Laboratoire de Génie Informatique et d’Automatique de l’Artois

Sebastien RAMEL

Associate professor
Member of the research themes:
Contact information:
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Revue Internationale avec Comité de Lecture

International journal with review committee
Sebastien RAMEL -- Frédéric PICHON -- François DELMOTTE
A reliable version of choquistic regression based on evidence theory
Knowledge-Based Systems, KBS, pp 106252, Vol. 205, 10/2020

Conférence Internationale avec Comité de Lecture

International conference with review committee
Sebastien RAMEL -- Frédéric PICHON -- François DELMOTTE
Active evidential calibration of binary SVM classifiers
Belief Functions: Theory and Applications, Proc. of the 5th International Conference, BELIEF 2018, pp 208-216, Volume 11069 of Lecture Notes in Computer Science, Compiègne, France, F. Cuzzolin, S. Destercke, T. Denœux, A. Martin, Springer, 09/2018

Conférence Nationale avec Comité de Lecture

Sebastien RAMEL -- Frédéric PICHON -- François DELMOTTE
Étalonnage évidentiel actif de classifieurs SVM
Actes des 27e Rencontres Francophones sur la Logique Floue et ses Applications, LFA 2018, pp 93-100, (Prix du meilleur papier doctorant), Arras, France, Cepadues, 11/2018

Author of the Ph.D. thesis "Evidential logistic regression: application to active classifier calibration and choquistic extension"

2016 - 2020

Logistic regression is a well-established classification model, whose monotonicity has contributed to its popularity. However, it has at least two limitations. First, it lacks self-awareness, that is, an ability to represent the ignorance (aka epistemic or reducible uncertainty) involved in its predictions, which is crucial in safety-critical classification problems. Recently, an extension of logistic regression was introduced to remedy this issue and was applied to the problem of classifier calibration. This extension is formalised within evidence theory and relies in particular on a sound method for statistical inference and prediction developed in this framework. The first contribution of this thesis is to study the interest of this extension for active learning in the context of classifier calibration. An uncertainty sampling strategy based on ignorance is proposed and validated experimentally. A second limitation of logistic regression is that it lacks flexibility, that is, an ability to model nonlinear dependencies between the predictors. To address this issue, an elegant generalisation of logistic regression based on the Choquet
integral, called choquistic regression, was proposed. It preserves the monotonicity of logistic regression whilst alleviating its linearity. However, much as logistic regression, it lacks self-awareness. The second contribution of this thesis is to palliate this problem by deriving an extension of choquistic regression based on evidence theory, similar to the evidential extension of logistic regression. The usefulness of the obtained approach is confirmed empirically in classification problems where cautiousness in decision-making is allowed.

As part of the ELSAT2020 (VUMOPE) regional project, this thesis is partially funded by the ELSAT2020 project, which is co-financed by the European Union with the European Regional Development Fund, the French State and the Hauts-de-France Regional Council.


2015 - 2022

Vers Une MObilité Propre et Efficace

Summary :

L’objectif de ce projet est de repenser les recherches sur l’amélioration des performances des véhicules en intégrant la complexité des usages, par exemples les comportements des conducteurs.