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- | ====== Belief Functions and Pattern recognition | + | ====== Belief Functions and Machine Learning |
- | - T. denoeux. Belief functions induced by random fuzzy sets: A general framework for representing uncertain and fuzzy evidence. Fuzzy Sets and Systems | + | |
+ | === Theory of belief functions | ||
+ | - T. Denoeux. Parametric families of continuous belief functions based on generalized Gaussian random fuzzy numbers. Fuzzy Sets and Systems, Volume 471, 108679, 2023. {{ : | ||
+ | - 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. {{ : | ||
+ | - 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. {{ : | ||
+ | - T. Denoeux. Belief functions induced by random fuzzy sets: A general framework for representing uncertain and fuzzy evidence. Fuzzy Sets and Systems, | ||
- T. Denoeux. Distributed combination of belief functions. Information Fusion, Vol. 65, pages 179-191, 2021. {{ : | - T. Denoeux. Distributed combination of belief functions. Information Fusion, Vol. 65, pages 179-191, 2021. {{ : | ||
- 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. {{ : | - 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. {{ : | ||
- | - 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 (to appear), 2020.{{ : | ||
- | - 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 (to appear), 2020. {{ : | ||
- | - T. Denoeux. Calibrated model-based evidential clustering using bootstrapping. Information Sciences, Vol. 528, pages 17-45, 2020. {{ : | ||
- | - Feng Li, Shoumei Li and Thierry Denoeux. Combining clusterings in the belief function framework. Array, Vol. 6, 100018, 2020. {{ : | ||
- | - T. Denoeux, O. Kanjanatarakul and S. Sriboonchitta. A New Evidential K-Nearest Neighbor | ||
- | - T. Denoeux. Logistic Regression, Neural Networks and Dempster-Shafer Theory: a New Perspective. Knowledge-Based Systems, Vol. 176, Pages 54–67, 2019. {{ : | ||
- T. Denoeux. Decision-Making with Belief Functions: a Review. International Journal of Approximate Reasoning, Vol. 109, Pages 87-110, 2019. {{ : | - T. Denoeux. Decision-Making with Belief Functions: a Review. International Journal of Approximate Reasoning, Vol. 109, Pages 87-110, 2019. {{ : | ||
- | - Z.-G. Su and T. Denoeux. BPEC: Belief-Peaks Evidential Clustering. IEEE Transactions on Fuzzy Systems, Vol. 27, Issue 1, Pages 111-123, 2019. {{: | ||
- | - 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. {{: | ||
- | - 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. {{: | ||
- | - 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. {{: | ||
- | - T. Denoeux and S. Li. Frequency-Calibrated Belief Functions: Review and New Insights. International Journal of Approximate Reasoning, Vol. 92, Pages 232-254, 2018. {{: | ||
- | - 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, | ||
- | - T. Denoeux, S. Sriboonchitta and O. Kanjanatarakul. Evidential clustering of large dissimilarity data. Knowledge-Based Systems, vol. 106, pages 179-195, | ||
- | - 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. {{: | ||
- | - 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: | ||
- | - O. Kanjanatarakul, | ||
- | - 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. {{: | ||
- | - 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, | ||
- 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. {{: | - 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. {{: | ||
- | - 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. {{: | ||
- | - 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. {{: | ||
- | - C. Lian, S. Ruan and T. Denoeux. An evidential classifier based on feature selection and two-step classification strategy. Pattern Recognition, | ||
- T. Denoeux, N. El Zoghby, V. Cherfaoui and A. Jouglet. Optimal object association in the Dempster-Shafer framework. IEEE Transactions on Cybernetics, | - T. Denoeux, N. El Zoghby, V. Cherfaoui and A. Jouglet. Optimal object association in the Dempster-Shafer framework. IEEE Transactions on Cybernetics, | ||
- | - T. Denoeux. Rejoinder on " | ||
- | - 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, | ||
- | - 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. {{en: | ||
- | - O. Kanjanatarakul, | ||
- | - 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. {{en: | ||
- | - 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. {{en: | ||
- | - T. Denoeux. Maximum likelihood estimation from Uncertain Data in the Belief Function Framework. IEEE Transactions on Knowledge and Data Engineering, | ||
- T. Denoeux and M.-H. Masson. Evidential reasoning in large partially ordered sets. Application to multi-label classification, | - T. Denoeux and M.-H. Masson. Evidential reasoning in large partially ordered sets. Application to multi-label classification, | ||
- | - 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. {{en: | ||
- 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. {{en: | - 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. {{en: | ||
- | - 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. {{en: | ||
- | - 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. {{en: | ||
- G. Nassreddine, | - G. Nassreddine, | ||
- F. Pichon and T. Denoeux. The unnormalized Dempster' | - F. Pichon and T. Denoeux. The unnormalized Dempster' | ||
- T. Denoeux, Z. Younes and F. Abdallah. Representing uncertainty on set-valued variables using belief functions. Artificial Intelligence, | - T. Denoeux, Z. Younes and F. Abdallah. Representing uncertainty on set-valued variables using belief functions. Artificial Intelligence, | ||
- | - M.-H. Masson and T. Denoeux. RECM: Relational Evidential c-means algorithm. Pattern Recognition Letters, Vol. 30, pages 1015-1026, 2009. {{en: | ||
- T. Denoeux. Extending stochastic ordering to belief functions on the real line. Information Sciences, Vol. 179, pages 1362-1376, 2009. {{en: | - T. Denoeux. Extending stochastic ordering to belief functions on the real line. Information Sciences, Vol. 179, pages 1362-1376, 2009. {{en: | ||
- | - E. Côme, L. Oukhellou, T. Denoeux and P. Aknin. Learning from partially supervised data using mixture models and belief functions. Pattern Recognition, | ||
- | - 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. {{en: | ||
- | - M.-H. Masson and T. Denoeux. ECM: An evidential version of the fuzzy c-means algorithm. Pattern Recognition, | ||
- T. Denoeux. Conjunctive and Disjunctive Combination of Belief Functions Induced by Non Distinct Bodies of Evidence. Artificial Intelligence, | - T. Denoeux. Conjunctive and Disjunctive Combination of Belief Functions Induced by Non Distinct Bodies of Evidence. Artificial Intelligence, | ||
- D. Mercier, B. Quost and T. Denoeux. Refined modeling of sensor reliability in the belief function framework using contextual discounting, | - D. Mercier, B. Quost and T. Denoeux. Refined modeling of sensor reliability in the belief function framework using contextual discounting, | ||
+ | - 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. {{en: | ||
+ | - T. Denoeux. Inner and outer approximation of belief structures using a hierarchical clustering approach. Int. Journal of Uncertainty, | ||
+ | - T. Denoeux. Modeling vague beliefs using fuzzy-valued belief structures. Fuzzy Sets and Systems, 116(2): | ||
+ | - T. Denoeux. Reasoning with imprecise belief structures. International Journal of Approximate Reasoning, 20:79-111, 1999. {{: | ||
+ | |||
+ | |||
+ | === Evidential classification and regression === | ||
+ | |||
+ | - T. Denoeux. Uncertainty Quantification in Logistic Regression using Random Fuzzy Sets and Belief Functions. International Journal of Approximate Reasoning, Volume 168, 109159, 2024. {{ : | ||
+ | - 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. {{ : | ||
+ | - Z. Tong, Ph. Xu and T. Denoeux. An evidential classifier based on Dempster-Shafer theory and deep learning. Neurocomputing, | ||
+ | - 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. {{ : | ||
+ | - 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. {{ : | ||
+ | - Z. Tong, Ph. Xu and T. Denoeux. Evidential fully convolutional network for semantic segmentation. Applied Intelligence, | ||
+ | - L. Ma and T. Denoeux. Partial Classification in the Belief Function Framework. Knowledge-Based Systems, Vol. 214, 106742, 2021. {{ : | ||
+ | - T. Denoeux, O. Kanjanatarakul and S. Sriboonchitta. A New Evidential K-Nearest Neighbor | ||
+ | - T. Denoeux. Logistic Regression, Neural Networks and Dempster-Shafer Theory: a New Perspective. Knowledge-Based Systems, Vol. 176, Pages 54–67, 2019. {{ : | ||
+ | - 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. {{: | ||
+ | - 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, | ||
+ | - 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. {{: | ||
+ | - 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: | ||
+ | - 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, | ||
+ | - 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. {{: | ||
+ | - 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. {{: | ||
+ | - C. Lian, S. Ruan and T. Denoeux. An evidential classifier based on feature selection and two-step classification strategy. Pattern Recognition, | ||
+ | - 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. {{en: | ||
+ | - E. Côme, L. Oukhellou, T. Denoeux and P. Aknin. Learning from partially supervised data using mixture models and belief functions. Pattern Recognition, | ||
- B. Quost, T. Denoeux and M.-H. Masson. Combinaison crédibiliste de classifieurs binaires. Traitement du Signal, Vol. 24, Issue 2, pages 83-101, 2007. {{en: | - B. Quost, T. Denoeux and M.-H. Masson. Combinaison crédibiliste de classifieurs binaires. Traitement du Signal, Vol. 24, Issue 2, pages 83-101, 2007. {{en: | ||
- 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. {{en: | - 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. {{en: | ||
- 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. {{en: | - 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. {{en: | ||
- | - 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. {{en: | ||
- | - M.-H. Masson and T. Denoeux. Clustering Interval-valued Data using Belief Functions. Pattern Recognition Letters, Vol. 25, Issue 2, 2004, Pages 163-171. {{: | ||
- | - 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. {{en: | ||
- 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. {{ : | - 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. {{ : | ||
- 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. {{en: | - 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. {{en: | ||
- | - 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. {{en: | ||
- | - T. Denoeux. Inner and outer approximation of belief structures using a hierarchical clustering approach. Int. Journal of Uncertainty, | ||
- T. Denoeux and L. M. Zouhal. Handling possibilistic labels in pattern classification using evidential reasoning. Fuzzy Sets and Systems, 122(3): | - T. Denoeux and L. M. Zouhal. Handling possibilistic labels in pattern classification using evidential reasoning. Fuzzy Sets and Systems, 122(3): | ||
- | | + | - T. Denoeux. A neural network classifier based on Dempster-Shafer theory. IEEE Transactions |
- | | + | |
- | - T. Denoeux. Reasoning with imprecise belief structures. International Journal of Approximate Reasoning, 20:79-111, 1999. {{: | + | |
- 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): | - 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): | ||
- 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), | - 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), | ||
Line 68: | Line 58: | ||
- T. Denoeux. Analysis of evidence-theoretic decision rules for pattern classification. Pattern Recognition, | - T. Denoeux. Analysis of evidence-theoretic decision rules for pattern classification. Pattern Recognition, | ||
- T. Denoeux. A k-nearest neighbor classification rule based on Dempster-Shafer theory. IEEE Transactions on Systems, Man and Cybernetics, | - T. Denoeux. A k-nearest neighbor classification rule based on Dempster-Shafer theory. IEEE Transactions on Systems, Man and Cybernetics, | ||
+ | |||
+ | === Evidential clustering === | ||
+ | - 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. {{ : | ||
+ | - Andrea Campagner, Davide Ciucci and Thierry Denoeux. A General Framework for Evaluating and Comparing Soft Clusterings. Information Sciences, Volume 623, Pages 70-93, 2023. {{ : | ||
+ | - 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. {{ : | ||
+ | - T. Denoeux. NN-EVCLUS: Neural Network-based Evidential Clustering. Information Sciences, Vol. 572, Pages 297-330, 2021. {{ : | ||
+ | - T. Denoeux. Calibrated model-based evidential clustering using bootstrapping. Information Sciences, Vol. 528, pages 17-45, 2020. {{ : | ||
+ | - Feng Li, Shoumei Li and Thierry Denoeux. Combining clusterings in the belief function framework. Array, Vol. 6, 100018, 2020. {{ : | ||
+ | - Z.-G. Su and T. Denoeux. BPEC: Belief-Peaks Evidential Clustering. IEEE Transactions on Fuzzy Systems, Vol. 27, Issue 1, Pages 111-123, 2019. {{: | ||
+ | - 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. {{: | ||
+ | - 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. {{: | ||
+ | - T. Denoeux, S. Sriboonchitta and O. Kanjanatarakul. Evidential clustering of large dissimilarity data. Knowledge-Based Systems, vol. 106, pages 179-195, | ||
+ | - 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. {{: | ||
+ | - 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. {{en: | ||
+ | - 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. {{en: | ||
+ | - 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. {{en: | ||
+ | - M.-H. Masson and T. Denoeux. RECM: Relational Evidential c-means algorithm. Pattern Recognition Letters, Vol. 30, pages 1015-1026, 2009. {{en: | ||
+ | - M.-H. Masson and T. Denoeux. ECM: An evidential version of the fuzzy c-means algorithm. Pattern Recognition, | ||
+ | - M.-H. Masson and T. Denoeux. Clustering Interval-valued Data using Belief Functions. Pattern Recognition Letters, Vol. 25, Issue 2, 2004, Pages 163-171. {{: | ||
+ | - 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. {{en: | ||
+ | |||
+ | |||
+ | === Statistical inference === | ||
+ | - T. Denoeux and S. Li. Frequency-Calibrated Belief Functions: Review and New Insights. International Journal of Approximate Reasoning, Vol. 92, Pages 232-254, 2018. {{: | ||
+ | - O. Kanjanatarakul, | ||
+ | - T. Denoeux. Rejoinder on " | ||
+ | - 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, | ||
+ | - O. Kanjanatarakul, | ||
+ | - 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. {{en: | ||
+ | - 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. {{en: | ||
+ | - T. Denoeux. Maximum likelihood estimation from Uncertain Data in the Belief Function Framework. IEEE Transactions on Knowledge and Data Engineering, | ||
+ | - 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. {{en: | ||
+ | - 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. {{en: | ||
+ | |||
+ |