Learning from Imbalanced Data Using an Evidential Undersampling-Based Ensemble - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2022

Learning from Imbalanced Data Using an Evidential Undersampling-Based Ensemble

Résumé

In many real-world binary classification problems, one class tends to be heavily underrepresented when it consists of far fewer observations than the other class. This results in creating a biased model with undesirable performance. Different techniques, such as undersampling, have been proposed to fix this issue. Ensemble methods have also been proven to be a good strategy to improve the performance of the resulting model in the case of class imbalance. In this paper, we propose an evidential undersampling-based ensemble approach. To alleviate the issue of losing important data, our undersampling technique assigns soft evidential labels to each majority instance, which are later used to discard only the unwanted observations, such as noisy and ambiguous examples. Finally, to improve the final results, the proposed undersampling approach is incorporated into an evidential classifier fusion-based ensemble. The comparative study against wellknown ensemble methods reveal that our method is efficient according to the G-Mean and F-Score measures.
Fichier principal
Vignette du fichier
SUM_2022_paper_9310.pdf (1.77 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03840612 , version 1 (05-11-2022)

Identifiants

Citer

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, Oct 2022, Paris, France. pp.235-248, ⟨10.1007/978-3-031-18843-5_16⟩. ⟨hal-03840612⟩

Collections

UNIV-ARTOIS LGI2A
22 Consultations
69 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More