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ORCIDThierry Denoeux
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- affiliation: Université de technologie de Compiègne, France
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2020 – today
- 2025
[j127]Ling Huang
, Su Ruan, Pierre Decazes, Thierry Denoeux:
Deep evidential fusion with uncertainty quantification and reliability learning for multimodal medical image segmentation. Inf. Fusion 113: 102648 (2025)- 2024
- [j126]Ying Lv, Bofeng Zhang, Xiaodong Yue, Thierry Denoeux, Shan Yue:
Selecting reliable instances based on evidence theory for transfer learning. Expert Syst. Appl. 250: 123739 (2024) - [j125]Thierry Denoeux:
Uncertainty quantification in logistic regression using random fuzzy sets and belief functions. Int. J. Approx. Reason. 168: 109159 (2024) - [j124]Ismaïl Baaj, Zied Bouraoui, Antoine Cornuéjols, Thierry Denoeux, Sébastien Destercke, Didier Dubois, Marie-Jeanne Lesot, João Marques-Silva, Jérôme Mengin, Henri Prade, Steven Schockaert, Mathieu Serrurier, Olivier Strauss, Christel Vrain:
Synergies between machine learning and reasoning - An introduction by the Kay R. Amel group. Int. J. Approx. Reason. 171: 109206 (2024) - [j123]Thierry Denoeux:
Combination of dependent and partially reliable Gaussian random fuzzy numbers. Inf. Sci. 681: 121208 (2024) - [c108]Thierry Denoeux:
Uncertainty Quantification in Regression Neural Networks Using Likelihood-Based Belief Functions. BELIEF 2024: 40-48 - [c107]Ling Huang, Yucheng Xing, Thierry Denoeux, Mengling Feng:
An Evidential Time-to-Event Prediction Model Based on Gaussian Random Fuzzy Numbers. BELIEF 2024: 49-57 - [c106]Thierry Denoeux:
Combination of Dependent Gaussian Random Fuzzy Numbers. BELIEF 2024: 264-272 - [e11]Yaxin Bi, Anne-Laure Jousselme, Thierry Denoeux:
Belief Functions: Theory and Applications - 8th International Conference, BELIEF 2024, Belfast, UK, September 2-4, 2024, Proceedings. Lecture Notes in Computer Science 14909, Springer 2024, ISBN 978-3-031-67976-6 [contents] - [i23]Ling Huang, Yucheng Xing, Thierry Denoeux, Mengling Feng:
An evidential time-to-event prediction model based on Gaussian random fuzzy numbers. CoRR abs/2406.13487 (2024) - 2023
- [j122]Thierry Denoeux:
Reasoning with fuzzy and uncertain evidence using epistemic random fuzzy sets: General framework and practical models. Fuzzy Sets Syst. 453: 1-36 (2023) - [j121]Thierry Denoeux:
Parametric families of continuous belief functions based on generalized Gaussian random fuzzy numbers. Fuzzy Sets Syst. 471: 108679 (2023) - [j120]Andrea Campagner, Davide Ciucci, Thierry Denoeux:
A distributional framework for evaluation, comparison and uncertainty quantification in soft clustering. Int. J. Approx. Reason. 162: 109008 (2023) - [j119]Ling Huang, Su Ruan, Thierry Denoeux:
Semi-supervised multiple evidence fusion for brain tumor segmentation. Neurocomputing 535: 40-52 (2023) - [j118]Ling Huang, Su Ruan, Thierry Denoeux:
Application of belief functions to medical image segmentation: A review. Inf. Fusion 91: 737-756 (2023) - [j117]Andrea Campagner, Davide Ciucci, Thierry Denoeux:
A general framework for evaluating and comparing soft clusterings. Inf. Sci. 623: 70-93 (2023) - [j116]Thierry Denoeux:
Quantifying Prediction Uncertainty in Regression Using Random Fuzzy Sets: The ENNreg Model. IEEE Trans. Fuzzy Syst. 31(10): 3690-3699 (2023) - [c105]Thierry Denoeux:
Belief Functions on the Real Line Defined by Transformed Gaussian Random Fuzzy Numbers. FUZZ 2023: 1-6 - [i22]Ling Huang, Su Ruan, Pierre Decazes, Thierry Denoeux:
Deep evidential fusion with uncertainty quantification and contextual discounting for multimodal medical image segmentation. CoRR abs/2309.05919 (2023) - 2022
- [j115]Lianmeng Jiao, Thierry Denoeux, Zhun-ga Liu, Quan Pan:
EGMM: An evidential version of the Gaussian mixture model for clustering. Appl. Soft Comput. 129: 109619 (2022) - [j114]John C. Aldrich, A. Philip Dawid, Thierry Denoeux, Prakash P. Shenoy, Vladimir Vovk:
Probability and statistics: Foundations and history. Special Issue in honor of Glenn Shafer. Int. J. Approx. Reason. 141: 1-4 (2022) - [j113]John C. Aldrich, A. Philip Dawid, Thierry Denoeux, Prakash P. Shenoy, Vladimir Vovk:
Glenn Shafer - A short biography. Int. J. Approx. Reason. 141: 5-10 (2022) - [j112]Andrea Campagner, Davide Ciucci, Thierry Denoeux:
Belief functions and rough sets: Survey and new insights. Int. J. Approx. Reason. 143: 192-215 (2022) - [j111]Ling Huang, Su Ruan, Pierre Decazes, Thierry Denoeux:
Lymphoma segmentation from 3D PET-CT images using a deep evidential network. Int. J. Approx. Reason. 149: 39-60 (2022) - [c104]Wei Liu, Xiaodong Yue, Yufei Chen, Thierry Denoeux:
Trusted Multi-View Deep Learning with Opinion Aggregation. AAAI 2022: 7585-7593 - [c103]Andrea Campagner, Davide Ciucci, Thierry Denoeux:
A Distributional Approach for Soft Clustering Comparison and Evaluation. BELIEF 2022: 3-12 - [c102]Thierry Denoeux:
An Evidential Neural Network Model for Regression Based on Random Fuzzy Numbers. BELIEF 2022: 57-66 - [c101]Haijie Fu, Xiaodong Yue, Wei Liu, Thierry Denoeux:
Stable Clustering Ensemble Based on Evidence Theory. ICIP 2022: 2046-2050 - [c100]Ling Huang, Thierry Denoeux, Pierre Vera, Su Ruan:
Evidence Fusion with Contextual Discounting for Multi-modality Medical Image Segmentation. MICCAI (5) 2022: 401-411 - [i21]Ling Huang, Su Ruan, Pierre Decazes, Thierry Denoeux:
Lymphoma segmentation from 3D PET-CT images using a deep evidential network. CoRR abs/2201.13078 (2022) - [i20]Thierry Denoeux:
Reasoning with fuzzy and uncertain evidence using epistemic random fuzzy sets: general framework and practical models. CoRR abs/2202.08081 (2022) - [i19]Andrea Campagner, Davide Ciucci, Thierry Denoeux:
A Distributional Approach for Soft Clustering Comparison and Evaluation. CoRR abs/2206.09827 (2022) - [i18]Ling Huang, Thierry Denoeux, Pierre Vera, Su Ruan:
Evidence fusion with contextual discounting for multi-modality medical image segmentation. CoRR abs/2206.11739 (2022) - [i17]Thierry Denoeux:
An Evidential Neural Network Model for Regression Based on Random Fuzzy Numbers. CoRR abs/2208.00647 (2022) - 2021
- [j110]Zheng Tong, Philippe Xu, Thierry Denoeux:
Evidential fully convolutional network for semantic segmentation. Appl. Intell. 51(9): 6376-6399 (2021) - [j109]Thierry Denoeux:
Belief functions induced by random fuzzy sets: A general framework for representing uncertain and fuzzy evidence. Fuzzy Sets Syst. 424: 63-91 (2021) - [j108]Jianxu Liu, Songsak Sriboonchitta, Aree Wiboonpongse, Thierry Denoeux:
A trivariate Gaussian copula stochastic frontier model with sample selection. Int. J. Approx. Reason. 137: 181-198 (2021) - [j107]Zheng Tong, Philippe Xu, Thierry Denoeux:
An evidential classifier based on Dempster-Shafer theory and deep learning. Neurocomputing 450: 275-293 (2021) - [j106]Thierry Denoeux:
Distributed combination of belief functions. Inf. Fusion 65: 179-191 (2021) - [j105]Thierry Denoeux:
NN-EVCLUS: Neural network-based evidential clustering. Inf. Sci. 572: 297-330 (2021) - [j104]Liyao Ma, Thierry Denoeux:
Partial classification in the belief function framework. Knowl. Based Syst. 214: 106742 (2021) - [j103]Minh-Quyet Ha, Duong-Nguyen Nguyen, Viet Cuong Nguyen, Takahiro Nagata, Toyohiro Chikyow, Hiori Kino, Takashi Miyake, Thierry Denoeux, Van-Nam Huynh, Hieu-Chi Dam:
Evidence-based recommender system for high-entropy alloys. Nat. Comput. Sci. 1(7): 470-478 (2021) - [j102]Zhi-gang Su, Qinghua Hu, Thierry Denoeux:
A Distributed Rough Evidential K-NN Classifier: Integrating Feature Reduction and Classification. IEEE Trans. Fuzzy Syst. 29(8): 2322-2335 (2021) - [j101]Zhunga Liu, Linqing Huang, Kuang Zhou, Thierry Denoeux:
Combination of Transferable Classification With Multisource Domain Adaptation Based on Evidential Reasoning. IEEE Trans. Neural Networks Learn. Syst. 32(5): 2015-2029 (2021) - [c99]Ling Huang, Su Ruan, Pierre Decazes, Thierry Denoeux:
Evidential Segmentation of 3D PET/CT Images. BELIEF 2021: 159-167 - [c98]Zheng Tong, Philippe Xu, Thierry Denoeux:
Fusion of Evidential CNN Classifiers for Image Classification. BELIEF 2021: 168-176 - [c97]Ling Huang, Su Ruan, Thierry Denoeux:
Covid-19 Classification with Deep Neural Network and Belief Functions. BIBE 2021: 3:1-3:4 - [c96]Xiaoqian Zhou, Xiaodong Yue, Zhikang Xu, Thierry Denoeux, Yufei Chen:
Deep Neural Networks with Prior Evidence for Bladder Cancer Staging. BIBM 2021: 1221-1226 - [c95]Ling Huang, Su Ruan, Thierry Denoeux:
Belief Function-Based Semi-Supervised Learning For Brain Tumor Segmentation. ISBI 2021: 160-164 - [c94]Ling Huang, Thierry Denoeux, David Tonnelet, Pierre Decazes, Su Ruan:
Deep PET/CT Fusion with Dempster-Shafer Theory for Lymphoma Segmentation. MLMI@MICCAI 2021: 30-39 - [e10]Thierry Denoeux, Eric Lefèvre, Zhunga Liu, Frédéric Pichon:
Belief Functions: Theory and Applications - 6th International Conference, BELIEF 2021, Shanghai, China, October 15-19, 2021, Proceedings. Lecture Notes in Computer Science 12915, Springer 2021, ISBN 978-3-030-88600-4 [contents] - [i16]Ling Huang, Su Ruan, Thierry Denoeux:
Covid-19 classification with deep neural network and belief functions. CoRR abs/2101.06958 (2021) - [i15]Ling Huang, Su Ruan, Thierry Denoeux:
Belief function-based semi-supervised learning for brain tumor segmentation. CoRR abs/2102.00097 (2021) - [i14]Zheng Tong, Philippe Xu, Thierry Denoeux:
Evidential fully convolutional network for semantic segmentation. CoRR abs/2103.13544 (2021) - [i13]Zheng Tong, Philippe Xu, Thierry Denoeux:
An evidential classifier based on Dempster-Shafer theory and deep learning. CoRR abs/2103.13549 (2021) - [i12]Ling Huang, Su Ruan, Pierre Decazes, Thierry Denoeux:
Evidential segmentation of 3D PET/CT images. CoRR abs/2104.13293 (2021) - [i11]Ling Huang, Thierry Denoeux, David Tonnelet, Pierre Decazes, Su Ruan:
Deep PET/CT fusion with Dempster-Shafer theory for lymphoma segmentation. CoRR abs/2108.05422 (2021) - [i10]Zheng Tong, Philippe Xu, Thierry Denoeux:
Fusion of evidential CNN classifiers for image classification. CoRR abs/2108.10233 (2021) - [i9]Emmanuel Ramasso, Thierry Denoeux, Gaël Chevallier:
Clustering acoustic emission data streams with sequentially appearing clusters using mixture models. CoRR abs/2108.11211 (2021) - 2020
- [j100]Feng Li, Shoumei Li, Thierry Denoeux:
Combining clusterings in the belief function framework. Array 6: 100018 (2020) - [j99]Thierry Denoeux, Prakash P. Shenoy:
An interval-valued utility theory for decision making with Dempster-Shafer belief functions. Int. J. Approx. Reason. 124: 194-216 (2020) - [j98]Thierry Denoeux:
Calibrated model-based evidential clustering using bootstrapping. Inf. Sci. 528: 17-45 (2020) - [c93]Bin Yuan, Xiaodong Yue, Ying Lv, Thierry Denoeux:
Evidential Deep Neural Networks for Uncertain Data Classification. KSEM (2) 2020: 427-437 - [p7]Thierry Denoeux, Didier Dubois, Henri Prade:
Representations of Uncertainty in Artificial Intelligence: Probability and Possibility. A Guided Tour of Artificial Intelligence Research (1) (I) 2020: 69-117 - [p6]Thierry Denoeux, Didier Dubois, Henri Prade:
Representations of Uncertainty in AI: Beyond Probability and Possibility. A Guided Tour of Artificial Intelligence Research (1) (I) 2020: 119-150 - [i8]Thierry Denoeux:
Belief functions induced by random fuzzy sets: Application to statistical inference. CoRR abs/2004.11638 (2020) - [i7]Thierry Denoeux:
NN-EVCLUS: Neural Network-based Evidential Clustering. CoRR abs/2009.12795 (2020) - [i6]Lianmeng Jiao, Thierry Denoeux, Zhun-ga Liu, Quan Pan:
EGMM: an Evidential Version of the Gaussian Mixture Model for Clustering. CoRR abs/2010.01333 (2020)
2010 – 2019
- 2019
- [j97]Thierry Denoeux:
Editorial: Opening up computer science. Array 1-2: 100005 (2019) - [j96]Thierry Denoeux:
Decision-making with belief functions: A review. Int. J. Approx. Reason. 109: 87-110 (2019) - [j95]Thierry Denoeux, Orakanya Kanjanatarakul, Songsak Sriboonchitta:
A new evidential K-nearest neighbor rule based on contextual discounting with partially supervised learning. Int. J. Approx. Reason. 113: 287-302 (2019) - [j94]Thierry Denoeux:
Logistic regression, neural networks and Dempster-Shafer theory: A new perspective. Knowl. Based Syst. 176: 54-67 (2019) - [j93]Zhi-gang Su, Thierry Denoeux:
BPEC: Belief-Peaks Evidential Clustering. IEEE Trans. Fuzzy Syst. 27(1): 111-123 (2019) - [j92]Chunfeng Lian, Su Ruan, Thierry Denoeux, Hua Li, Pierre Vera:
Joint Tumor Segmentation in PET-CT Images Using Co-Clustering and Fusion Based on Belief Functions. IEEE Trans. Image Process. 28(2): 755-766 (2019) - [c92]Thierry Denoeux, Orakanya Kanjanatarakul:
Multistep Prediction using Point-Cloud Approximation of Continuous Belief Functions. FUZZ-IEEE 2019: 1-6 - [c91]Yixuan Qiao, Shoumei Li, Thierry Denoeux:
Collaborative Evidential Clustering. IFSA/NAFIPS 2019: 518-530 - [c90]Thierry Denoeux, Prakash P. Shenoy:
An Axiomatic Utility Theory for Dempster-Shafer Belief Functions. ISIPTA 2019: 145-155 - [c89]Liyao Ma, Thierry Denoeux:
Making Set-Valued Predictions in Evidential Classification: A Comparison of Different Approaches. ISIPTA 2019: 276-285 - [c88]Zheng Tong, Philippe Xu, Thierry Denoeux:
ConvNet and Dempster-Shafer Theory for Object Recognition. SUM 2019: 368-381 - [p5]Frédéric Pichon, Didier Dubois, Thierry Denoeux:
Quality of Information Sources in Information Fusion. Information Quality in Information Fusion and Decision Making 2019: 31-49 - [e9]Sébastien Destercke, Thierry Denoeux, María Angeles Gil, Przemyslaw Grzegorzewski, Olgierd Hryniewicz:
Uncertainty Modelling in Data Science, SMPS 2018, Compiègne, France, 17-21 September 2018. Advances in Intelligent Systems and Computing 832, Springer 2019, ISBN 978-3-319-97546-7 [contents] - [i5]Thierry Denoeux:
Calibrated model-based evidential clustering using bootstrapping. CoRR abs/1912.06137 (2019) - [i4]Thierry Denoeux, Prakash P. Shenoy:
An Interval-Valued Utility Theory for Decision Making with Dempster-Shafer Belief Functions. CoRR abs/1912.06594 (2019) - [i3]Zied Bouraoui, Antoine Cornuéjols, Thierry Denoeux, Sébastien Destercke, Didier Dubois, Romain Guillaume, João Marques-Silva, Jérôme Mengin, Henri Prade, Steven Schockaert, Mathieu Serrurier, Christel Vrain:
From Shallow to Deep Interactions Between Knowledge Representation, Reasoning and Machine Learning (Kay R. Amel group). CoRR abs/1912.06612 (2019) - 2018
- [j91]Thierry Denoeux, Shoumei Li:
Frequency-calibrated belief functions: Review and new insights. Int. J. Approx. Reason. 92: 232-254 (2018) - [j90]Liqi Sui, Pierre Feissel, Thierry Denoeux:
Identification of elastic properties in the belief function framework. Int. J. Approx. Reason. 101: 69-87 (2018) - [j89]Zhi-gang Su, Thierry Denoeux, Yong-sheng Hao, Ming Zhao:
Evidential K-NN classification with enhanced performance via optimizing a class of parametric conjunctive t-rules. Knowl. Based Syst. 142: 7-16 (2018) - [j88]Feng Li, Shoumei Li, Thierry Denoeux:
k-CEVCLUS: Constrained evidential clustering of large dissimilarity data. Knowl. Based Syst. 142: 29-44 (2018) - [j87]Chunfeng Lian, Su Ruan, Thierry Denoeux, Hua Li, Pierre Vera:
Spatial Evidential Clustering With Adaptive Distance Metric for Tumor Segmentation in FDG-PET Images. IEEE Trans. Biomed. Eng. 65(1): 21-30 (2018) - [j86]Thierry Denoeux, Shoumei Li, Songsak Sriboonchitta:
Evaluating and Comparing Soft Partitions: An Approach Based on Dempster-Shafer Theory. IEEE Trans. Fuzzy Syst. 26(3): 1231-1244 (2018) - [c87]Thierry Denoeux:
Logistic Regression Revisited: Belief Function Analysis. BELIEF 2018: 57-64 - [c86]Orakanya Kanjanatarakul, Siwarat Kuson, Thierry Denoeux:
An Evidential K-Nearest Neighbor Classifier Based on Contextual Discounting and Likelihood Maximization. BELIEF 2018: 155-162 - [p4]Thierry Denoeux:
Quantifying Predictive Uncertainty Using Belief Functions: Different Approaches and Practical Construction. Predictive Econometrics and Big Data 2018: 157-176 - [e8]Sébastien Destercke, Thierry Denoeux, Fabio Cuzzolin, Arnaud Martin:
Belief Functions: Theory and Applications - 5th International Conference, BELIEF 2018, Compiègne, France, September 17-21, 2018, Proceedings. Lecture Notes in Computer Science 11069, Springer 2018, ISBN 978-3-319-99382-9 [contents] - [e7]Van-Nam Huynh, Masahiro Inuiguchi, Dang Hung Tran, Thierry Denoeux:
Integrated Uncertainty in Knowledge Modelling and Decision Making - 6th International Symposium, IUKM 2018, Hanoi, Vietnam, March 15-17, 2018, Proceedings. Lecture Notes in Computer Science 10758, Springer 2018, ISBN 978-3-319-75428-4 [contents] - [i2]Thierry Denoeux:
Logistic Regression, Neural Networks and Dempster-Shafer Theory: a New Perspective. CoRR abs/1807.01846 (2018) - [i1]Thierry Denoeux:
Decision-Making with Belief Functions: a Review. CoRR abs/1808.05322 (2018) - 2017
- [j85]Benjamin Quost, Thierry Denoeux, Shoumei Li:
Parametric classification with soft labels using the evidential EM algorithm: linear discriminant analysis versus logistic regression. Adv. Data Anal. Classif. 11(4): 659-690 (2017) - [j84]Jean-Baptiste Bordes, Franck Davoine, Philippe Xu, Thierry Denoeux:
Evidential grammars: A compositional approach for scene understanding. Application to multimodal street data. Appl. Soft Comput. 61: 1173-1185 (2017) - [j83]Songsak Sriboonchitta, Jianxu Liu, Aree Wiboonpongse, Thierry Denoeux:
A double-copula stochastic frontier model with dependent error components and correction for sample selection. Int. J. Approx. Reason. 80: 174-184 (2017) - [c85]Feng Li, Shoumei Li, Nana Tang, Thierry Denoeux:
Constrained interval-valued linear regression model. FUSION 2017: 1-8 - [c84]Chunfeng Lian, Su Ruan, Thierry Denoeux, Yu Guo, Pierre Vera:
Accurate tumor segmentation in FDG-PET images with guidance of complementary CT images. ICIP 2017: 4447-4451 - [c83]Chunfeng Lian, Su Ruan, Thierry Denoeux, Hua Li, Pierre Vera:
Tumor delineation in FDG-PET images using a new evidential clustering algorithm with spatial regularization and adaptive distance metric. ISBI 2017: 1177-1180 - [c82]Orakanya Kanjanatarakul, Thierry Denoeux:
Distributed data fusion in the dempster-shafer framework. SoSE 2017: 1-6 - 2016
- [j82]Benjamin Quost, Thierry Denoeux:
Clustering and classification of fuzzy data using the fuzzy EM algorithm. Fuzzy Sets Syst. 286: 134-156 (2016) - [j81]Philippe Xu, Franck Davoine, Hongbin Zha, Thierry Denoeux:
Evidential calibration of binary SVM classifiers. Int. J. Approx. Reason. 72: 55-70 (2016) - [j80]Orakanya Kanjanatarakul, Thierry Denoeux, Songsak Sriboonchitta:
Prediction of future observations using belief functions: A likelihood-based approach. Int. J. Approx. Reason. 72: 71-94 (2016) - [j79]Thierry Denoeux:
40 years of Dempster-Shafer theory. Int. J. Approx. Reason. 79: 1-6 (2016) - [j78]Thierry Denoeux, Songsak Sriboonchitta, Orakanya Kanjanatarakul:
Evidential clustering of large dissimilarity data. Knowl. Based Syst. 106: 179-195 (2016) - [j77]Chunfeng Lian, Su Ruan, Thierry Denoeux, Fabrice Jardin, Pierre Vera:
Selecting radiomic features from FDG-PET images for cancer treatment outcome prediction. Medical Image Anal. 32: 257-268 (2016) - [j76]Philippe Xu, Franck Davoine, Jean-Baptiste Bordes, Huijing Zhao, Thierry Denoeux:
Multimodal information fusion for urban scene understanding. Mach. Vis. Appl. 27(3): 331-349 (2016) - [j75]Sawsan Kanj, Fahed Abdallah, Thierry Denoeux, Kifah Tout:
Editing training data for multi-label classification with the k-nearest neighbor rule. Pattern Anal. Appl. 19(1): 145-161 (2016) - [j74]Marie-Hélène Masson, Sébastien Destercke, Thierry Denoeux:
Modelling and predicting partial orders from pairwise belief functions. Soft Comput. 20(3): 939-950 (2016) - [j73]Chunfeng Lian, Su Ruan, Thierry Denoeux:
Dissimilarity Metric Learning in the Belief Function Framework. IEEE Trans. Fuzzy Syst. 24(6): 1555-1564 (2016) - [j72]Lianmeng Jiao, Thierry Denoeux, Quan Pan:
A Hybrid Belief Rule-Based Classification System Based on Uncertain Training Data and Expert Knowledge. IEEE Trans. Syst. Man Cybern. Syst. 46(12): 1711-1723 (2016) - [c81]Orakanya Kanjanatarakul, Songsak Sriboonchitta, Thierry Denoeux:
k-EVCLUS: Clustering Large Dissimilarity Data in the Belief Function Framework. BELIEF 2016: 105-112 - [c80]Liqi Sui, Pierre Feissel, Thierry Denoeux:
Identification of Elastic Properties Based on Belief Function Inference. BELIEF 2016: 182-189 - [c79]Chunfeng Lian, Su Ruan, Thierry Denoeux:
Joint Feature Transformation and Selection Based on Dempster-Shafer Theory. IPMU (1) 2016: 253-261 - [c78]Thierry Denoeux, Orakanya Kanjanatarakul:
Evidential Clustering: A Review. IUKM 2016: 24-35 - [c77]Chunfeng Lian, Su Ruan, Thierry Denoeux, Hua Li, Pierre Vera:
Robust Cancer Treatment Outcome Prediction Dealing with Small-Sized and Imbalanced Data from FDG-PET Images. MICCAI (2) 2016: 61-69 - [c76]Thierry Denoeux, Orakanya Kanjanatarakul:
Beyond Fuzzy, Possibilistic and Rough: An Investigation of Belief Functions in Clustering. SMPS 2016: 157-164 - [e6]Van-Nam Huynh, Masahiro Inuiguchi, Bac Le, Bao Nguyen Le, Thierry Denoeux:
Integrated Uncertainty in Knowledge Modelling and Decision Making - 5th International Symposium, IUKM 2016, Da Nang, Vietnam, November 30 - December 2, 2016, Proceedings. Lecture Notes in Computer Science 9978, 2016, ISBN 978-3-319-49045-8 [contents] - 2015
- [j71]Aree Wiboonpongse, Jianxu Liu, Songsak Sriboonchitta, Thierry Denoeux:
Modeling dependence between error components of the stochastic frontier model using copula: Application to intercrop coffee production in Northern Thailand. Int. J. Approx. Reason. 65: 34-44 (2015) - [j70]Xun Wang, Shoumei Li, Thierry Denoeux:
Interval-Valued Linear Model. Int. J. Comput. Intell. Syst. 8(1): 114-127 (2015) - [j69]Lianmeng Jiao, Quan Pan, Thierry Denoeux, Yan Liang, Xiaoxue Feng:
Belief rule-based classification system: Extension of FRBCS in belief functions framework. Inf. Sci. 309: 26-49 (2015) - [j68]Thierry Denoeux, Orakanya Kanjanatarakul, Songsak Sriboonchitta:
EK-NNclus: A clustering procedure based on the evidential K-nearest neighbor rule. Knowl. Based Syst. 88: 57-69 (2015) - [j67]Chunfeng Lian, Su Ruan, Thierry Denoeux:
An evidential classifier based on feature selection and two-step classification strategy. Pattern Recognit. 48(7): 2318-2327 (2015) - [c75]Lianmeng Jiao, Thierry Denoeux, Quan Pan:
Evidential Editing K-Nearest Neighbor Classifier. ECSQARU 2015: 461-471 - [c74]Philippe Xu, Franck Davoine, Thierry Denoeux:
Evidential multinomial logistic regression for multiclass classifier calibration. FUSION 2015: 1106-1112 - [c73]Chunfeng Lian, Su Ruan, Thierry Denoeux, Pierre Vera:
Outcome prediction in tumour therapy based on Dempster-Shafer theory. ISBI 2015: 63-66 - [c72]Abdel-Djalil Ourabah, Benjamin Quost, Atef Gayed, Thierry Denoeux:
Estimating energy consumption of a PHEV using vehicle and on-board navigation data. Intelligent Vehicles Symposium 2015: 755-760 - [c71]Chunfeng Lian, Su Ruan, Thierry Denoeux, Hua Li, Pierre Vera:
Dempster-Shafer Theory Based Feature Selection with Sparse Constraint for Outcome Prediction in Cancer Therapy. MICCAI (3) 2015: 695-702 - [p3]Orakanya Kanjanatarakul, Nachatchapong Kaewsompong, Songsak Sriboonchitta, Thierry Denoeux:
Estimation and Prediction Using Belief Functions: Application to Stochastic Frontier Analysis. Econometrics of Risk 2015: 171-184 - [p2]Supanika Leurcharusmee, Jirakom Sirisrisakulchai, Songsak Sriboonchitta, Thierry Denoeux:
The Classifier Chain Generalized Maximum Entropy Model for Multi-label Choice Problems. Econometrics of Risk 2015: 185-199 - [e5]Sébastien Destercke, Thierry Denoeux:
Symbolic and Quantitative Approaches to Reasoning with Uncertainty - 13th European Conference, ECSQARU 2015, Compiègne, France, July 15-17, 2015. Proceedings. Lecture Notes in Computer Science 9161, Springer 2015, ISBN 978-3-319-20806-0 [contents] - [e4]Van-Nam Huynh, Masahiro Inuiguchi, Thierry Denoeux:
Integrated Uncertainty in Knowledge Modelling and Decision Making - 4th International Symposium, IUKM 2015, Nha Trang, Vietnam, October 15-17, 2015, Proceedings. Lecture Notes in Computer Science 9376, Springer 2015, ISBN 978-3-319-25134-9 [contents] - 2014
- [j66]Ana Colubi, Thierry Denoeux:
Special issue on imprecision in statistical data analysis. Comput. Stat. Data Anal. 71: 787-788 (2014) - [j65]Nadia Ben Abdallah, Nassima Mouhous Voyneau, Thierry Denoeux:
Combining statistical and expert evidence using belief functions: Application to centennial sea level estimation taking into account climate change. Int. J. Approx. Reason. 55(1): 341-354 (2014) - [j64]Orakanya Kanjanatarakul, Songsak Sriboonchitta, Thierry Denoeux:
Forecasting using belief functions: An application to marketing econometrics. Int. J. Approx. Reason. 55(5): 1113-1128 (2014) - [j63]Thierry Denoeux:
Likelihood-based belief function: Justification and some extensions to low-quality data. Int. J. Approx. Reason. 55(7): 1535-1547 (2014) - [j62]Thierry Denoeux:
Rejoinder on "Likelihood-based belief function: Justification and some extensions to low-quality data". Int. J. Approx. Reason. 55(7): 1614-1617 (2014) - [j61]Benoît Lelandais, Su Ruan, Thierry Denoeux, Pierre Vera, Isabelle Gardin:
Fusion of multi-tracer PET images for dose painting. Medical Image Anal. 18(7): 1247-1259 (2014) - [j60]Violaine Antoine, Benjamin Quost, Marie-Hélène Masson, Thierry Denoeux:
CEVCLUS: evidential clustering with instance-level constraints for relational data. Soft Comput. 18(7): 1321-1335 (2014) - [j59]Thierry Denoeux, Nicole El Zoghby, Véronique Cherfaoui, Antoine Jouglet:
Optimal Object Association in the Dempster-Shafer Framework. IEEE Trans. Cybern. 44(12): 2521-2531 (2014) - [j58]Philippe Xu, Franck Davoine, Jean-Baptiste Bordes, Thierry Denoeux:
Fusion d'informations pour la compréhension de scènes. Traitement du Signal 31(1-2): 57-80 (2014) - [j57]Emmanuel Ramasso, Thierry Denoeux:
Making Use of Partial Knowledge About Hidden States in HMMs: An Approach Based on Belief Functions. IEEE Trans. Fuzzy Syst. 22(2): 395-405 (2014) - [c70]Philippe Xu, Franck Davoine, Thierry Denoeux:
Evidential Logistic Regression for Binary SVM Classifier Calibration. Belief Functions 2014: 49-57 - [c69]Supanika Leurcharusmee, Peerapat Jatukannyaprateep, Songsak Sriboonchitta, Thierry Denoeux:
The Evidence-Theoretic k-NN Rule for Rank-Ordered Data: Application to Predict an Individual's Source of Loan. Belief Functions 2014: 58-67 - [c68]Nicolas Sutton-Charani, Sébastien Destercke, Thierry Denoeux:
Training and Evaluating Classifiers from Evidential Data: Application to E 2 M Decision Tree Pruning. Belief Functions 2014: 87-94 - [c67]Kittawit Autchariyapanitkul, Somsak Chanaim, Songsak Sriboonchitta, Thierry Denoeux:
Predicting Stock Returns in the Capital Asset Pricing Model Using Quantile Regression and Belief Functions. Belief Functions 2014: 219-226 - [c66]Philippe Xu, Franck Davoine, Thierry Denoeux:
Evidential combination of pedestrian detectors. BMVC 2014 - [c65]Lianmeng Jiao, Thierry Denoeux, Quan Pan:
Fusion of pairwise nearest-neighbor classifiers based on pairwise-weighted distance metric and Dempster-Shafer theory. FUSION 2014: 1-7 - [c64]Nicolas Sutton-Charani, Sébastien Destercke, Thierry Denoeux:
Application of E 2 M Decision Trees to Rubber Quality Prediction. IPMU (1) 2014: 107-116 - [c63]Nicole El Zoghby, Véronique Cherfaoui, Thierry Denoeux:
Evidential distributed dynamic map for cooperative perception in VANets. Intelligent Vehicles Symposium 2014: 1421-1426 - [e3]Van-Nam Huynh, Thierry Denoeux, Dang Hung Tran, Anh-Cuong Le, Son Bao Pham:
Knowledge and Systems Engineering - Proceedings of the Fifth International Conference, KSE 2013, Volume 1, Hanoi, Vietnam, 17-19 October, 2013. Advances in Intelligent Systems and Computing 244, Springer 2014, ISBN 978-3-319-02740-1 [contents] - [e2]Van-Nam Huynh, Thierry Denoeux, Dang Hung Tran, Anh-Cuong Le, Son Bao Pham:
Knowledge and Systems Engineering - Proceedings of the Fifth International Conference KSE 2013, Volume 2. Advances in Intelligent Systems and Computing 245, Springer 2014, ISBN 978-3-319-02820-0 [contents] - 2013
- [j56]Thierry Denoeux:
Maximum Likelihood Estimation from Uncertain Data in the Belief Function Framework. IEEE Trans. Knowl. Data Eng. 25(1): 119-130 (2013) - [c62]Nicole El Zoghby, Véronique Cherfaoui, Thierry Denoeux:
Optimal object association from pairwise evidential mass functions. FUSION 2013: 774-780 - [c61]Nadia Ben Abdallah, Nassima Mouhous Voyneau, Thierry Denoeux:
Using Dempster-Shafer theory to model uncertainty in climate change and environmental impact assessments. FUSION 2013: 2117-2124 - [c60]Nicolas Sutton-Charani, Sébastien Destercke, Thierry Denoeux:
Learning Decision Trees from Uncertain Data with an Evidential EM Approach. ICMLA (1) 2013: 111-116 - [c59]Jean-Baptiste Bordes, Franck Davoine, Philippe Xu, Thierry Denoeux:
Evidential Grammars for Image Interpretation - Application to Multimodal Traffic Scene Understanding. IUKM 2013: 65-78 - [c58]Philippe Xu, Franck Davoine, Jean-Baptiste Bordes, Huijing Zhao, Thierry Denoeux:
Information Fusion on Oversegmented Images: An Application for Urban Scene Understanding. MVA 2013: 189-193 - 2012
- [j55]Thierry Denoeux, Marie-Hélène Masson:
Evidential reasoning in large partially ordered sets - Application to multi-label classification, ensemble clustering and preference aggregation. Ann. Oper. Res. 195(1): 135-161 (2012) - [j54]Violaine Antoine, Benjamin Quost, Marie-Hélène Masson, Thierry Denoeux:
CECM: Constrained evidential C-means algorithm. Comput. Stat. Data Anal. 56(4): 894-914 (2012) - [j53]Frédéric Pichon, Didier Dubois, Thierry Denoeux:
Relevance and truthfulness in information correction and fusion. Int. J. Approx. Reason. 53(2): 159-175 (2012) - [j52]Etienne Côme, Latifa Oukhellou, Thierry Denoeux, Patrice Aknin:
Fault diagnosis of a railway device using semi-supervised independent factor analysis with mixing constraints. Pattern Anal. Appl. 15(3): 313-326 (2012) - [j51]Zohra Leila Cherfi, Latifa Oukhellou, Etienne Côme, Thierry Denoeux, Patrice Aknin:
Partially supervised Independent Factor Analysis using soft labels elicited from multiple experts: application to railway track circuit diagnosis. Soft Comput. 16(5): 741-754 (2012) - [c57]Sawsan Kanj, Fahed Abdallah, Thierry Denoeux:
Evidential Multi-label Classification Using the Random k-Label Sets Approach. Belief Functions 2012: 21-28 - [c56]Nicolas Sutton-Charani, Sébastien Destercke, Thierry Denoeux:
Classification Trees Based on Belief Functions. Belief Functions 2012: 77-84 - [c55]Marie-Hélène Masson, Thierry Denoeux:
Ranking from Pairwise Comparisons in the Belief Functions Framework. Belief Functions 2012: 311-318 - [c54]Nicole El Zoghby, Véronique Cherfaoui, Bertrand Ducourthial, Thierry Denoeux:
Distributed Data Fusion for Detecting Sybil Attacks in VANETs. Belief Functions 2012: 351-358 - [c53]Emmanuel Ramasso, Thierry Denoeux, Noureddine Zerhouni:
Partially-Hidden Markov Models. Belief Functions 2012: 359-366 - [c52]Didier Dubois, Thierry Denoeux:
Conditioning in Dempster-Shafer Theory: Prediction vs. Revision. Belief Functions 2012: 385-392 - [c51]Nadia Ben Abdallah, Nassima Mouhous Voyneau, Thierry Denoeux:
Combining Statistical and Expert Evidence within the D-S Framework: Application to Hydrological Return Level Estimation. Belief Functions 2012: 393-400 - [c50]Sawsan Kanj, Fahed Abdallah, Thierry Denoeux:
Purifying training data to improve performance of multi-label classification algorithms. FUSION 2012: 1784-1791 - [c49]Rui Jorge Almeida, Thierry Denoeux, Uzay Kaymak:
Constructing Rule-Based Models Using the Belief Functions Framework. IPMU (3) 2012: 554-563 - [c48]Bertrand Ducourthial, Véronique Cherfaoui, Thierry Denoeux:
Self-stabilizing Distributed Data Fusion. SSS 2012: 148-162 - [e1]Thierry Denoeux, Marie-Hélène Masson:
Belief Functions: Theory and Applications - Proceedings of the 2nd International Conference on Belief Functions, Compiègne, France, 9-11 May 2012. Advances in Intelligent and Soft Computing 164, Springer 2012, ISBN 978-3-642-29460-0 [contents] - 2011
- [j50]Zoulficar Younes, Fahed Abdallah, Thierry Denoeux, Hichem Snoussi:
A Dependent Multilabel Classification Method Derived from the k-Nearest Neighbor Rule. EURASIP J. Adv. Signal Process. 2011 (2011) - [j49]Thierry Denoeux:
Maximum likelihood estimation from fuzzy data using the EM algorithm. Fuzzy Sets Syst. 183(1): 72-91 (2011) - [j48]Marie-Hélène Masson, Thierry Denoeux:
Ensemble clustering in the belief functions framework. Int. J. Approx. Reason. 52(1): 92-109 (2011) - [j47]Benjamin Quost, Marie-Hélène Masson, Thierry Denoeux:
Classifier fusion in the Dempster-Shafer framework using optimized t-norm based combination rules. Int. J. Approx. Reason. 52(3): 353-374 (2011) - [j46]Fahed Abdallah, Ghalia Nassreddine, Thierry Denoeux:
A Multiple-Hypothesis Map-Matching Method Suitable for Weighted and Box-Shaped State Estimation for Localization. IEEE Trans. Intell. Transp. Syst. 12(4): 1495-1510 (2011) - [c47]Violaine Antoine, Benjamin Quost, Mylène Masson, Thierry Denoeux:
CEVCLUS: Constrained evidential clustering of proximity data. EUSFLAT Conf. 2011: 876-882 - [c46]Latifa Oukhellou, Etienne Côme, Patrice Aknin, Thierry Denoeux:
Semi-supervised Feature Extraction Using Independent Factor Analysis. ICMLA (2) 2011: 330-333 - [c45]Zohra Leila Cherfi, Latifa Oukhellou, Etienne Côme, Thierry Denoeux, Patrice Aknin:
Using Imprecise and Uncertain Information to Enhance the Diagnosis of a Railway Device. NL-MUA 2011: 213-220 - 2010
- [j45]Thierry Denoeux, Zoulficar Younes, Fahed Abdallah:
Representing uncertainty on set-valued variables using belief functions. Artif. Intell. 174(7-8): 479-499 (2010) - [j44]Latifa Oukhellou, Alexandra Debiolles, Thierry Denoeux, Patrice Aknin:
Fault diagnosis in railway track circuits using Dempster-Shafer classifier fusion. Eng. Appl. Artif. Intell. 23(1): 117-128 (2010) - [j43]Frédéric Pichon, Thierry Denoeux:
The Unnormalized Dempster's Rule of Combination: A New Justification from the Least Commitment Principle and Some Extensions. J. Autom. Reason. 45(1): 61-87 (2010) - [j42]Ghalia Nassreddine, Fahed Abdallah, Thierry Denoeux:
State Estimation Using Interval Analysis and Belief-Function Theory: Application to Dynamic Vehicle Localization. IEEE Trans. Syst. Man Cybern. Part B 40(5): 1205-1218 (2010) - [c44]Violaine Antoine, Benjamin Quost, Marie-Hélène Masson, Thierry Denoeux:
CECM: Adding pairwise constraints to evidential clustering. FUZZ-IEEE 2010: 1-8 - [c43]Zoulficar Younes, Fahed Abdallah, Thierry Denoeux:
Fuzzy multi-label learning under veristic variables. FUZZ-IEEE 2010: 1-8 - [c42]Zoulficar Younes, Fahed Abdallah, Thierry Denoeux:
Evidential Multi-Label Classification Approach to Learning from Data with Imprecise Labels. IPMU 2010: 119-128 - [c41]Thierry Denoeux, Marie-Hélène Masson:
Dempster-Shafer Reasoning in Large Partially Ordered Sets: Applications in Machine Learning. IUM 2010: 39-54 - [c40]Thierry Denoeux:
Theory of Belief Functions for Data Analysis and Machine Learning Applications: Review and Prospects. KSEM 2010: 3 - [c39]Thierry Denoeux:
Maximum Likelihood from Evidential Data: An Extension of the EM Algorithm. SMPS 2010: 181-188 - [c38]Didier Dubois, Thierry Denoeux:
Statistical Inference with Belief Functions and Possibility Measures: A Discussion of Basic Assumptions. SMPS 2010: 217-225 - [c37]Benjamin Quost, Thierry Denoeux:
Clustering Fuzzy Data Using the Fuzzy EM Algorithm. SUM 2010: 333-346
2000 – 2009
- 2009
- [j41]David Mercier, Genevieve Cron, Thierry Denoeux, Marie-Hélène Masson:
Decision fusion for postal address recognition using belief functions. Expert Syst. Appl. 36(3): 5643-5653 (2009) - [j40]Thierry Denoeux:
Extending stochastic ordering to belief functions on the real line. Inf. Sci. 179(9): 1362-1376 (2009) - [j39]Etienne Côme, Latifa Oukhellou, Thierry Denoeux, Patrice Aknin:
Learning from partially supervised data using mixture models and belief functions. Pattern Recognit. 42(3): 334-348 (2009) - [j38]Marie-Hélène Masson, Thierry Denoeux:
RECM: Relational evidential c-means algorithm. Pattern Recognit. Lett. 30(11): 1015-1026 (2009) - [c36]Marie-Hélène Masson, Thierry Denoeux:
Belief Functions and Cluster Ensembles. ECSQARU 2009: 323-334 - [c35]Etienne Côme, Latifa Oukhellou, Patrice Aknin, Thierry Denoeux:
Partially-supervised learning in Independent Factor Analysis. ESANN 2009 - [c34]Krystyna Biletska, Sophie Midenet, Marie-Hélène Masson, Thierry Denoeux:
Fuzzy Modelling of Sensor Data for the Estimation of an Origin-Destination Matrix. IFSA/EUSFLAT Conf. 2009: 849-854 - [c33]Ghalia Nassreddine, Fahed Abdallah, Thierry Denoeux:
A state estimation method for multiple model systems using belief function theory. FUSION 2009: 506-513 - [c32]Etienne Côme, Latifa Oukhellou, Thierry Denoeux, Patrice Aknin:
Noiseless Independent Factor Analysis with Mixing Constraints in a Semi-supervised Framework. Application to Railway Device Fault Diagnosis. ICANN (2) 2009: 416-425 - [c31]Krystyna Biletska, Marie-Hélène Masson, Sophie Midenet, Thierry Denoeux:
Short-time OD matrix estimation for a complex junction using fuzzy-timed high-level petri nets. ITSC 2009: 1-6 - [c30]Benjamin Quost, Thierry Denoeux:
Learning from data with uncertain labels by boosting credal classifiers. KDD Workshop on Knowledge Discovery from Uncertain Data 2009: 38-47 - [c29]Krystyna Biletska, Marie-Hélène Masson, Sophie Midenet, Thierry Denoeux:
Multisensor data fusion for OD matrix estimation. SMC 2009: 3024-3029 - [c28]Zoulficar Younes, Fahed Abdallah, Thierry Denoeux:
An Evidence-Theoretic k-Nearest Neighbor Rule for Multi-label Classification. SUM 2009: 297-308 - 2008
- [j37]Thierry Denoeux:
Conjunctive and disjunctive combination of belief functions induced by nondistinct bodies of evidence. Artif. Intell. 172(2-3): 234-264 (2008) - [j36]Thierry Denoeux:
Special issue in memory of Philippe Smets (1938-2005). Int. J. Approx. Reason. 48(2): 349-351 (2008) - [j35]Astride Aregui, Thierry Denoeux:
Constructing consonant belief functions from sample data using confidence sets of pignistic probabilities. Int. J. Approx. Reason. 49(3): 575-594 (2008) - [j34]David Mercier, Benjamin Quost, Thierry Denoeux:
Refined modeling of sensor reliability in the belief function framework using contextual discounting. Inf. Fusion 9(2): 246-258 (2008) - [j33]Marie-Hélène Masson, Thierry Denoeux:
ECM: An evidential version of the fuzzy c. Pattern Recognit. 41(4): 1384-1397 (2008) - [c27]Zoulficar Younes, Fahed Abdallah, Thierry Denoeux:
Multi-label classification algorithm derived from K-nearest neighbor rule with label dependencies. EUSIPCO 2008: 1-5 - [c26]Frédéric Pichon, Thierry Denoeux:
A New Justification of the Unnormalized Dempster's Rule of Combination from the Least Commitment Principle. FLAIRS 2008: 666-671 - [c25]Véronique Cherfaoui, Thierry Denoeux, Zohra Leila Cherfi:
Distributed data fusion: application to confidence management in vehicular networks. FUSION 2008: 1-8 - [c24]Ghalia Nassreddine, Fahed Abdallah, Thierry Denoeux:
Map matching algorithm using belief function theory. FUSION 2008: 1-8 - [c23]Benjamin Quost, Marie-Hélène Masson, Thierry Denoeux:
Refined classifier combination using belief functions. FUSION 2008: 1-7 - [c22]Etienne Côme, Latifa Oukhellou, Thierry Denoeux, Patrice Aknin:
Mixture Model Estimation with Soft Labels. SMPS 2008: 165-174 - [p1]Thierry Denoeux:
A k -Nearest Neighbor Classification Rule Based on Dempster-Shafer Theory. Classic Works of the Dempster-Shafer Theory of Belief Functions 2008: 737-760 - 2007
- [j32]Benjamin Quost, Thierry Denoeux, Marie-Hélène Masson:
Pairwise classifier combination using belief functions. Pattern Recognit. Lett. 28(5): 644-653 (2007) - [c21]Thierry Denoeux:
Pattern Recognition and Information Fusion Using Belief Functions: Some Recent Developments. ECSQARU 2007: 1 - [c20]Astride Aregui, Thierry Denoeux:
Consonant Belief Function Induced by a Confidence Set of Pignistic Probabilities. ECSQARU 2007: 344-355 - [c19]Frédéric Pichon, Thierry Denoeux:
On Latent Belief Structures. ECSQARU 2007: 368-380 - [c18]Astride Aregui, Thierry Denoeux:
Fusion of one-class classifiers in the belief function framework. FUSION 2007: 1-8 - 2006
- [j31]Pierre-Alexandre Hébert, Marie-Hélène Masson, Thierry Denoeux:
Fuzzy multidimensional scaling. Comput. Stat. Data Anal. 51(1): 335-359 (2006) - [j30]Marie-Hélène Masson, Thierry Denoeux:
Inferring a possibility distribution from empirical data. Fuzzy Sets Syst. 157(3): 319-340 (2006) - [j29]Hugues Bersini, Thierry Denoeux, Didier Dubois, Henri Prade:
In Memoriam: Philippe Smets (1938-2005). Fuzzy Sets Syst. 157(8) (2006) - [j28]Hugues Bersini, Thierry Denoeux, Didier Dubois, Henri Prade:
Philippe Smets (1938-2005). Int. J. Approx. Reason. 41(3) (2006) - [j27]Thierry Denoeux:
Constructing belief functions from sample data using multinomial confidence regions. Int. J. Approx. Reason. 42(3): 228-252 (2006) - [j26]Sabrina Démotier, Paul Walter Schön, Thierry Denoeux:
Risk assessment based on weak information using belief functions: a case study in water treatment. IEEE Trans. Syst. Man Cybern. Syst. 36(3): 382-396 (2006) - [j25]Thierry Denoeux, Philippe Smets:
Classification Using Belief Functions: Relationship Between Case-Based and Model-Based Approaches. IEEE Trans. Syst. Man Cybern. Part B 36(6): 1395-1406 (2006) - [c17]Alexandra Debiolles, Latifa Oukhellou, Thierry Denoeux, Patrice Aknin:
Output coding of spatially dependent subclassifiers in evidential framework. Application to the diagnosis of railway track/vehicle transmission system. FUSION 2006: 1-6 - [c16]Thierry Denoeux:
The cautious rule of combination for belief functions and some extensions. FUSION 2006: 1-8 - [c15]David Mercier, Thierry Denoeux, Marie-Hélène Masson:
General Correction Mechanisms for Weakening or Reinforcing Belief Functions. FUSION 2006: 1-7 - 2005
- [j24]Thierry Denoeux:
R. P. Srivastava and T. J. Mock, Belief Functions in Business Decisions, in Studies in Fuzziness and Soft Computing, vol. 88, Physica-Verlag, Heidelberg (2002) ISBN 3-7908-1451-2 (345pp.). Fuzzy Sets Syst. 151(2): 435-436 (2005) - [j23]Thierry Denoeux, Marie-Hélène Masson, Pierre-Alexandre Hébert:
Nonparametric rank-based statistics and significance tests for fuzzy data. Fuzzy Sets Syst. 153(1): 1-28 (2005) - [j22]Thierry Denoeux, Piero P. Bonissone:
Editorial. Int. J. Approx. Reason. 40(3): 125-126 (2005) - [c14]David Mercier, Benjamin Quost, Thierry Denoeux:
Contextual Discounting of Belief Functions. ECSQARU 2005: 552-562 - 2004
- [j21]Simon Petit-Renaud, Thierry Denoeux:
Nonparametric regression analysis of uncertain and imprecise data using belief functions. Int. J. Approx. Reason. 35(1): 1-28 (2004) - [j20]Marie-Hélène Masson, Thierry Denoeux:
Clustering interval-valued proximity data using belief functions. Pattern Recognit. Lett. 25(2): 163-171 (2004) - [j19]Thierry Denoeux, Marie-Hélène Masson:
Principal component analysis of fuzzy data using autoassociative neural networks. IEEE Trans. Fuzzy Syst. 12(3): 336-349 (2004) - [j18]Thierry Denoeux, Marie-Hélène Masson:
EVCLUS: evidential clustering of proximity data. IEEE Trans. Syst. Man Cybern. Part B 34(1): 95-109 (2004) - [c13]Sandro Glaucio Maquiné de Souza, Thierry Denoeux, Yves Grandvalet:
Recycling experiments for sludge monitoring in waste water treatment. SMC (2) 2004: 1342-1347 - 2003
- [j17]Jérémie François, Yves Grandvalet, Thierry Denoeux, Jean-Michel Roger:
Resample and combine: an approach to improving uncertainty representation in evidential pattern classification. Inf. Fusion 4(2): 75-85 (2003) - [j16]Jérémie François, Yves Grandvalet, Thierry Denoeux, Jean-Michel Roger:
Addendum to resample and combine: an approach to improving uncertainty representation in evidential pattern classification. Inf. Fusion 4(3): 235-236 (2003) - [c12]Sabrina Démotier, Thierry Denoeux, Paul Walter Schön:
Risk Assessment in Drinking Water Production Using Belief Functions. ECSQARU 2003: 319-331 - [c11]Sabrina Démotier, Paul Walter Schön, Thierry Denoeux, Khaled Odeh:
A new approach to assess risk in water treatment using the belief function framework. SMC 2003: 1792-1797 - 2002
- [j15]Marie-Hélène Masson, Thierry Denoeux:
Multidimensional scaling of fuzzy dissimilarity data. Fuzzy Sets Syst. 128(3): 339-352 (2002) - [j14]Thierry Denoeux, Amel Ben Yaghlane:
Approximating the combination of belief functions using the fast Mo"bius transform in a coarsened frame. Int. J. Approx. Reason. 31(1-2): 77-101 (2002) - 2001
- [j13]Thierry Denoeux, Lalla Merieme Zouhal:
Handling possibilistic labels in pattern classification using evidential reasoning. Fuzzy Sets Syst. 122(3): 409-424 (2001) - [j12]Nicolas Valentin, Thierry Denoeux:
A neural network-based software sensor for coagulation control in a water treatment plant. Intell. Data Anal. 5(1): 23-39 (2001) - [j11]Thierry Denoeux:
Inner and Outer Approximation of Belief Structures Using a Hierarchical Clustering Approach. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 9(4): 437-460 (2001) - [c10]Amel Ben Yaghlane, Thierry Denoeux, Khaled Mellouli:
Coarsening Approximations of Belief Functions. ECSQARU 2001: 362-373 - [c9]Patrick Vannoorenberghe, Thierry Denoeux:
Likelihood-based Vs Distance-based Evidential Classifiers. FUZZ-IEEE 2001: 320-323 - 2000
- [j10]Thierry Denoeux:
Modeling vague beliefs using fuzzy-valued belief structures. Fuzzy Sets Syst. 116(2): 167-199 (2000) - [j9]Thierry Denoeux, Marie-Hélène Masson:
Multidimensional scaling of interval-valued dissimilarity data. Pattern Recognit. Lett. 21(1): 83-92 (2000) - [j8]Thierry Denoeux:
A neural network classifier based on Dempster-Shafer theory. IEEE Trans. Syst. Man Cybern. Part A 30(2): 131-150 (2000) - [c8]Thierry Denoeux, M. Skarstein Bjanger:
Induction of decision trees from partially classified data using belief functions. SMC 2000: 2923-2928
1990 – 1999
- 1999
- [j7]Thierry Denoeux:
Reasoning with imprecise belief structures. Int. J. Approx. Reason. 20(1): 79-111 (1999) - [c7]Simon Petit-Renaud, Thierry Denoeux:
Handling Different Forms of Uncertainty in Regression Analysis: A Fuzzy Belief Structure Approach. ESCQARU 1999: 340-351 - [c6]Michèle Rombaut, Iman Jarkass, Thierry Denoeux:
State Recognition in Discrete Dynamical Systems Using Petri Nets and Evidence Theory. ESCQARU 1999: 352-361 - [c5]Nicolas Valentin, Thierry Denoeux, F. Fotoohi:
An hybrid neural network based system for optimization of coagulant dosing in a water treatment plant. IJCNN 1999: 3380-3385 - 1998
- [j6]Lalla Merieme Zouhal, Thierry Denoeux:
An evidence-theoretic k-NN rule with parameter optimization. IEEE Trans. Syst. Man Cybern. Part C 28(2): 263-271 (1998) - 1997
- [j5]Thierry Denoeux:
Analysis of evidence-theoretic decision rules for pattern classification. Pattern Recognit. 30(7): 1095-1107 (1997) - [c4]Thierry Denoeux:
Function approximation in the framework of evidence theory: a connectionist approach. ICNN 1997: 199-203 - 1996
- [j4]Régis Lengellé, Thierry Denoeux:
Training MLPs layer by layer using an objective function for internal representations. Neural Networks 9(1): 83-97 (1996) - 1995
- [j3]Thierry Denoeux, P. Rizand:
Analysis of Rainfall Forecasting using Neural Networks. Neural Comput. Appl. 3(1): 50-61 (1995) - [j2]Thierry Denoeux:
A k-nearest neighbor classification rule based on Dempster-Shafer theory. IEEE Trans. Syst. Man Cybern. 25(5): 804-813 (1995) - [c3]Lalla Merieme Zouhal, Thierry Denoeux:
An Adaptive k-NN Rule Based on Dempster-Shafer Theory. CAIP 1995: 310-317 - [c2]Mohamed Karouia, Régis Lengellé, Thierry Denoeux:
Performance analysis of a MLP weight initialization algorithm. ESANN 1995 - [c1]T. Trautmann, Thierry Denoeux:
Comparison of dynamic feature map models for environmental monitoring. ICNN 1995: 73-78 - 1993
- [j1]Thierry Denoeux, Régis Lengellé:
Initializing back propagation networks with prototypes. Neural Networks 6(3): 351-363 (1993)
Coauthor Index
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last updated on 2024-11-13 23:50 CET by the dblp team
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