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

Fares GRINA

Ph.D. student
Member of the research themes:
Contact information:

    Revue Internationale avec Comité de Lecture

    2023
    International journal with review committee
    DOI
    Fares GRINA -- Zied ELOUEDI -- Eric LEFEVRE
    Re-sampling of multi-class imbalanced data using belief function theory and ensemble learning
    International Journal of Approximate Reasoning, pp 1-15, Vol. 156, 05/2023

    Conférence Internationale avec Comité de Lecture

    2023
    International conference with review committee
    Fares GRINA -- Zied ELOUEDI -- Eric LEFEVRE
    Evidential genrative adversarial networks for handling imbalanced learning
    17th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2023, Arras, France, September 19-22, 09/2023
    2022
    International conference with review committee
    DOI
    Fares GRINA -- Zied ELOUEDI -- Eric LEFEVRE
    Learning from Imbalanced Data Using an Evidential Undersampling-Based Ensemble
    15th International Conference on Scalable Uncertainty Management, SUM 2022, pp 235-248, Paris, France, October 17-19 2022, 10/2022
    2022
    International conference with review committee
    DOI
    Fares GRINA -- Zied ELOUEDI -- Eric LEFEVRE
    Evidential Hybrid Re-sampling for Multi-class Imbalanced Data
    International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2022, pp 612-623, Milan, Italy, July 11-15, 2022, 07/2022
    2021
    International conference with review committee
    DOI
    Fares GRINA -- Zied ELOUEDI -- Eric LEFEVRE
    Evidential Undersampling Approach for Imbalanced Datasets with Class-Overlapping and Noise
    18th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2021, pp 181-192, Umeå, Sweden, September 27 - 30 2021, 09/2021
    2021
    International conference with review committee
    DOI
    Fares GRINA -- Zied ELOUEDI -- Eric LEFEVRE
    Uncertainty-Aware Resampling Method for Imbalanced Classification Using Evidence Theory
    16th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2021, pp 342-353, Prague, Czechia, September 21-24 2021, 09/2021
    2020
    International conference with review committee
    Fares GRINA -- Zied ELOUEDI -- Eric LEFEVRE
    A preprocessing approach for class-imbalanced data using SMOTE an Belief function theory
    International intelligent data engineering and automated learning, IDEAL'2020, pp 3-11, 11/2020

    Conférence Nationale avec Comité de Lecture

    2022
    French conference with review committee
    Fares GRINA -- Zied ELOUEDI -- Eric LEFEVRE
    Déséquilibre multi-classes : une approche évidentielle de rééchantillonnage hybride
    31e Rencontres Francophones sur la Logique Floue et ses Applications, LFA 2022, pp 255-262, Toulouse, France, 20 et 21 octobre 2022, 10/2022

    Ph.D. topic: "Dealing with imbalanced classification using belief function theory"

    2022

    In supervised learning, rare events are difficult to identify due to their infrequency in the training data. This is a huge issue since misclassifying rare events can be very costly. For example, failing to detect a serious financial fraudulent transaction can cause huge losses to an organization. This type of situation can be referred to as an imbalanced data classification problem, where one of the classes represents a very small minority of the data and the most prevalent class is called the majority class. To tackle the imbalance issue, many approaches have been proposed over the years to cope with imbalanced data classification, most of which have been based on data re-sampling, algorithm adaptions, cost-sensitive learning, ensemble learning, as well as different combinations of these approaches.

    Despite the attraction that imbalanced data is receiving in literature, there are still many issues relevant to this topic that are not yet addressed. Uncertainty is one of those issues, which is a common problem in supervised learning. In fact, it can be found in every stage of the learning process, from data preprocessing to model selection. In imbalanced classification, we can distinguish different sources of uncertainties. To deal with them, this thesis will focus on the development of belief function theory based methods to tackle challenging class-imbalanced problems.