Thierry Denœux

Université de Technologie de Compiègne

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Marie-Hélène Masson

Heuristics and Diagnostics for Complex Systems

Su Ruan

Fujian University of Traditional Chinese Medicine

Benjamin Quost

Heuristics and Diagnostics for Complex Systems

Philippe Xu

Science Applications International Corporation (United States)

Ling Huang

Guilin University of Technology

Sébastien Destercke

Centre National de la Recherche Scientifique

Songsak Sriboonchitta

Chiang Mai University

Didier Dubois

Université Toulouse - Jean Jaurès

Fahed Abdallah

Université de Lorraine

Franck Davoine

Heuristics and Diagnostics for Complex Systems

Cooperative Institutions

Centre National de la Recherche Scientifique

Université de Technologie de Compiègne

Heuristics and Diagnostics for Complex Systems

Sorbonne Université

Université Toulouse III - Paul Sabatier

Université Claude Bernard Lyon 1

Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes

Université de Rouen Normandie

Laboratoire d'Automatique, Génie Informatique et Signal

Japan Advanced Institute of Science and Technology

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In Memoriam: Philippe Smets (1938–2005)

Fuzzy Sets and Systems (2006)

Hughes Bersini Thierry Denœux Didier Dubois Henri Prade

10.1016/j.fss.2006.02.003

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Citations (2)

Analysis of evidence-theoretic decision rules for pattern classification

Pattern Recognition (1997)

Thierry Denœux

Decision rule

Completeness (order theory)

Training set

Decision theory

10.1016/s0031-3203(96)00137-9

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Citations (183)

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

Advances in Data Analysis and Classification (2017)

Benjamin Quost Thierry Denœux Shoumei Li

Discriminative model

Generative model

10.1007/s11634-017-0301-2

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Citations (34)

Evidential multinomial logistic regression for multiclass classifier calibration

HAL (Le Centre pour la Communication Scientifique Directe) (2015)

Philippe Xu Franck Davoine Thierry Denœux

The calibration of classifiers is an important task in information fusion. To compare or combine the outputs of several classifiers, they need to be represented in a common space. Probabilistic calibration methods transform the output of a classifier into a posterior probability distribution. In this paper, we introduce an evidential calibration method for multiclass classification problems. Our approach uses an extension of multinomial logistic regression to the theory of belief functions. We demonstrate that the use of belief functions instead of probability distributions is often beneficial. In particular, when different classifiers are trained with unbalanced amount of training data, the gain achieved by our evidential approach can become significant. We applied our method to the calibration of multiclass SVM classifiers which were constructed through a “one-vs-all” framework. Experiments were conducted using six different datasets from the UCI repository.

Probabilistic classification

Multinomial distribution

Multiclass classification

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On Latent Belief Structures

Lecture notes in computer science (2007)

Frédéric Pichon Thierry Denœux

Associative property

10.1007/978-3-540-75256-1_34

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Citations (6)

Influence of weight initialization on generalization performance

HAL (Le Centre pour la Communication Scientifique Directe) (1995)

M. Karouia Thierry Denœux Régis Lengelle

Initialization

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

Studies in computational intelligence (2014)

Orakanya Kanjanatarakul Nachatchapong Kaewsompong Songsak Sriboonchitta Thierry Denœux

Statistical Inference

Predictive inference

10.1007/978-3-319-13449-9_12

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Citations (3)

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

Lecture notes in computer science (2016)

Orakanya Kanjanatarakul Songsak Sriboonchitta Thierry Denœux

Sample complexity

10.1007/978-3-319-45559-4_11

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Citations (2)

Belief Functions: Theory and Applications

Lecture notes in computer science (2014)

Sébastien Destercke Thierry Denœux Fabio Cuzzolin Arnaud Martin

Belief Revision

10.1007/978-3-319-11191-9

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Citations (47)

A Distributional Approach for Soft Clustering Comparison and Evaluation

arXiv (Cornell University) (2022)

Andrea Campagner Davide Ciucci Thierry Denœux

The development of external evaluation criteria for soft clustering (SC) has received limited attention: existing methods do not provide a general approach to extend comparison measures to SC, and are unable to account for the uncertainty represented in the results of SC algorithms. In this article, we propose a general method to address these limitations, grounding on a novel interpretation of SC as distributions over hard clusterings, which we call \emph{distributional measures}. We provide an in-depth study of complexity- and metric-theoretic properties of the proposed approach, and we describe approximation techniques that can make the calculations tractable. Finally, we illustrate our approach through a simple but illustrative experiment.

10.48550/arxiv.2206.09827

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