Learning from Imbalanced Datasets with Cross-View Cooperation-Based Ensemble Methods

Cécile Capponi (1, 2) , Sokol Koço

1 LIS - Laboratoire d'Informatique et des Systèmes (LIS) (Marseille, Toulon)
2 QARMA - éQuipe d'AppRentissage de MArseille

In this paper, we address the problem of learning from im-balanced multi-class datasets in a supervised setting when multiple descriptions of the data-also called views-are available. Each view incorporates various information on the examples, and in particular, depending on the task at hand, each view might be better at recognizing only a subset of the classes. Establishing a sort-of cooperation between the views is needed for all the classes to be equally recognized-a crucial problem particularly for imbalanced datasets. The novelty of our work consists in capitalizing on the complementariness of the views so that each class can be processed by the most appropriate view(s); thus improving the per-class performances of the final classifier. The main contribution of this paper are two ensemble learning methods based on recent theoretical works on the use of the confusion matrix's norm as an error measure, while empirical results show the benefits of the proposed approaches.

Mots clés: Boosting; Multi-view Learning; Multi-class Learning; Imbalanced Learning; ensemble methods; imbalanced classes; confusion matrix norm; boosting; multi-view learning

Domaines: Intelligence artificielle [cs.AI]

Cécile Capponi, Sokol Koço. Learning from Imbalanced Datasets with Cross-View Cooperation-Based Ensemble Methods. Springer. Linking and Mining Heterogeneous and Multi-view Data, 2019, 978-3-030-01872-6. ⟨hal-02068594⟩