UMR CNRS 7253

Thierry Denoeux
Thierry Denoeux
Thierry Denoeux
Thierry Denoeux
Thierry Denoeux

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Belief Functions and Pattern recognition

  1. 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
  2. T. Denoeux. Logistic Regression, Neural Networks and Dempster-Shafer Theory: a New Perspective. Knowledge-Based Systems, Vol. 176, Pages 54–67, 2019. pdf
  3. T. Denoeux. Decision-Making with Belief Functions: a Review. International Journal of Approximate Reasoning, Vol. 109, Pages 87-110, 2019. pdf
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  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. T. Denoeux, S. Sriboonchitta and O. Kanjanatarakul. Evidential clustering of large dissimilarity data. Knowledge-Based Systems, vol. 106, pages 179-195, 2016. pdf
  11. 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
  12. 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
  13. 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
  14. 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
  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. 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
  17. 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
  18. 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
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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
  24. 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
  25. 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
  26. 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
  27. 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
  28. 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
  29. 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
  30. 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
  31. 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
  32. 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
  33. 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
  34. 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
  35. 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
  36. M.-H. Masson and T. Denoeux. RECM: Relational Evidential c-means algorithm. Pattern Recognition Letters, Vol. 30, pages 1015-1026, 2009. pdf
  37. T. Denoeux. Extending stochastic ordering to belief functions on the real line. Information Sciences, Vol. 179, pages 1362-1376, 2009. pdf
  38. 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
  39. 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
  40. 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
  41. 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
  42. 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
  43. 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
  44. 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
  45. 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
  46. 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
  47. 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
  48. 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
  49. 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. postscript
  50. 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
  51. 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
  52. 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
  53. 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
  54. T. Denoeux. Modeling vague beliefs using fuzzy-valued belief structures. Fuzzy Sets and Systems, 116(2):167-199, 2000. pdf
  55. 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
  56. T. Denoeux. Reasoning with imprecise belief structures. International Journal of Approximate Reasoning, 20:79-111, 1999. pdf
  57. 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
  58. 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
  59. T. Denoeux. Application du modèle des Croyances Transférables en Reconnaissance de Formes. Traitement du Signal, 14(5):443-451, 1998. postscript
  60. T. Denoeux. Analysis of evidence-theoretic decision rules for pattern classification. Pattern Recognition, 30(7):1095-1107, 1997. pdf
  61. 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

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