Generally, data augmentation techniques have shown effectiveness for the classification problem.
For example, SMOTE is widely used for imbalanced classification.
How about the regression problem?
Can anyone recommend any effective over-sampling approaches for the regression problem?
I've read some papers about SMOTE for regression and SMOGN.
However, I'm not sure that they can surprisingly increase the prediction performance, yet.