Generalized Oversampling for Learning from Imbalanced datasets and Associated Theory

Our paper, Generalized Oversampling for Learning from Imbalanced datasets and Associated Theory, is now online on ArXiv. 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 this paper, we propose a data augmentation procedure, the GOLIATH algorithm, based on kernel density estimates which can be used … <a …