To SMOTE, or not to SMOTE?. (arXiv:2201.08528v1 [cs.LG])

Web: http://arxiv.org/abs/2201.08528

In imbalanced binary classification problems the objective metric is often
non-symmetric and associates a higher penalty with the minority samples. On the
other hand, the loss function used for training is usually symmetric - equally
penalizing majority and minority samples. Balancing schemes, that augment the
data to be more balanced before training the model, were proposed to address
this discrepancy and were shown to improve prediction performance empirically
on tabular data. However, recent studies of consistent classifiers suggest that
the …

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