Thierry Denoeux

发表

Distributed data fusion: application to confidence management in vehicular networks

Véronique Berge-Cherfaoui, Thierry Denoeux, Zohra Leila Cherfi, 2008, 2008 11th International Conference on Information Fusion.

Constructing Rule-Based Models Using the Belief Functions Framework

Uzay Kaymak, Thierry Denoeux, Rui Jorge Almeida, 2012, IPMU.

State Estimation Using Interval Analysis and Belief-Function Theory: Application to Dynamic Vehicle Localization

Thierry Denoeux, Ghalia Nassreddine, Fahed Abdallah, 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

Optimal Object Association in the Dempster–Shafer Framework

Véronique Berge-Cherfaoui, Thierry Denoeux, Nicole El Zoghby, 2014, IEEE Transactions on Cybernetics.

Special issue on imprecision in statistical data analysis

Thierry Denoeux, Ana Colubi, 2014, Comput. Stat. Data Anal..

Constructing consonant belief functions from sample data using confidence sets of pignistic probabilities

Thierry Denoeux, Astride Aregui, T. Denoeux, 2008, Int. J. Approx. Reason..

Maximum likelihood estimation from fuzzy data using the EM algorithm

Thierry Denoeux, T. Denoeux, 2011, Fuzzy Sets Syst..

Resample and combine: an approach to improving uncertainty representation in evidential pattern classification

Thierry Denoeux, Yves Grandvalet, Jérémie François, 2003, Inf. Fusion.

Evidential Clustering: A Review

Thierry Denoeux, Orakanya Kanjanatarakul, T. Denoeux, 2016, IUKM.

Estimating energy consumption of a PHEV using vehicle and on-board navigation data

Thierry Denoeux, Abdeldjalil Ouahabi, Benjamin Quost, 2015, 2015 IEEE Intelligent Vehicles Symposium (IV).

CEVCLUS: evidential clustering with instance-level constraints for relational data

Thierry Denoeux, Violaine Antoine, Benjamin Quost, 2014, Soft Comput..

Multidimensional scaling of interval-valued dissimilarity data

Thierry Denoeux, Marie-Hélène Masson, T. Denoeux, 2000, Pattern Recognit. Lett..

Robust Optimization of Demand Response Power Bids for Drinking Water Systems

Thierry Denoeux, Antoine Jouglet, Chouaib Mkireb, 2019 .

Ranking from Pairwise Comparisons in the Belief Functions Framework

Thierry Denoeux, Marie-Hélène Masson, 2012, Belief Functions.

Combining statistical and expert evidence using belief functions: Application to centennial sea level estimation taking into account climate change

Thierry Denoeux, Nadia Ben Abdallah, Nassima Mouhous Voyneau, 2014, Int. J. Approx. Reason..

Theory of Belief Functions for Data Analysis and Machine Learning Applications: Review and Prospects

Thierry Denoeux, 2010, KSEM.

CEVCLUS: Constrained evidential clustering of proximity data

Thierry Denoeux, Violaine Antoine, Benjamin Quost, 2011, EUSFLAT Conf..

A new approach to assess risk in water treatment using the belief function framework

Thierry Denoeux, Sabrina Démotier, Paul Walter Schön, 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

A New Justification of the Unnormalized Dempster's Rule of Combination from the Least Commitment Principle

Thierry Denoeux, Frédéric Pichon, T. Denoeux, 2008, FLAIRS.

Logistic Regression, Neural Networks and Dempster-Shafer Theory: a New Perspective pdf

Thierry Denoeux, T. Denoeux, 2018, Knowl. Based Syst..

Optimal object association from pairwise evidential mass functions

Véronique Berge-Cherfaoui, Thierry Denoeux, Nicole El Zoghby, 2013, Proceedings of the 16th International Conference on Information Fusion.

Handling Different Forms of Uncertainty in Regression Analysis: A Fuzzy Belief Structure Approach

Thierry Denoeux, Simon Petit-Renaud, 1999, ESCQARU.

Pattern Recognition and Information Fusion Using Belief Functions: Some Recent Developments

Thierry Denoeux, 2007, ECSQARU.

Maximum Likelihood from Evidential Data: An Extension of the EM Algorithm

Thierry Denoeux, 2010, SMPS.

Forecasting using belief functions: An application to marketing econometrics

Thierry Denoeux, Songsak Sriboonchitta, Orakanya Kanjanatarakul, 2014, Int. J. Approx. Reason..

Consonant Belief Function Induced by a Confidence Set of Pignistic Probabilities

Thierry Denoeux, Astride Aregui, 2007, ECSQARU.

EVCLUS: evidential clustering of proximity data

Thierry Denoeux, Marie-Hélène Masson, T. Denoeux, 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

State Recognition in Discrete Dynamical Systems Using Petri Nets and Evidence Theory

Thierry Denoeux, Iman Jarkass, Michèle Rombaut, 1999, ESCQARU.

A state estimation method for multiple model systems using belief function theory

Thierry Denoeux, Ghalia Nassreddine, Fahed Abdallah, 2009, 2009 12th International Conference on Information Fusion.

Predicting Stock Returns in the Capital Asset Pricing Model Using Quantile Regression and Belief Functions

Thierry Denoeux, Songsak Sriboonchitta, Kittawit Autchariyapanitkul, 2014, Belief Functions.

A k -Nearest Neighbor Classification Rule Based on Dempster-Shafer Theory

Thierry Denoeux, 2008, Classic Works of the Dempster-Shafer Theory of Belief Functions.

NN-EVCLUS: Neural Network-based Evidential Clustering pdf

Thierry Denoeux, 2020, ArXiv.

Fuzzy multidimensional scaling

Thierry Denoeux, Pierre-Alexandre Hébert, Marie-Hélène Masson, 2006, Comput. Stat. Data Anal..

Accurate tumor segmentation in FDG-PET images with guidance of complementary CT images

Yu Guo, Thierry Denoeux, Chunfeng Lian, 2017, 2017 IEEE International Conference on Image Processing (ICIP).

Clustering and classification of fuzzy data using the fuzzy EM algorithm

Thierry Denoeux, Benjamin Quost, T. Denoeux, 2016, Fuzzy Sets Syst..

Neural networks for process control and optimization: two industrial applications.

Thierry Denoeux, Gérard Bloch, T. Denoeux, 2003, ISA transactions.

Fusion of one-class classifiers in the belief function framework

Thierry Denoeux, Astride Aregui, T. Denoeux, 2007, 2007 10th International Conference on Information Fusion.

Dempster-Shafer Theory Based Feature Selection with Sparse Constraint for Outcome Prediction in Cancer Therapy

Thierry Denoeux, Chunfeng Lian, Su Ruan, 2015, MICCAI.

Evidential distributed dynamic map for cooperative perception in VANets

Véronique Berge-Cherfaoui, Thierry Denoeux, Nicole El Zoghby, 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

A new evidential K-nearest neighbor rule based on contextual discounting with partially supervised learning

Thierry Denoeux, Songsak Sriboonchitta, Orakanya Kanjanatarakul, 2019, Int. J. Approx. Reason..

Statistical Inference with Belief Functions and Possibility Measures: A Discussion of Basic Assumptions

Thierry Denoeux, Didier Dubois, 2010, SMPS.

k-CEVCLUS: Constrained evidential clustering of large dissimilarity data

Thierry Denoeux, Feng Li, Shoumei Li, 2017, Knowl. Based Syst..

ConvNet and Dempster-Shafer Theory for Object Recognition

Thierry Denoeux, Zheng Tong, Philippe Xu, 2019, SUM.

Performance analysis of a MLP weight initialization algorithm

Thierry Denoeux, Régis Lengellé, Mohamed Karouia, 1995, ESANN.

Pairwise Classifier Combination in the Framework of Belief Functions

Thierry Denoeux, Benjamin Quost, Marie-Hélène Masson, 2005 .

Induction of decision trees from partially classified data using belief functions

Thierry Denoeux, M. Skarstein Bjanger, T. Denoeux, 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

Conjunctive and disjunctive combination of belief functions induced by nondistinct bodies of evidence

Thierry Denoeux, T. Denoeux, Thierry Denoeux, 2008, Artif. Intell..

Outcome prediction in tumour therapy based on Dempster-Shafer theory

Thierry Denoeux, Chunfeng Lian, Su Ruan, 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

Inner and Outer Approximation of Belief Structures Using a Hierarchical Clustering Approach

Thierry Denoeux, T. Denoeux, 2001, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

Multimodal information fusion for urban scene understanding

Huijing Zhao, Thierry Denoeux, Franck Davoine, 2016, Machine Vision and Applications.

The cautious rule of combination for belief functions and some extensions

Thierry Denoeux, 2006, 2006 9th International Conference on Information Fusion.

Modelling and predicting partial orders from pairwise belief functions

Thierry Denoeux, Sébastien Destercke, Marie-Hélène Masson, 2016, Soft Comput..

Making Set-Valued Predictions in Evidential Classification: A Comparison of Different Approaches

Thierry Denoeux, Liyao Ma, 2019, ISIPTA.

From Shallow to Deep Interactions Between Knowledge Representation, Reasoning and Machine Learning (Kay R. Amel group) pdf

Thierry Denoeux, Steven Schockaert, Mathieu Serrurier, 2019, ArXiv.

Classifier fusion in the Dempster-Shafer framework using optimized t-norm based combination rules

Thierry Denoeux, Benjamin Quost, Marie-Hélène Masson, 2011, Int. J. Approx. Reason..

Evidential K-NN classification with enhanced performance via optimizing a class of parametric conjunctive t-rules

Ming Zhao, Thierry Denoeux, Zhi-gang Su, 2017, Knowl. Based Syst..

Evidential Deep Neural Networks for Uncertain Data Classification

Thierry Denoeux, Xiaodong Yue, Bin Yuan, 2020, KSEM.

Generalizing the Evidence-Theoretic k-NN rule to Fuzzy Pattern Recognition

Thierry Denoeux, Lalla Merieme Zouhal, 1997 .

Quality of Information Sources in Information Fusion

Thierry Denoeux, Didier Dubois, Frédéric Pichon, 2019, Information Quality in Information Fusion and Decision Making.

Modeling vague beliefs using fuzzy-valued belief structures

Thierry Denoeux, T. Denoeux, 2000, Fuzzy Sets Syst..

Handling possibilistic labels in pattern classification using evidential reasoning

Thierry Denoeux, Lalla Merieme Zouhal, T. Denoeux, 2001, Fuzzy Sets Syst..

Representing uncertainty on set-valued variables using belief functions

Thierry Denoeux, Fahed Abdallah, Zoulficar Younes, 2010, Artif. Intell..

Coarsening Approximations of Belief Functions

Thierry Denoeux, Khaled Mellouli, Amel Ben Yaghlane, 2001, ECSQARU.

Fusion of pairwise nearest-neighbor classifiers based on pairwise-weighted distance metric and Dempster-Shafer theory

Quan Pan, Thierry Denoeux, Lianmeng Jiao, 2014, 17th International Conference on Information Fusion (FUSION).

CECM: Adding pairwise constraints to evidential clustering

Thierry Denoeux, Violaine Antoine, Benjamin Quost, 2010, International Conference on Fuzzy Systems.

Collaborative Evidential Clustering

Thierry Denoeux, Shoumei Li, Yixuan Qiao, 2019, IFSA/NAFIPS.

A double-copula stochastic frontier model with dependent error components and correction for sample selection

Thierry Denoeux, Songsak Sriboonchitta, Jianxu Liu, 2017, Int. J. Approx. Reason..

RECM: Relational evidential c-means algorithm

Thierry Denoeux, Marie-Hélène Masson, T. Denoeux, 2009, Pattern Recognit. Lett..

Robust Cancer Treatment Outcome Prediction Dealing with Small-Sized and Imbalanced Data from FDG-PET Images

Thierry Denoeux, Chunfeng Lian, Su Ruan, 2016, MICCAI.

CECM: Constrained evidential C-means algorithm

Thierry Denoeux, Violaine Antoine, Benjamin Quost, 2012, Comput. Stat. Data Anal..

Refined modeling of sensor reliability in the belief function framework using contextual discounting

Thierry Denoeux, Benjamin Quost, David Mercier, 2008, Inf. Fusion.

Learning from partially supervised data using mixture models and belief functions

Thierry Denoeux, Patrice Aknin, Etienne Côme, 2009, Pattern Recognit..

The Evidence-Theoretic k-NN Rule for Rank-Ordered Data: Application to Predict an Individual's Source of Loan

Thierry Denoeux, Songsak Sriboonchitta, Supanika Leurcharusmee, 2014, Belief Functions.

Editing training data for multi-label classification with the k-nearest neighbor rule

Thierry Denoeux, Fahed Abdallah, Kifah Tout, 2016, Pattern Analysis and Applications.

Purifying training data to improve performance of multi-label classification algorithms

Thierry Denoeux, Fahed Abdallah, Sawsan Kanj, 2012, 2012 15th International Conference on Information Fusion.

Belief functions induced by random fuzzy sets: Application to statistical inference pdf

Thierry Denoeux, T. Denoeux, 2020, ArXiv.

A Distributed Rough Evidential K-NN Classifier: Integrating Feature Reduction and Classification

Thierry Denoeux, Qinghua Hu, Zhi-gang Su, 2021, IEEE Transactions on Fuzzy Systems.

Multi-label classification algorithm derived from K-nearest neighbor rule with label dependencies

Thierry Denoeux, Fahed Abdallah, Zoulficar Younes, 2008, 2008 16th European Signal Processing Conference.

Analysis of evidence-theoretic decision rules for pattern classification

Thierry Denoeux, T. Denoeux, 1997, Pattern Recognit..

Knowledge and Systems Engineering - Proceedings of the Fifth International Conference, KSE 2013, Volume 1, Hanoi, Vietnam, 17-19 October, 2013

Thierry Denoeux, Van-Nam Huynh, Son Bao Pham, 2014, KSE.

An Interval-Valued Utility Theory for Decision Making with Dempster-Shafer Belief Functions

Thierry Denoeux, Prakash P. Shenoy, P. P. Shenoy, 2019, Int. J. Approx. Reason..

Information Fusion on Oversegmented Images: An Application for Urban Scene Understanding

Huijing Zhao, Thierry Denoeux, Franck Davoine, 2013, MVA.

An Adaptive k-NN Rule Based on Dempster-Shafer Theory

Thierry Denoeux, Lalla Merieme Zouhal, 1995, CAIP.

Partially-supervised learning in Independent Factor Analysis

Thierry Denoeux, Patrice Aknin, Etienne Côme, 2009, ESANN.

Initializing back propagation networks with prototypes

Thierry Denoeux, Régis Lengellé, R. Lengellé, 1993, Neural Networks.

An hybrid neural network based system for optimization of coagulant dosing in a water treatment plant

Thierry Denoeux, Nicolas Valentin, F. Fotoohi, 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

BPEC: Belief-Peaks Evidential Clustering

Thierry Denoeux, Zhi-gang Su, T. Denoeux, 2019, IEEE Transactions on Fuzzy Systems.

Adapting a Combination Rule to Non-Independent Information Sources

Thierry Denoeux, Benjamin Quost, Marie-Hélène Masson, 2008, IPMU 2008.

Using Dempster-Shafer theory to model uncertainty in climate change and environmental impact assessments

Thierry Denoeux, Nadia Ben Abdallah, Nassima Mouhous Voyneau, 2013, Proceedings of the 16th International Conference on Information Fusion.

Distributed data fusion in the dempster-shafer framework

Thierry Denoeux, Orakanya Kanjanatarakul, 2017, 2017 12th System of Systems Engineering Conference (SoSE).

Prediction of future observations using belief functions: A likelihood-based approach

Thierry Denoeux, Songsak Sriboonchitta, Orakanya Kanjanatarakul, 2016, Int. J. Approx. Reason..

Conditioning in Dempster-Shafer Theory: Prediction vs. Revision

Thierry Denoeux, Didier Dubois, D. Dubois, 2012, Belief Functions.

Analysis of radar images for rainfall forecasting using neural networks

Thierry Denoeux, P. Rizand, T. Denoeux, 1995, Neural Computing & Applications.

An Axiomatic Utility Theory for Dempster-Shafer Belief Functions

Thierry Denoeux, Prakash P. Shenoy, P. P. Shenoy, 2019, ISIPTA.

A Neuro-Fuzzy model for missing data reconstruction

Thierry Denoeux, Simon Petitrenaud, 1998 .

Fault diagnosis of a railway device using semi-supervised independent factor analysis with mixing constraints

Thierry Denoeux, Patrice Aknin, Etienne Côme, 2011, Pattern Analysis and Applications.

An evidential classifier based on feature selection and two-step classification strategy

Thierry Denoeux, Chunfeng Lian, Su Ruan, 2015, Pattern Recognit..

40 years of Dempster-Shafer theory

Thierry Denoeux, T. Denoeux, 2016, Int. J. Approx. Reason..

Knowledge and Systems Engineering - Proceedings of the Fifth International Conference KSE 2013, Volume 2

Thierry Denoeux, Van-Nam Huynh, Son Bao Pham, 2014, KSE.

Estimation and Prediction Using Belief Functions: Application to Stochastic Frontier Analysis

Thierry Denoeux, Songsak Sriboonchitta, Orakanya Kanjanatarakul, 2015, Econometrics of Risk.

Reasoning with imprecise belief structures

Thierry Denoeux, T. Denoeux, 1999, Int. J. Approx. Reason..

Using Imprecise and Uncertain Information to Enhance the Diagnosis of a Railway Device

Thierry Denoeux, Zohra Leila Cherfi, Patrice Aknin, 2011, NL-MUA.

General Correction Mechanisms for Weakening or Reinforcing Belief Functions

Thierry Denoeux, Marie-Hélène Masson, David Mercier, 2006, 2006 9th International Conference on Information Fusion.

Modeling dependence between error components of the stochastic frontier model using copula: Application to intercrop coffee production in Northern Thailand

Thierry Denoeux, Songsak Sriboonchitta, Jianxu Liu, 2015, Int. J. Approx. Reason..

Evidential combination of pedestrian detectors

Thierry Denoeux, Franck Davoine, Philippe Xu, 2014, BMVC.

Mixture Model Estimation with Soft Labels

Thierry Denoeux, Patrice Aknin, Etienne Côme, 2008, SMPS.

Editorial: Opening up computer science

Thierry Denoeux, T. Denoeux, 2019, Array.

Evidential calibration of binary SVM classifiers

Hongbin Zha, Thierry Denoeux, Franck Davoine, 2016, Int. J. Approx. Reason..

Frequency-calibrated belief functions: Review and new insights

Thierry Denoeux, Shoumei Li, Shoumei Li, 2018, Int. J. Approx. Reason..

Rejoinder on "Likelihood-based belief function: Justification and some extensions to low-quality data"

Thierry Denoeux, T. Denoeux, 2014, Int. J. Approx. Reason..

Integrated Uncertainty in Knowledge Modelling and Decision Making

Thierry Denoeux, Bao Nguyen Le, 2016, Lecture Notes in Computer Science.

Thierry Denoeux, Chunfeng Lian, Su Ruan, 2016, IEEE Transactions on Fuzzy Systems.

Editorial

Piero P. Bonissone, Thierry Denoeux, P. Bonissone, 2005, Int. J. Approx. Reason..

Combining Statistical and Expert Evidence within the D-S Framework: Application to Hydrological Return Level Estimation

Thierry Denoeux, Nadia Ben Abdallah, Nassima Mouhous Voyneau, 2012, Belief Functions.

Maximum Likelihood Estimation from Uncertain Data in the Belief Function Framework

Thierry Denoeux, T. Denoeux, 2013, IEEE Transactions on Knowledge and Data Engineering.

Evidential clustering of large dissimilarity data

Thierry Denoeux, Songsak Sriboonchitta, Orakanya Kanjanatarakul, 2016, Knowl. Based Syst..

A Multiple-Hypothesis Map-Matching Method Suitable for Weighted and Box-Shaped State Estimation for Localization

Thierry Denoeux, Ghalia Nassreddine, Fahed Abdallah, 2011, IEEE Transactions on Intelligent Transportation Systems.

Application of E 2 M Decision Trees to Rubber Quality Prediction

Thierry Denoeux, Sébastien Destercke, Nicolas Sutton-Charani, 2014, IPMU.

Multisensor data fusion for OD matrix estimation

Thierry Denoeux, Marie-Hélène Masson, Sophie Midenet, 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.

MACHINE LEARNING AS A DECISION SUPPORT TOOL FOR WASTEWATER TREATMENT PLANT OPERATION

Thierry Denoeux, Abel Dembele, Pascal Blanc, 2019, Water Resources Management X.

Self-stabilizing Distributed Data Fusion

Véronique Berge-Cherfaoui, Thierry Denoeux, Bertrand Ducourthial, 2012, SSS.

A k-nearest neighbor classification rule based on Dempster-Shafer theory

Thierry Denoeux, T. Denoeux, 1995, IEEE Trans. Syst. Man Cybern..

Decision fusion for postal address recognition using belief functions

Thierry Denoeux, Marie-Hélène Masson, David Mercier, 2009, Expert Syst. Appl..

A Dependent Multilabel Classification Method Derived from the k-Nearest Neighbor Rule

Hichem Snoussi, Thierry Denoeux, Fahed Abdallah, 2011, EURASIP J. Adv. Signal Process..

Constrained interval-valued linear regression model

Thierry Denoeux, Feng Li, Shoumei Li, 2017, 2017 20th International Conference on Information Fusion (Fusion).

Distributed Data Fusion for Detecting Sybil Attacks in VANETs

Véronique Berge-Cherfaoui, Thierry Denoeux, Nicole El Zoghby, 2012, Belief Functions.

The Classifier Chain Generalized Maximum Entropy Model for Multi-label Choice Problems

Thierry Denoeux, Songsak Sriboonchitta, Jirakom Sirisrisakulchai, 2015, Econometrics of Risk.

Searching for the Most Plausible Partition: an Evidential Reasoning Approach to Clustering Orakanya Kanjanatarakul

Thierry Denoeux, Heudiasyc Umr, 2015 .

Evidential Editing K-Nearest Neighbor Classifier

Quan Pan, Thierry Denoeux, Lianmeng Jiao, 2015, ECSQARU.

Nonparametric regression analysis of uncertain and imprecise data using belief functions

Thierry Denoeux, Simon Petitrenaud, T. Denoeux, 2004, Int. J. Approx. Reason..

Covid-19 Classification with Deep Neural Network and Belief Functions pdf

Thierry Denoeux, Su Ruan, Ling Huang, 2021, BIBE.

Multistep Prediction using Point-Cloud Approximation of Continuous Belief Functions

Thierry Denoeux, Orakanya Kanjanatarakul, 2019, 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

Approximating the combination of belief functions using the fast Mo"bius transform in a coarsened frame

Thierry Denoeux, Amel Ben Yaghlane, T. Denoeux, 2002, Int. J. Approx. Reason..

Constructing belief functions from sample data using multinomial confidence regions

Thierry Denoeux, T. Denoeux, 2006, Int. J. Approx. Reason..

A neural network-based software sensor for coagulation control in a water treatment plant

Thierry Denoeux, Nicolas Valentin, T. Denoeux, 2001, Intell. Data Anal..

Function approximation in the framework of evidence theory: a connectionist approach

Thierry Denoeux, 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

Decision-Making with Belief Functions: a Review

Thierry Denoeux, T. Denoeux, 2018, Int. J. Approx. Reason..

Multidimensional scaling of fuzzy dissimilarity data

Thierry Denoeux, Marie-Hélène Masson, T. Denoeux, 2002, Fuzzy Sets Syst..

A neural network classifier based on Dempster-Shafer theory

Thierry Denoeux, T. Denoeux, 2000, IEEE Trans. Syst. Man Cybern. Part A.

Philippe Smets (1938-2005)

Thierry Denoeux, Didier Dubois, Henri Prade, 2006, Int. J. Approx. Reason..

Refined classifier combination using belief functions

Thierry Denoeux, Benjamin Quost, Marie-Hélène Masson, 2008, 2008 11th International Conference on Information Fusion.

Ensemble clustering in the belief functions framework

Thierry Denoeux, Marie-Hélène Masson, T. Denoeux, 2011, Int. J. Approx. Reason..

Evidential grammars: A compositional approach for scene understanding. Application to multimodal street data

Thierry Denoeux, Franck Davoine, Jean-Baptiste Bordes, 2017, Appl. Soft Comput..

Partially-Hidden Markov Models

Thierry Denoeux, Noureddine Zerhouni, Emmanuel Ramasso, 2012, Belief Functions.

Parametric classification with soft labels using the evidential EM algorithm: linear discriminant analysis versus logistic regression

Thierry Denoeux, Benjamin Quost, Shoumei Li, 2017, Advances in Data Analysis and Classification.

Addendum to resample and combine: an approach to improving uncertainty representation in evidential pattern classification

Thierry Denoeux, Yves Grandvalet, Jérémie François, 2003, Inf. Fusion.

In Memoriam: Philippe Smets (1938-2005)

Thierry Denoeux, Didier Dubois, Henri Prade, 2006, Fuzzy Sets Syst..

Partially supervised Independent Factor Analysis using soft labels elicited from multiple experts: application to railway track circuit diagnosis

Thierry Denoeux, Zohra Leila Cherfi, Latifa Oukhellou, 2012, Soft Comput..

Fault diagnosis in railway track circuits using Dempster-Shafer classifier fusion

Thierry Denoeux, Patrice Aknin, Latifa Oukhellou, 2010, Eng. Appl. Artif. Intell..

Monitoring of sludge dewatering equipment by image classification

Thierry Denoeux, Yves Grandvalet, Sandro Maquine de Souza, 2004, SPIE Optics East.

Method for calculating a fuel consumption and electric power management instruction of a hybrid motor vehicle

Thierry Denoeux, Benjamin Quost, Abdel-Djalil Ourabah, 2017 .

Confidence Management in Vehicular Network

Thierry Denoeux, Véronique Cherfaoui, Zohra-Leïla Cherfi, 2009 .

Nonparametric rank-based statistics and significance tests for fuzzy data

Thierry Denoeux, Pierre-Alexandre Hébert, Marie-Hélène Masson, 2005, Fuzzy Sets Syst..

EK-NNclus: A clustering procedure based on the evidential K-nearest neighbor rule

Thierry Denoeux, Songsak Sriboonchitta, Orakanya Kanjanatarakul, 2015, Knowl. Based Syst..

Fusion of multi-tracer PET images for dose painting

Thierry Denoeux, Su Ruan, Benoît Lelandais, 2014, Medical Image Anal..

A Fuzzy-neuro system for reconstruction of multi-sensor information

Thierry Denoeux, Simon Petitrenaud, T. Denoeux, 1998 .

Evidential reasoning in large partially ordered sets

Thierry Denoeux, Marie-Hélène Masson, T. Denoeux, 2012, Ann. Oper. Res..

Fuzzy Modelling of Sensor Data for the Estimation of an Origin-Destination Matrix

Thierry Denoeux, Marie-Hélène Masson, Sophie Midenet, 2009, IFSA/EUSFLAT Conf..

Regression analysis using fuzzy evidence theory.

Thierry Denoeux, Simon Petitrenaud, 1999 .

Inferring a possibility distribution from empirical data

Thierry Denoeux, Marie-Hélène Masson, T. Denoeux, 2006, Fuzzy Sets Syst..

On Latent Belief Structures

Thierry Denoeux, Frédéric Pichon, T. Denoeux, 2007, ECSQARU.

Output coding of spatially dependent subclassifiers in evidential framework. Application to the diagnosis of railway track/vehicle transmission system

Thierry Denoeux, Patrice Aknin, Latifa Oukhellou, 2006, 2006 9th International Conference on Information Fusion.

Pairwise classifier combination using belief functions

Thierry Denoeux, Benjamin Quost, Marie-Hélène Masson, 2007, Pattern Recognit. Lett..

Fusion d'informations pour la compréhension de scènes

Thierry Denoeux, Franck Davoine, Jean-Baptiste Bordes, 2014, Traitement du Signal.

Integrated Uncertainty in Knowledge Modelling and Decision Making

Thierry Denoeux, Masahiro Inuiguchi, Van-Nam Huynh, 2015, Lecture Notes in Computer Science.

Belief Functions and Cluster Ensembles

Thierry Denoeux, Marie-Hélène Masson, T. Denoeux, 2009, ECSQARU.

Relevance and truthfulness in information correction and fusion

Thierry Denoeux, Didier Dubois, Frédéric Pichon, 2012, Int. J. Approx. Reason..

Rejoinder on "Likelihood-based belief function: Justification and some extensions to low-quality data"

Thierry Denoeux, 2014, Int. J. Approx. Reason..

Combination of Transferable Classification With Multisource Domain Adaptation Based on Evidential Reasoning

Thierry Denoeux, Kuang Zhou, Zhun-Ga Liu, 2020, IEEE Transactions on Neural Networks and Learning Systems.

Calibrated model-based evidential clustering using bootstrapping

Thierry Denoeux, 2020, Inf. Sci..

ECM: An evidential version of the fuzzy c

Thierry Denoeux, Marie-Hélène Masson, T. Denoeux, 2008, Pattern Recognit..

An evidence-theoretic neural network classifier

Thierry Denoeux, 1995, 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century.

Tumor delineation in FDG-PET images using a new evidential clustering algorithm with spatial regularization and adaptive distance metric

Thierry Denoeux, Chunfeng Lian, Su Ruan, 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).

Likelihood-based vs. distance-based evidential classifiers

Thierry Denoeux, Patrick Vannoorenberghe, P. Vannoorenberghe, 2001, 10th IEEE International Conference on Fuzzy Systems. (Cat. No.01CH37297).

Belief functions induced by random fuzzy sets: A general framework for representing uncertain and fuzzy evidence

Thierry Denoeux, T. Denoeux, 2020, Fuzzy Sets Syst..

An Evidential K-Nearest Neighbor Classifier Based on Contextual Discounting and Likelihood Maximization

Thierry Denoeux, Orakanya Kanjanatarakul, Siwarat Kuson, 2018, BELIEF.

Special issue in memory of Philippe Smets (1938-2005)

Thierry Denoeux, T. Denoeux, 2008, Int. J. Approx. Reason..

Belief rule-based classification system: Extension of FRBCS in belief functions framework

Quan Pan, Yan Liang, Thierry Denoeux, 2015, Inf. Sci..

Clustering interval-valued proximity data using belief functions

Thierry Denoeux, Marie-Hélène Masson, 2004, Pattern Recognit. Lett..

Learning from data with uncertain labels by boosting credal classifiers

Thierry Denoeux, Benjamin Quost, T. Denoeux, 2009, U '09.

Joint Feature Transformation and Selection Based on Dempster-Shafer Theory

Thierry Denoeux, Chunfeng Lian, Su Ruan, 2016, IPMU.

Selecting radiomic features from FDG-PET images for cancer treatment outcome prediction

Thierry Denoeux, Chunfeng Lian, Su Ruan, 2016, Medical Image Anal..

Combining clusterings in the belief function framework

Thierry Denoeux, Feng Li, Shoumei Li, 2020, Array.

Dempster-Shafer Reasoning in Large Partially Ordered Sets: Applications in Machine Learning

Thierry Denoeux, Marie-Hélène Masson, T. Denoeux, 2010, IUM.

Interval-Valued Linear Model

Thierry Denoeux, Shoumei Li, Xun Wang, 2015, Int. J. Comput. Intell. Syst..

Evidential Grammars for Image Interpretation - Application to Multimodal Traffic Scene Understanding

Thierry Denoeux, Franck Davoine, Jean-Baptiste Bordes, 2013, IUKM.

Classification Trees Based on Belief Functions

Thierry Denoeux, Sébastien Destercke, Nicolas Sutton-Charani, 2012, Belief Functions.

Quantifying Predictive Uncertainty Using Belief Functions: Different Approaches and Practical Construction

Thierry Denoeux, T. Denoeux, 2018, Predictive Econometrics and Big Data.

Extending stochastic ordering to belief functions on the real line

Thierry Denoeux, T. Denoeux, Thierry Denoeux, 2009, Inf. Sci..

Information fusion and evidential grammars for object class segmentation

Huijing Zhao, Thierry Denoeux, Franck Davoine, 2013 .

Noiseless Independent Factor Analysis with Mixing Constraints in a Semi-supervised Framework. Application to Railway Device Fault Diagnosis

Thierry Denoeux, Patrice Aknin, Etienne Côme, 2009, ICANN.

Distributed combination of belief functions

Thierry Denoeux, 2021, Inf. Fusion.

Making Use of Partial Knowledge About Hidden States in HMMs: An Approach Based on Belief Functions

Thierry Denoeux, Emmanuel Ramasso, T. Denoeux, 2014, IEEE Transactions on Fuzzy Systems.

Beyond Fuzzy, Possibilistic and Rough: An Investigation of Belief Functions in Clustering

Thierry Denoeux, Orakanya Kanjanatarakul, T. Denoeux, 2016, SMPS.

Identification of elastic properties in the belief function framework

Thierry Denoeux, Pierre Feissel, Liqi Sui, 2018, Int. J. Approx. Reason..

Identification of Elastic Properties Based on Belief Function Inference

Thierry Denoeux, Pierre Feissel, Liqi Sui, 2016, BELIEF.

EGMM: an Evidential Version of the Gaussian Mixture Model for Clustering pdf

Thierry Denoeux, Zhun-ga Liu, Quan Pan, 2020, ArXiv.

Contextual Discounting of Belief Functions

Thierry Denoeux, Benjamin Quost, David Mercier, 2005, ECSQARU.

Semi-supervised Feature Extraction Using Independent Factor Analysis

Thierry Denoeux, Patrice Aknin, Etienne Côme, 2010, 2011 10th International Conference on Machine Learning and Applications and Workshops.

Evidential Multi-Label Classification Approach to Learning from Data with Imprecise Labels

Thierry Denoeux, Fahed Abdallah, Zoulficar Younes, 2010, IPMU.

An Evidence-Theoretic k-Nearest Neighbor Rule for Multi-label Classification

Thierry Denoeux, Fahed Abdallah, Zoulficar Younes, 2009, SUM.

The Unnormalized Dempster’s Rule of Combination: A New Justification from the Least Commitment Principle and Some Extensions

Thierry Denoeux, Frédéric Pichon, T. Denoeux, 2010, Journal of Automated Reasoning.

k-EVCLUS: Clustering Large Dissimilarity Data in the Belief Function Framework

Thierry Denoeux, Songsak Sriboonchitta, Orakanya Kanjanatarakul, 2016, BELIEF.

Uncertainty Modelling in Data Science, SMPS 2018, Compiègne, France, 17-21 September 2018

Thierry Denoeux, Sébastien Destercke, Olgierd Hryniewicz, 2019, International Conference on Soft Methods in Probability and Statistics.

Clustering Fuzzy Data Using the Fuzzy EM Algorithm

Thierry Denoeux, Benjamin Quost, 2010, SUM.

Evidential multinomial logistic regression for multiclass classifier calibration

Thierry Denoeux, Franck Davoine, Philippe Xu, 2015, 2015 18th International Conference on Information Fusion (Fusion).

Risk Assessment in Drinking Water Production Using Belief Functions

Thierry Denoeux, Sabrina Démotier, Paul Walter Schön, 2003, ECSQARU.

Principal component analysis of fuzzy data using autoassociative neural networks

Thierry Denoeux, Marie-Hélène Masson, T. Denoeux, 2004, IEEE Transactions on Fuzzy Systems.

Map matching algorithm using belief function theory

Thierry Denoeux, Ghalia Nassreddine, Fahed Abdallah, 2008, 2008 11th International Conference on Information Fusion.

Logistic Regression Revisited: Belief Function Analysis

Thierry Denoeux, 2018, BELIEF.

Recycling experiments for sludge monitoring in waste water treatment

Thierry Denoeux, Yves Grandvalet, Sandro Glaucio Maquiné de Souza, 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

An evidence-theoretic k-NN rule with parameter optimization

Thierry Denoeux, Lalla Merieme Zouhal, T. Denoeux, 1998, IEEE Trans. Syst. Man Cybern. Part C.

Belief Functions: Theory and Applications

Thierry Denoeux, Sébastien Destercke, Fabio Cuzzolin, 2018, Lecture Notes in Computer Science.

Learning Decision Trees from Uncertain Data with an Evidential EM Approach

Thierry Denoeux, Sébastien Destercke, Nicolas Sutton-Charani, 2013, 2013 12th International Conference on Machine Learning and Applications.

Training and Evaluating Classifiers from Evidential Data: Application to E 2 M Decision Tree Pruning

Thierry Denoeux, Sébastien Destercke, Nicolas Sutton-Charani, 2014, Belief Functions.

A Hybrid Belief Rule-Based Classification System Based on Uncertain Training Data and Expert Knowledge

Quan Pan, Thierry Denoeux, Lianmeng Jiao, 2016, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

Fuzzy multi-label learning under veristic variables

Thierry Denoeux, Fahed Abdallah, Zoulficar Younes, 2010, International Conference on Fuzzy Systems.

Evidential Multi-label Classification Using the Random k-Label Sets Approach

Thierry Denoeux, Fahed Abdallah, Sawsan Kanj, 2012, Belief Functions.

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.)

Thierry Denoeux, 2005, Fuzzy Sets Syst..

Evidential Logistic Regression for Binary SVM Classifier Calibration

Thierry Denoeux, Franck Davoine, Philippe Xu, 2014, Belief Functions.

Deep PET/CT fusion with Dempster-Shafer theory for lymphoma segmentation

Thierry Denoeux, Su Ruan, Pierre Decazes, 2021, MLMI@MICCAI.

Belief functions induced by random fuzzy sets: A general framework for representing uncertain and fuzzy evidence

Thierry Denoeux, 2021, Fuzzy Sets Syst..

Evidential segmentation of 3D PET/CT images

Thierry Denoeux, Su Ruan, Pierre Decazes, 2021, BELIEF.

Evidential fully convolutional network for semantic segmentation

Thierry Denoeux, Zheng Tong, Philippe Xu, 2021, Applied Intelligence.

Belief Function-Based Semi-Supervised Learning For Brain Tumor Segmentation

Thierry Denoeux, Su Ruan, Ling Huang, 2021, 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI).

A trivariate Gaussian copula stochastic frontier model with sample selection

Thierry Denoeux, Songsak Sriboonchitta, Aree Wiboonpongse, 2021, Int. J. Approx. Reason..

Fusion of evidential CNN classifiers for image classification

Thierry Denoeux, Zheng Tong, Philippe Xu, 2021, BELIEF.

An evidential classifier based on Dempster-Shafer theory and deep learning

Thierry Denoeux, Zheng Tong, Philippe Xu, 2021, Neurocomputing.

NN-EVCLUS: Neural Network-based Evidential Clustering

Thierry Denoeux, 2021, Inf. Sci..

Clustering acoustic emission data streams with sequentially appearing clusters using mixture models pdf

Thierry Denoeux, Gaël Chevallier, Emmanuel Ramasso, 2021, Mechanical Systems and Signal Processing.

Partial classification in the belief function framework

Thierry Denoeux, Liyao Ma, T. Denoeux, 2021, Knowl. Based Syst..

Track Circuit automatic diagnosis based on a local electrical modelling

Thierry Denoeux, Patrice Aknin, Latifa Oukhellou, 2006 .

Risk assessment based on weak information using belief functions: a case study in water treatment

Thierry Denoeux, Sabrina Démotier, Paul Walter Schön, 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

Construction of predictive be-lief functions using a frequentist approach

Thierry Denoeux, 2006 .