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

Asma TRABELSI

Ph.D. student
Member of the research themes:
Contact information:
Asma TRABELSI -- Zied ELOUEDI -- Eric LEFEVRE
Comparing dependent combination rules under the belief classifier fusion framework
Soft Computing, pp 6919-6932, Vol. 21, No. 23, décembre 2017
Asma TRABELSI -- Zied ELOUEDI -- Eric LEFEVRE
Ensemble enhanced evidential k-NN classifier through random subspaces
40th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU'2017, pp 212-221, juillet 2017
Asma TRABELSI -- Zied ELOUEDI -- Eric LEFEVRE
A novel k-NN approach for data with uncertain attribute values
30th International Conference on Industrial, Engineering other Applications of Applied Intelligent Systems, IEA/AIE’2017, pp 160-170, juin 2017
2016
French conference with review committee
Asma TRABELSI -- Zied ELOUEDI -- Eric LEFEVRE
Nouvelle méthode d’arbre de décision pour le traitement des données partiellement incertaines
Actes des 25e Rencontres Francophones sur la Logique Floue et ses Applications, LFA'2016, pp 57-64, octobre 2016
Asma TRABELSI -- Zied ELOUEDI -- Eric LEFEVRE
Handling uncertain attribute values in decision tree classifier using the belief function theory
17th International Conference on Artificial Intelligence: Methodology, Systems, Applications, AIMSA'2016, pp 26-35, septembre 2016
Asma TRABELSI -- Zied ELOUEDI -- Eric LEFEVRE
Feature Selection From Partially Uncertain Data Within the Belief Function Framework
16th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2016, pp 643-655, Vol. 2, Springer, CCIS, juillet 2016
2015
International conference with review committee
Asma TRABELSI -- Zied ELOUEDI -- Eric LEFEVRE
Belief function combination: comparative study within the classifier fusion framework
1st International Conference on Advanced Intelligent Systems and Informatics, AISI 2015, pp 425-435, novembre 2015
2015
International conference with review committee
Asma TRABELSI -- Zied ELOUEDI -- Eric LEFEVRE
Classifier fusion within the belief function framework using dependent combination rules
22nd International Symposium on Methodologies for Intelligent Systems, ISMIS 2015, pp 133-138, octobre 2015

Ph.D. topic: "Classifier ensemble under the belief function framework"

2016

Ensemble learning, also referred to as ensemble classifier or multiple classifier, has been a hot topic in the fields of pattern recognition and machine learning since 1990s to solve complex classification problems. Both experimental and theoretical researches have proven that the combination of a set of accurate and diverse classifiers may achieve good performance.

The construction of an ensemble system requires two main levels. The first one consists of training a set of classifiers as base learners, while the second level concerns the integration of the constructed classifiers through some combination strategies. The generation of an optimal ensemble of classifiers has still an open question which has led numerous research directions.

It is worth noting that several real data applications suffer from noise, imprecision and incompleteness. For this reason, it is important that ensemble classifier techniques deal with the uncertainty that occurs in both construction and fusion levels. The belief function theory permits such handling as it is a general framework for reasoning under uncertainty.

The main contribution of our thesis is to build ensemble learning from uncertain data, especially where the uncertainty is represented within the belief function framework. We look forward to tackling the uncertainty in both the construction and the fusion level.