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Evidential Machine Learning

Evidential classification and regression

  1. T. Denoeux. Uncertainty Quantification in Logistic Regression using Random Fuzzy Sets and Belief Functions. International Journal of Approximate Reasoning, Volume 168, 109159, 2024. pdf
  2. Ying Lv, Bofeng Zhangh, Xiaodong Yue, Thierry Denoeux, Shan Yue. Selecting Reliable Instances Based on Evidence Theory for Transfer Learning. Expert Systems with Applications, Volume 250, 123739, 2024. pdf
  3. T. Denoeux. Quantifying Prediction Uncertainty in Regression using Random Fuzzy Sets: the ENNreg model. IEEE Transactions on Fuzzy Systems, Vol. 31, Issue 10, pages 3690-3699, 2023. pdf
  4. 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
  5. 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
  6. 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
  7. Z. Tong, Ph. Xu and T. Denoeux. Evidential fully convolutional network for semantic segmentation. Applied Intelligence, Vol. 51, pages 6376–6399, 2021. pdf
  8. L. Ma and T. Denoeux. Partial Classification in the Belief Function Framework. Knowledge-Based Systems, Vol. 214, 106742, 2021. pdf
  9. 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
  10. T. Denoeux. Logistic Regression, Neural Networks and Dempster-Shafer Theory: a New Perspective. Knowledge-Based Systems, Vol. 176, Pages 54–67, 2019. pdf
  11. 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
  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. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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
  24. 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
  25. 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
  26. 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
  27. 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
  28. 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
  29. 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
  30. T. Denoeux. Application du modèle des Croyances Transférables en Reconnaissance de Formes. Traitement du Signal, 14(5):443-451, 1998. postscript
  31. T. Denoeux. Analysis of evidence-theoretic decision rules for pattern classification. Pattern Recognition, 30(7):1095-1107, 1997. pdf
  32. 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 Distributional Framework for Evaluation, Comparison and Uncertainty Quantification in Soft Clustering. International Journal of Approximate Reasoning, Volume 162, 109008, 2023. pdf
  2. Andrea Campagner, Davide Ciucci and Thierry Denoeux. A General Framework for Evaluating and Comparing Soft Clusterings. Information Sciences, Volume 623, Pages 70-93, 2023. pdf
  3. 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
  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. Feng Li, Shoumei Li and Thierry Denoeux. Combining clusterings in the belief function framework. Array, Vol. 6, 100018, 2020. pdf
  7. 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
  8. 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
  9. 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
  10. T. Denoeux, S. Sriboonchitta and O. Kanjanatarakul. Evidential clustering of large dissimilarity data. Knowledge-Based Systems, vol. 106, pages 179-195, 2016. pdf
  11. 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
  12. 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
  13. 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
  14. 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
  15. M.-H. Masson and T. Denoeux. RECM: Relational Evidential c-means algorithm. Pattern Recognition Letters, Vol. 30, pages 1015-1026, 2009. pdf
  16. 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
  17. 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
  18. 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

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en/publi/evml_art.txt · Last modified: 2024/05/10 05:10 by tdenoeux
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