World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
58
Citations
11500
World Ranking
2519
National Ranking
38

Overview

Thierry Denoeux is affiliated with the University of Technology of Compiègne in France and has a substantial body of research focused on computer science, particularly in areas bridging artificial intelligence, machine learning, and approximate reasoning. Their work intersects multiple subfields including Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Management Science and Operations Research, and Statistics and Probability.

Themes central to Denoeux's research include Multi-Criteria Decision Making, Advanced Clustering Algorithms Research, Fuzzy Systems and Optimization, Radiomics and Machine Learning in Medical Imaging, Bayesian Modeling and Causal Inference, Fuzzy Logic and Control Systems, and Medical Image Segmentation Techniques.

Several recent publications highlight these research interests:

  • "An evidential classifier based on Dempster-Shafer theory and deep learning" (2021), Neurocomputing
  • "Combination of Transferable Classification With Multisource Domain Adaptation Based on Evidential Reasoning" (2020), IEEE Transactions on Neural Networks and Learning Systems
  • "Partial classification in the belief function framework" (2021), Knowledge-Based Systems
  • "Lymphoma segmentation from 3D PET-CT images using a deep evidential network" (2022), International Journal of Approximate Reasoning
  • "Belief functions and rough sets: Survey and new insights" (2022), International Journal of Approximate Reasoning

Denoeux frequently collaborates with several researchers, including:

  • Ling Huang
  • Su Ruan
  • Pierre Decazes
  • Xiaodong Yue
  • Davide Ciucci

The majority of their publications appear in venues such as:

  • arXiv (Cornell University)
  • International Journal of Approximate Reasoning
  • Fuzzy Sets and Systems
  • Information Fusion
  • Information Sciences

Denoeux has also contributed to the book literature with works published by Springer Science+Business Media, including two editions titled "Belief Functions: Theory and Applications" released in 2021 and 2022.

Overall, the scholar's research contributions revolve around computational methods related to belief functions, evidential reasoning, and their applications across machine learning and medical imaging domains, reflected in an extensive publication record and collaborative network.

Best Publications

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

    Unknown

  • A neural network classifier based on Dempster-Shafer theory

    Unknown

  • ECM: An evidential version of the fuzzy c

    Unknown

  • An evidence-theoretic k-NN rule with parameter optimization

    Unknown

  • EVCLUS: evidential clustering of proximity data

    Unknown

  • Maximum Likelihood Estimation from Uncertain Data in the Belief Function Framework

    Unknown

  • Decision-Making with Belief Functions: a Review

    Unknown

  • Analysis of evidence-theoretic decision rules for pattern classification

    Unknown

  • Classification Using Belief Functions: Relationship Between Case-Based and Model-Based Approaches

    T. Denoeux;P. Smets

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

    Unknown

  • Handling possibilistic labels in pattern classification using evidential reasoning

    Unknown

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

    Unknown

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

    Unknown

  • Initializing back propagation networks with prototypes

    Unknown

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

    Unknown

  • Inferring a possibility distribution from empirical data

    Unknown

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

    Unknown

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

    Unknown

  • Maximum likelihood estimation from fuzzy data using the EM algorithm

    Unknown

  • Belief rule-based classification system

    Lianmeng Jiao;Quan Pan;Thierry Denœux;Yan Liang

  • Modeling vague beliefs using fuzzy-valued belief structures

    Unknown

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

    Lianmeng Jiao;Thierry Denoeux;Quan Pan

  • Multimodal information fusion for urban scene understanding

    Philippe Xu;Franck Davoine;Jean-Baptiste Bordes;Huijing Zhao

  • Dissimilarity Metric Learning in the Belief Function Framework

    Chunfeng Lian;Su Ruan;Thierry Denoux

  • Map matching algorithm using belief function theory

    G. Nassreddine;F. Abdallah;T. Denoeux

  • Distributed data fusion: application to confidence management in vehicular networks

    V. Cherfaoui;T. Denoeux;Z.L. Cherfi

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

    Ling Huang;Su Ruan;Thierry Denoux

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

    Ling Huang;Su Ruan;Thierry Denoeux

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

    Chunfeng Lian;Chunfeng Lian;Su Ruan;Thierry Denœux;Hua Li

  • Outcome prediction in tumour therapy based on Dempster-Shafer theory

    Chunfeng Lian;Su Ruan;Thierry Denoux;Pierre Vera

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

    S. Demotier;W. Schon;T. Denoeux;K. Odeh

  • Deep PET/CT Fusion with Dempster-Shafer Theory for Lymphoma Segmentation.

    Ling Huang;Ling Huang;Thierry Denoeux;Thierry Denoeux;David Tonnelet;Pierre Decazes

  • A new method for state estimation of dynamic system based on Dempster Shafer theory

    Ghalia Nassreddine;Fahed Abdallah;Thierry Denoeux

  • Constructing Rule-Based Models Using the Belief Functions Framework

    Rui Jorge Almeida;Thierry Denoeux;Uzay Kaymak

  • In Memoriam: Philippe Smets (1938--2005)

    Hughes Bersini;Thierry Denœux;Didier Dubois;Henri Prade

Frequent Co-Authors

Su Ruan
Su Ruan University of Rouen

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Best Scientists Citing Thierry Denoeux

Trending Scientists

Recently Published Articles