What was used before data augmentation (e.g. SMOTE) to train ML models on imbalanced data? Please provide citations

Question

I am seriously curious on how imbalance data was treated in machine learning and statistical learning before modern time data augmentation solutions such as SMOTE appeared.

Please provide citations not only opinions.