Springer, AISC 164, Compiègne, France, 9-11 May 2012, pages 77-84. pdf
  56. V. Antoine, B. Quost, M.-H. Masson and T. Denoeux. CEVCLUS: Constrained evidential clustering of proximity data. In Proceedings of EUSFLAT 2011, Aix-les-Bains, France, 18-22 July 2011, pages 876-882, Atlantis Press, doi:10.2991/eusflat.2011.80. pdf
  57. T. Denoeux. Maximum likelihood from evidential data: an extension of the EM algorithm. In C. Borgelt et al. (Eds), Combining soft computing and statistical methods in data analysis (Proceedings of SMPS 2010, Oviedo, Spain, September 28 - October 1, 2010), Advances in Intelligent and Soft Computing, pages 181-188, Springer, 2010. pdf
  58. D. Dubois and T. Denoeux. Statistical Inference with Belief Functions and Possibility Measures : a discussion of basic assumptions. In C. Borgelt et al. (Eds), Combining soft computing and statistical methods in data analysis (Proceedings of SMPS 2010, Oviedo, Spain, September 28 - October 1, 2010), Advances in Intelligent and Soft Computing, pages 217-225, Springer, 2010. pdf
  59. L.-H. Masson, Li Qiang and T. Denoeux. Ordonnancement d’alternatives dans le cadre de la théorie des fonctions de croyance. In Rencontres Francophones sur la Logique Floue et ses Applications (LFA 2010), pages 161-167, Lannion, France, November 2010, Cépaduès-Editions. pdf
  60. V. Antoine, B. Quost, M.-H. Masson and T. Denoeux. CECM: Adding pairwise constraints to evidential clustering. 2010 IEEE International Conference on Fuzzy Systems (FUZZ‐IEEE 2010), Barcelona, Spain, July 2010, pages 879-886, IEEE. pdf
  61. Z. Younes, F. Abdallah and T. Denoeux. Fuzzy Multi-Label Learning Under Veristic Variables. 2010 IEEE International Conference on Fuzzy Systems (FUZZ‐IEEE 2010), Barcelona, Spain, July 2010, pages 1696-1703, IEEE. pdf
  62. Z. Younes, F. Abdallah and T. Denoeux. Evidential multi-Label classification approach to learning from data with imprecise labels. In Proc. of the 13th Int. Conf. on Information Processing and Management of Uncertainty (IPMU 2010), June 28-July 2, 2010, Dortmund, Germany, LNAI-6178, pp. 119-128, Springer-Verlag. pdf
  63. V. Antoine, B. Quost, M.-H. Masson and T. Denoeux. CECM : Algorithme évidentiel des c-moyennes avec contraintes. In Rencontres Francophones sur la Logique Floue et ses Applications (LFA 2009), pages 275-282, Annecy, France, November 2009, Cépaduès-Editions. pdf
  64. D. Dubois and T. Denoeux. Pertinence et Sincérité en Fusion d'Informations. In Rencontres Francophones sur la Logique Floue et ses Applications (LFA 2009), pages 23-30, Annecy, France, November 2009, Cépaduès-Editions. pdf
  65. Z. Younes, F. Abdallah and T. Denoeux. An Evidence-Theoretic k-Nearest Neighbor Rule for Multi-Label Classification. In Proceedings of the 3rd International Conference on Scalable Uncertainty Management (SUM 2009), September 2009, Washington, DC, USA, LNAI-5785, pp. 297-308, Springer-Verlag.pdf
  66. E. Côme, L. Oukhellou, T. Denoeux and P. Aknin. Noiseless Independent Factor Analysis with mixing constraints in a semi-supervised framework. Application to railway device fault diagnosis. 19th International Conference on Artificial Neural Networks (ICANN '09), 14-17 September 2009, Limassol, Cyprus, LNCS 5769, pp 416-425, Springer-Verlag. pdf
  67. F. Pichon and T. Denoeux. Interpretation and Computation of alpha-Junctions for Combining Belief Functions. 6th International Symposium on Imprecise Probability: Theories and Applications (ISIPTA '09), Durham, United Kingdom, 2009. pdf
  68. M.-H. Masson and T. Denoeux. Belief Functions and Cluster Ensembles. In C. Sossai and G. Chemello (Eds.): ECSQARU 2009, LNAI 5590, pp. 323–334, 2009. Springer-Verlag. pdf
  69. B. Quost and T. Denoeux. Learning from data with uncertain labels by boosting credal classifiers. In J. Pei, L. Getoor and A. de Keijzer (Eds), Proc. of the First ACM SIGKDD Int. Workshop on Knowledge Discovery from Uncertain Data, Paris, France, pp. 38-47, June 2009. pdf
  70. G. Nassreddine, F. Abdallah and T. Denoeux. A state estimation method for multiple model systems using belief function theory. In 12th Int. Conf. on Information Fusion (FUSION '09), Seattle, Washington, USA, July 2009. pdf
  71. G. Nassreddine, F. Abdallah and T. Denoeux. Map Matching algorithm using interval analysis and Dempster-Shafer theory. IEEE Intelligent Vehicles Symposium (IV 2009), Xi'an, Shannxi, China, June 3-5 2009. pdf
  72. E. Côme, L. Oukhellou, P. Aknin and T. Denoeux. Partially-supervised learning in Independent Factor Analysis. In Proc. of the 17 th European Symposium on Artificial Neural Networks (ESANN 2009), Bruges, Belgium, 22-24 April 2009. pdf
  73. M.-H. Masson and T. Denoeux. KECM : Une version noyau de l’algorithme évidentiel des c-moyennes. In Rencontres Francophones sur la Logique Floue et ses Applications (LFA '08), pages 216-223, Lens, France, Octobre 2008, Cépaduès-Editions.pdf
  74. E. Côme, L. Oukhellou, T. Denoeux, and P. Aknin. Mixture model estimation with soft labels. In D. Dubois et al. (Eds), Soft Methods for Handling Variability and Imprecision (Proceedings of the 4th Int. Conference on Soft Methods in Probability and Statistics, Toulouse, France, 8-10 September 2008), pages 165-174, Springer-Verlag, Berlin, 2008. pdf
  75. Z. Younes, F. Aballah and T. Denoeux. Multi-label classification algorithm derived from k-nearest neighbor rule with label dependencies. In Proceedings of the 16th European Signal Processing Conference, Lausanne, Switzerland, August 25-29, 2008. pdf
  76. B. Quost, M.-H. Masson and T. Denoeux. Refined classifier combination using belief functions. In Proceedings of the 11th Int. Conf. on Information Fusion (FUSION '08), pages 776-782, Cologne, Germany, June 30-July 03, 2008. pdf
  77. B. Quost, T. Denoeux, and M.-H. Masson. Adapting a Combination Rule to Non-Independent Information Sources. In L. Magdalena, M. Ojeda-Aciego, J.L. Verdegay (eds): Proceedings of IPMU'08, pp. 448-455, Torremolinos (Malaga), June 22-27, 2008. pdf
  78. D. Mercier, T. Denoeux and M.-H. Masson. A parameterized family of belief functions correction mechanisms. In L. Magdalena, M. Ojeda-Aciego, J.L. Verdegay (eds): Proceedings of IPMU'08, pp. 306-313, Torremolinos (Malaga), June 22-27, 2008. pdf
  79. F. Pichon and T. Denoeux. A new singular property of the unnormalized Dempster’s rule among uninorm-based combination rules. In L. Magdalena, M. Ojeda-Aciego, J.L. Verdegay (eds): Proceedings of IPMU'08, pp. 314-321, Torremolinos (Malaga), June 22-27, 2008. pdf
  80. F. Pichon and T. Denoeux. T-norm and Uninorm-Based Combination of Belief Functions. In Proceedings of NAFIPS '08, New-York, May 19-22, 2008. (Best student paper award). pdf
  81. F. Pichon and T. Denoeux. A New Justification of the Unnormalized Dempster’s Rule of Combination from the Least Commitment Principle. 21st International FLAIRS Conference (FLAIRS '08), Special Track on Uncertain Reasoning, pages 666-671, Coconut Grove, Florida, USA, May 15-17, 2008. pdf
  82. M.-H. Masson and T. Denoeux. ECM : algorithme évidentiel des c-moyennes. In Rencontres Francophones sur la Logique Floue et ses Applications (LFA '07), pages 17-24, Nîmes, France, Novembre, 2007, Cépaduès-Editions.pdf
  83. F. Pichon and T. Denoeux. Structures de croyance latente : Règles de combinaison conjonctive et prise de décision. In Rencontres Francophones sur la Logique Floue et ses Applications (LFA '07), pages 91-98, Nîmes, France, Novembre, 2007, Cépaduès-Editions.pdf
  84. G. Nassreddine, F. Abdallah and T. Denoeux. Estimation d'état par la théorie des fonctions de croyance. In Rencontres Francophones sur la Logique Floue et ses Applications (LFA '07), pages 115-124, Nîmes, France, Novembre, 2007, Cépaduès-Editions.pdf
  85. A. Aregui and T. Denoeux. Consonant Belief Function induced by a Confidence Set of Pignistic Probabilities. In K. Mellouli, Ed, Proceedings of ECQSARU '07, pages 344-355, Hammamet, Tunisia, October/November, 2007, Springer-Verlag.pdf
  86. F. Pichon and T. Denoeux. On Latent Belief Structures. In K. Mellouli, Ed, Proceedings of ECQSARU '07, pages 368-380, Hammamet, Tunisia, October/November, 2007, Springer-Verlag.pdf
  87. A. Aregui and T. Denoeux. Fusion of one-class classifiers in the belief function framework. In Proc. of the 10th International Conference on Information Fusion, Québec, Canada, July 2007 (Best student paper award). pdf
  88. A. Aregui and T. Denoeux. Constructing Predictive Belief Functions from Continuous Sample Data Using Confidence Bands. In G. De Cooman and J. Vejnarova and M. Zaffalon (Eds), Proceedings of the Fifth International Symposium on Imprecise Probability: Theories and Applications (ISIPTA '07), pages 11-20, Prague, Czech Republic, July 2007. pdf
  89. D. Mercier, G. Cron, T. Denoeux and M.-H. Masson. Une Approche Globale de Fusion d’Adresses Postales Basée sur la Théorie des Fonctions de Croyance. In Rencontres Francophones sur la Logique Floue et ses Applications, pages 287-294, Toulouse, France, October 2006. Cépaduès. pdf
  90. D. Mercier, G. Cron, T. Denoeux and M. Masson. Vers un Modèle de Fusion de Décisions de Lecteurs d'Adresses Postales Basé sur la Théorie des Fonctions de Croyance. In L. Likforman-Sulem (Ed), Actes du 9e Colloque International Francophone sur l'Ecrit et le Document (CIFED'2006), pages 79–84, Fribourg, Suisse, September 2006, pdf
  91. T. Denoeux. The cautious rule of combination for belief functions and some extensions, Proceedings of FUSION'2006, Florence, Italy, July 2006. pdf
  92. D. Mercier, T. Denoeux and M. Masson. General correction mechanisms for weakening or reinforcing belief functions, Proceedings of FUSION'2006, Florence, Italy, July 2006. pdf
  93. T. Denoeux. Construction of predictive belief functions using a frequentist approach, Proceedings of IPMU'2006, Vol II, pages 1412-1419, Paris, France, July 2006. pdf
  94. D. Mercier, T. Denoeux and M.-H. Masson. Refined sensor tuning in the belief function framework using contextual discounting, Proceedings of IPMU'2006, Vol II, pages 1443-1450, Paris, France, July 2006. pdf
  95. B. Quost, T. Denoeux, and M. Masson. One-against-all combination in the framework of belief functions, Proceedings of IPMU'2006, Vol I, pages 356-363, Paris, France, July 2006. pdf
  96. A. Ben Yaghlane, T. Denoeux and K. Mellouli. Elicitation of expert opinions for constructing belief functions, Proceedings of IPMU'2006, Vol I, pages 403-411, Paris, France, July 2006. pdf
  97. A. Aregui and T. Denoeux. Novelty detection in the belief functions framework, Proceedings of IPMU'2006, Vol I, pages 412-419, Paris, France, July 2006. pdf
  98. A. Ben Yaghlane, T. Denoeux and K. Mellouli. Constructing belief functions from expert opinions, Proceedings of the 2nd International Conference on Information & Communication Technologies: from Theory to Applications (ICTTA'06), Damascus, Syria, April 2006. IEEE. pdf
  99. D. Mercier, G. Cron, T. Denoeux and M. Masson. Fusion of multi-level decision systems using the Transferable Belief Model, Proceedings of FUSION'2005, Philadelphia, PA, USA, July 2005. pdf
  100. B. Quost, T. Denoeux, M. Masson. Pairwise Classifier Combination in the Framework of Belief Functions, Proceedings of FUSION'2005, Philadelphia, PA, USA, July 2005. pdf
  101. D. Mercier, B. Quost and T. Denoeux. Contextual discounting of belief functions. In L. Godo, Ed, Proceedings of ECQSARU'2005, pages 552-562, Barcelona, Spain, July 2005, Springer-Verlag . pdf
  102. B. Quost, T. Denoeux and M. Masson. Combinaison de classifieurs binaires dans le cadre du Modèle des Croyances Transférables. In Rencontres Francophones sur la Logique Floue et ses Applications, pages 123-130, Nantes, France, November 2004. Cépaduès.pdf
  103. M. Masson et T. Denoeux. Clustering of proximity data using belief functions. Proceedings of IPMU'2002, Vol I, pages 609-616, Annecy, France, July 2002. pdf
  104. P. Vannoorenbergue and T. Denoeux. Handling uncertain labels in multiclass problems using belief decision trees IPMU'2002, Vol. III, pages 1919-1926, Annecy, France, July 2002. postscriptpdf
  105. P. Vannoorenbergue and T. Denoeux. Likelihood-based vs. distance-based evidential classifiers Proc. of FUZZ-IEEE'2001, Melbourne, Australia, December 2001. IEEE. postscript
  106. E Lefevre, P. Vannoorenbergue, O. Colot et T. Denoeux. Combinaison d'évidence pour la discrimination en présence d'étiquetage imprécis Rencontres Francophones sur la Logique Floue et ses Applications, pages 65-72, Mons, Belgique, November 2001. Cépaduès. postscript
  107. A. Ben Yaghlane, T. Denoeux and K. Melloui. Coarsening approximations of belief functions. In S. Benferhat and P. Besnard, Eds, Proceedings of ECQSARU'2001, pages 362-373, Toulouse, September 2001, Springer-Verlag . postscript, pdf
  108. C. Ambroise, T. Denoeux, G. Govaert and P. Smets. Learning from an imprecise teacher: probabilistic and evidential approaches. Proceedings of ASMDA'2001, vol. 1, pages 100-105, Compiègne, France, June 2001. pdf
  109. T. Denoeux and M. Skarstein-Bjanger, Induction of decision trees from partially classified data. Proceedings of SMC'2000, pages 2923-2928, Nashville, TN, October 2000, IEEE. pdf
  110. T. Denoeux. Inner and outer clustering approximations of belief structures. In Proceedings of IPMU'2000, Vol. I, pages 125-132, Madrid, July 2000. postscript
  111. J. François, Y. Grandvalet, T. Denoeux and J.-M. Roger. Bagging belief structures in Dempster-Shafer K-NN rule. In Proceedings of IPMU'2000, Vol. I, pages 111-118, Madrid, July 2000. postscript
  112. T. Denoeux. Modélisation de l'imprécis et de l'incertain en apprentissage supervisé par la théorie des fonctions de croyance (invited talk). Rencontres Francophones sur la Logique Floue et ses Applications, pages 13-20, Valenciennes, october 1999. Cépaduès. postscript
  113. S. Petit-Renaud et T. Denoeux. Application de la théorie des fonctions de croyance en régression. Rencontres Francophones sur la Logique Floue et ses Applications, pages 169-176, Valenciennes, october 1999. Cépaduès. postscript
  114. S. Petit-Renaud et T. Denoeux. Regression analysis using fuzzy evidence theory. Proceedings of FUZZ-IEEE'99, vol. 3, pages 1229-1234, Seoul, August 1999. postscript. (Best student paper)
  115. M. Rombaut, I. Jarkass et T. Denoeux. State recognition in discrete dynamical systems using Petri nets and evidence theory. In A. Hunter and S. Pearsons (Eds), Symbolic and quantitative approaches to reasoning and uncertainty (ECSQARU'99), pages 352-361, London, June 1999. Springer Verlag. postscript
  116. S. Petit-Renaud et T. Denoeux. Handling different forms of uncertainty in regression analysis: a fuzzy belief structure approach. In A. Hunter and S. Pearsons (Eds), Symbolic and quantitative approaches to reasoning and uncertainty (ECSQARU'99), pages 340-351, London, June 1999. postscript
  117. T. Denoeux. Function approximation in the framework of evidence theory: A connectionist approach. Proceedings of the 1997 International Conference on Neural Networks (ICNN'97) , volume 1, pages 199-203, Houston, June 1997. IEEE. pdf
  118. L. M. Zouhal and T. Denoeux. Generalizing the evidence-theoretic k-NN rule to fuzzy pattern recognition. Proceedings of the Second International Symposium on Fuzzy Logic and Applications ISFL'97, pages 294-300, Zurich, February 1997. ICSC Academic Press. postscript
  119. L. M. Zouhal and T. Denoeux. Reconnaissance de Formes Floues par la Théorie de Dempster et Shafer. In Rencontres Francophones sur la Logique Floue et ses Applications, pages 3-8, Nancy, Decembre 1996. Cépaduès. postscript
  120. T. Denoeux and G. Govaert. Combined supervised and unsupervised learning for system diagnosis using Dempster-Shafer theory. In P. Borne, M. Staroswiecki, J. P. Cassar and S. El Khattabi (Eds) CESA'96 IMACS Multiconference, Computational Engineering in Systems Applications. Symposium on Control, Optimization and Supervision, volume 1, pages 104-109, Lille, July 9-12, 1996. postscript
  121. L. M. Zouhal and T. Denoeux. An adaptive k-NN rule based on Dempster-Shafer theory. In Proceedings of the 6th International Conference on Computer Analysis of Images and Patterns (CAIP'95), pages 310-317, Prague, September 1995. Springer Verlag. postscript
  122. L. M. Zouhal and T. Denoeux. Une méthode de discrimination non paramétrique basée sur la théorie de Dempster et Shafer. In Actes du Quinzième Colloque GRETSI, pages 689-692, Juan les Pins, Septembre 1995. postscript
  123. L. M. Zouhal and T. Denoeux. A comparison between fuzzy and evidence-theoretic k-NN rules for pattern recognition. In Proceedings of EUFIT'95, vol. 3, pages 1319-1325, Aachen, August 1995. postscript
  124. T. Denoeux. An evidence-theoretic neural network classifier. In IEEE International Conference on Systems, Man and Cybernetics, volume 3, pages 712-717, Vancouver, October 1995. postscript
  125. T. Denoeux. Application of evidence theory to k-NN pattern classification. In E. S. Gelsema and L. N. Kanal, editors, Pattern Recognition in Practice IV, pages 13-24. Elsevier, Amsterdam, 1994.