Generalized Oversampling for Learning from Imbalanced datasets and Associated Theory
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 in
classification and regression. This general approach encompasses two large
families of synthetic oversampling: those based on perturbations, such as
Gaussian Noise, and those based on interpolations, such as SMOTE. It also
provides an explicit form of these machine learning algorithms and an
expression of their conditional densities, in particular for SMOTE. New
synthetic data generators are deduced. We apply GOLIATH in imbalanced
regression combining such generator procedures with a wild-bootstrap resampling
technique for the target values. We evaluate the performance of the GOLIATH
algorithm in imbalanced regression situations. We empirically evaluate and
compare our approach and demonstrate significant improvement over existing
state-of-the-art techniques.