Generalized Oversampling for Learning from Imbalanced datasets and Associated Theory

Our paper, Generalized Oversampling for Learning from Imbalanced datasets and Associated Theory: Application in Regression, written with Samuel Stocksieker and Denys Pommeret, has been accepted for publication in TMLR (Transactions on Machine Learning Research) In supervised learning, it is quite frequent to be confronted with real imbalanced datasets. This situation leads to a learning difficulty for standard algorithms. Research and solutions in imbalanced learning have mainly focused on classification tasks. Despite its importance, very few solutions exist for imbalanced regression. In …