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Belief Functions and Machine Learning

Theory of belief functions

  1. 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
  2. A. Campagner, D. Ciucci and T. Denoeux. Belief Functions and Rough Sets: Survey and New Insights. International Journal of Approximate Reasoning, Vol. 143, Pages 192-215, 2022. pdf
  3. 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
  4. T. Denoeux. Distributed combination of belief functions. Information Fusion, Vol. 65, pages 179-191, 2021. pdf
  5. T. Denoeux and P. P. Shenoy. An Interval-Valued Utility Theory for Decision Making with Dempster-Shafer Belief Functions. International Journal of Approximate Reasoning, Vol. 124, pages 194-216, 2020. pdf
  6. T. Denoeux. Decision-Making with Belief Functions: a Review. International Journal of Approximate Reasoning, Vol. 109, Pages 87-110, 2019. pdf
  7. M.-H. Masson, S. Destercke and T. Denoeux. Modelling and predicting partial orders from pairwise belief functions. Soft Computing, Vol. 20, Issue 3, pp 939-950, 2016. pdf
  8. T. Denoeux, N. El Zoghby, V. Cherfaoui and A. Jouglet. Optimal object association in the Dempster-Shafer framework. IEEE Transactions on Cybernetics, Vol. 44, Issue 22, pages 2521-2531, 2014. pdf
  9. T. Denoeux and M.-H. Masson. Evidential reasoning in large partially ordered sets. Application to multi-label classification, ensemble clustering and preference aggregation. Annals of Operations Research, Volume 195, Issue 1, Page 135-161, 2012. pdf
  10. F. Pichon, D. Dubois and T. Denoeux. Relevance and truthfulness in information correction and fusion. International Journal of Approximate Reasoning, Vol. 53, Issue 2, pages 159-175, 2012. pdf
  11. G. Nassreddine, F. Abdallah and T. Denoeux. State estimation using interval analysis and belief function theory: Application to dynamic vehicle localization. IEEE Transactions on Systems, Man and Cybernetics B, vol. 40, Issue 5, pages 1205-1218, 2010. pdf
  12. F. Pichon and T. Denoeux. The unnormalized Dempster's rule of combination: a new justi fication from the Least Commitment Principle and some extensions. Journal of Automated Reasoning, Vol. 45, Issue 1, pages 61-87, 2010. pdf
  13. T. Denoeux, Z. Younes and F. Abdallah. Representing uncertainty on set-valued variables using belief functions. Artificial Intelligence, Vol. 174, Issues 7-8, pages 479-499, 2010. pdf
  14. T. Denoeux. Extending stochastic ordering to belief functions on the real line. Information Sciences, Vol. 179, pages 1362-1376, 2009. pdf
  15. T. Denoeux. Conjunctive and Disjunctive Combination of Belief Functions Induced by Non Distinct Bodies of Evidence. Artificial Intelligence, Vol. 172, pages 234–264, 2008. pdf
  16. D. Mercier, B. Quost and T. Denoeux. Refined modeling of sensor reliability in the belief function framework using contextual discounting, Information Fusion, Vol. 9, Issue 2, pages 246–258, 2008. pdf
  17. T. Denoeux and A. Ben Yaghlane. Approximating the Combination of Belief Functions using the Fast Moebius Transform in a coarsened frame. International Journal of Approximate Reasoning, Vol. 31, No. 1-2, 77-101, 2002. pdf
  18. T. Denoeux. Inner and outer approximation of belief structures using a hierarchical clustering approach. Int. Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, Vol. 9, No. 4, 437-460, 2001. pdf
  19. T. Denoeux. Modeling vague beliefs using fuzzy-valued belief structures. Fuzzy Sets and Systems, 116(2):167-199, 2000. pdf
  20. T. Denoeux. Reasoning with imprecise belief structures. International Journal of Approximate Reasoning, 20:79-111, 1999. pdf

Evidential classification and regression

  1. Z. Tong, Ph. Xu and T. Denoeux. An evidential classifier based on Dempster-Shafer theory and deep learning. Neurocomputing, Vol. 450, pages 275-293, 2021. pdf
  2. Z.-G. Liu, L.-Q. Huang, K. Zhou, and T. Denoeux. Combination of Transferable Classification with Multisource Domain Adaptation Based on Evidential Reasoning. IEEE Transactions on Neural Networks and Learning Systems, Vol. 32, Issue 5, pages 2015-2029, 2021. pdf
  3. Z.-G. Su, Q. Hu, and T. Denoeux. A Distributed Rough Evidential K-NN Classifier: Integrating Feature Reduction and Classification. IEEE Transactions on Fuzzy Systems, Volume 29, Issue 8, pages 2322-2335, 2021. pdf
  4. Z. Tong, Ph. Xu and T. Denoeux. Evidential fully convolutional network for semantic segmentation. Applied Intelligence, Vol. 51, pages 6376–6399, 2021. pdf
  5. L. Ma and T. Denoeux. Partial Classification in the Belief Function Framework. Knowledge-Based Systems, Vol. 214, 106742, 2021. pdf
  6. T. Denoeux, O. Kanjanatarakul and S. Sriboonchitta. A New Evidential K-Nearest Neighbor Rule based on Contextual Discounting with Partially Supervised learning. International Journal of Approximate Reasoning, Vol. 113, pages 287-302, 2019. pdf
  7. T. Denoeux. Logistic Regression, Neural Networks and Dempster-Shafer Theory: a New Perspective. Knowledge-Based Systems, Vol. 176, Pages 54–67, 2019. pdf
  8. Z.-G. Su, T. Denoeux, Y.-S. Hao and M. Zhao. Evidential K-NN Classification with Enhanced Performance via Optimizing a Class of Parametric Conjunctive t-Rules. Knowledge-Based Systems, Volume 142, 15 February 2018, Pages 7-16. pdf
  9. 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
  10. C. Lian, S. Ruan and T. Denoeux. Dissimilarity metric learning in the belief function framework. IEEE Transactions on Fuzzy Systems, Vol. 24, Issue 6, pp. 1555-1564, 2016. pdf
  11. L. Jiao, T. Denoeux and Q. Pan. A hybrid belief rule-based classification system based on uncertain training data and expert knowledge. IEEE Transactions on Systems, Man and Cybernetics: Systems, Vol. 46, issue 12, pages 1711-1723, 2016. pdf
  12. S. Kanj, F. Abdallah, T. Denoeux and K. Tout. Editing training data for multi-label classification with the k-nearest neighbors rule. Pattern Analysis and Applications, Vol. 19, Issue 1, pp 145-161, 2016. pdf
  13. 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
  14. L. Jiao, Q. Pan, T. Denoeux , Y. Liang and X. Feng. Belief rule-based classification system: extension of FRBCS in belief functions framework. Information Sciences, Vol. 309, Pages 26–49, 2015. pdf
  15. C. Lian, S. Ruan and T. Denoeux. An evidential classifier based on feature selection and two-step classification strategy. Pattern Recognition, Vol. 48, pages 2318-2327, 2015. pdf
  16. B. Quost, M.-H. Masson and T. Denoeux. Classifier fusion in the Dempster-Shafer framework using optimized t-norm based combination rules. International Journal of Approximate Reasoning, vol. 52, Issue 3, pages 353-374, 2011. pdf
  17. E. Côme, L. Oukhellou, T. Denoeux and P. Aknin. Learning from partially supervised data using mixture models and belief functions. Pattern Recognition, Vol. 42, Issue 3, pages 334-348, 2009. pdf
  18. B. Quost, T. Denoeux and M.-H. Masson. Combinaison crédibiliste de classifieurs binaires. Traitement du Signal, Vol. 24, Issue 2, pages 83-101, 2007. pdf
  19. B. Quost, T. Denoeux and M.-H. Masson. Pairwise classifier combination using belief functions. Pattern Recognition Letters, Volume 28, Issue 5 , Pages 644-653, 2007. pdf
  20. T. Denoeux and P. Smets. Classification using Belief Functions: the Relationship between the Case-based and Model-based Approaches, IEEE Transactions on Systems, Man and Cybernetics B , Vol. 36, Issue 6, Pages 1395-1406, 2006. pdf
  21. S. Petit-Renaud and T. Denoeux. Nonparametric regression analysis of uncertain and imprecise data using belief Functions. International Journal of Approximate Reasoning, Vol. 35, No. 1, 1-28, 2004. pdf
  22. J. François, Y. Grandvalet, T. Denoeux and J.-M. Roger. Resample and Combine: An Approach to Improving Uncertainty Representation in Evidential Pattern Classification. Information Fusion, (4):75-85, 2003. postscript
  23. T. Denoeux and L. M. Zouhal. Handling possibilistic labels in pattern classification using evidential reasoning. Fuzzy Sets and Systems, 122(3):47-62, 2001. pdf
  24. 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
  25. 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
  26. T. Denoeux, M. Masson and B. Dubuisson. Advanced pattern recognition techniques for system monitoring and diagnosis: a survey. Journal Européen des Systèmes Automatisés (RAIRO-APII-JESA), 31(9-10):1509-1539, 1998. pdf
  27. T. Denoeux. Application du modèle des Croyances Transférables en Reconnaissance de Formes. Traitement du Signal, 14(5):443-451, 1998. postscript
  28. T. Denoeux. Analysis of evidence-theoretic decision rules for pattern classification. Pattern Recognition, 30(7):1095-1107, 1997. pdf
  29. 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

Evidential clustering

  1. Andrea Campagner, Davide Ciucci and Thierry Denoeux. A General Framework for Evaluating and Comparing Soft Clusterings. Information Sciences (accepted for publication), 2022. pdf
  2. Lianmeng Jiao, Thierry Denoeux, Zhun-ga Liu and Quan Pan. EGMM: an Evidential Version of the Gaussian Mixture Model for Clustering. Applied Soft Computing, Vol. 129, 109619, 2022. pdf
  3. T. Denoeux. NN-EVCLUS: Neural Network-based Evidential Clustering. Information Sciences, Vol. 572, Pages 297-330, 2021. pdf
  4. T. Denoeux. Calibrated model-based evidential clustering using bootstrapping. Information Sciences, Vol. 528, pages 17-45, 2020. pdf
  5. Feng Li, Shoumei Li and Thierry Denoeux. Combining clusterings in the belief function framework. Array, Vol. 6, 100018, 2020. pdf
  6. Z.-G. Su and T. Denoeux. BPEC: Belief-Peaks Evidential Clustering. IEEE Transactions on Fuzzy Systems, Vol. 27, Issue 1, Pages 111-123, 2019. pdf
  7. T. Denoeux, S. Li and S. Sriboonchitta. Evaluating and Comparing Soft Partitions: an Approach Based on Dempster-Shafer Theory. IEEE Transactions on Fuzzy Systems, Vol. 26, Issue 3, pages 1231-1244, 2018. pdf
  8. F. Li, S. Li and T. Denoeux. k-CEVCLUS: Constrained Evidential Clustering of Large Dissimilarity Data. Knowledge-Based Systems, Vol. 142, Pages 29-44, 2018. pdf
  9. T. Denoeux, S. Sriboonchitta and O. Kanjanatarakul. Evidential clustering of large dissimilarity data. Knowledge-Based Systems, vol. 106, pages 179-195, 2016. pdf
  10. T. Denoeux, O. Kanjanatarakul and S. Sriboonchitta. EK-NNclus: a clustering procedure based on the evidential K-nearest neighbor rule. Knowledge-Based Systems, Vol. 88, pages 57–69, 2015. pdf
  11. V. Antoine, B. Quost, M.-H. Masson and T. Denoeux. CEVCLUS: Evidential clustering with instance-level constraints for relational data. Soft Computing, Volume 18, Issue 7, pp 1321-1335, 2014. pdf
  12. V. Antoine, B. Quost, M.-H. Masson and T. Denoeux. CECM: Constrained Evidential C-Means algorithm. Computational Statistics and Data Analysis, Vol. 56, Issue 4, pages 894-914, 2012. pdf
  13. M.-H. Masson and T. Denoeux. Ensemble clustering in the belief functions framework. International Journal of Approximate Reasoning, Vol. 52, issue 1, pages 92-109, 2011. pdf
  14. M.-H. Masson and T. Denoeux. RECM: Relational Evidential c-means algorithm. Pattern Recognition Letters, Vol. 30, pages 1015-1026, 2009. pdf
  15. M.-H. Masson and T. Denoeux. ECM: An evidential version of the fuzzy c-means algorithm. Pattern Recognition, Vol. 41, Issue 4, pages 1384– 1397, 2008. pdf
  16. M.-H. Masson and T. Denoeux. Clustering Interval-valued Data using Belief Functions. Pattern Recognition Letters, Vol. 25, Issue 2, 2004, Pages 163-171. pdf
  17. T. Denoeux and M.-H. Masson. EVCLUS: Evidential Clustering of Proximity Data. IEEE Transactions on Systems, Man and Cybernetics B, Vol. 34, Issue 1, 95-109, 2004. pdf

Statistical inference

  1. T. Denoeux and S. Li. Frequency-Calibrated Belief Functions: Review and New Insights. International Journal of Approximate Reasoning, Vol. 92, Pages 232-254, 2018. pdf
  2. 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. pdf
  3. T. Denoeux. Rejoinder on “Likelihood-based belief function: Justification and some extensions to low-quality data”. International Journal of Approximate Reasoning, Volume 55, Issue 7, pages 1614-1617, 2014. pdf
  4. 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
  5. O. Kanjanatarakul, S. Sriboonchitta and T. Denoeux. Forecasting using belief functions: an application to marketing econometrics. International Journal of Approximate Reasoning, Vol. 55, Issue 5, pages 1113–1128, 2014. pdf
  6. E. Ramasso and T. Denoeux. Making use of partial knowledge about hidden states in HMMs: an approach based on belief functions. IEEE Transactions on Fuzzy Systems, Vol. 22, Issue 2, pages 395-405, 2014. pdf
  7. 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
  8. 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
  9. A. Aregui and T. Denoeux. Constructing Consonant Belief Functions from Sample Data using Confidence Sets of Pignistic Probabilities. International Journal of Approximate Reasoning, vol. 49, Issue 3, pages 575–594, 2008. pdf
  10. T. Denoeux. Constructing Belief Functions from Sample Data Using Multinomial Confidence Regions. International Journal of Approximate Reasoning, Vol. 42, Issue 3, Pages 228-252, 2006. pdf

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