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.

  • $\begingroup$ You may want to check this question and this question $\endgroup$
    – noe
    Commented Nov 10, 2023 at 10:28
  • $\begingroup$ @noe Thanks for the input and time. Much appreciated. I was aware of bias introduced by data augmentation techniques thus the origin of my question above. I will leave the question open because I need to know how the ML community trained vintage (before data augmentation) ML models on imbalance data. $\endgroup$
    – Full Array
    Commented Nov 10, 2023 at 16:37


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