Selecting the suitable resampling strategy for imbalanced data classification regarding dataset properties. (arXiv:2201.07932v1 [cs.LG])

In many application domains such as medicine, information retrieval,
cybersecurity, social media, etc., datasets used for inducing classification
models often have an unequal distribution of the instances of each class. This
situation, known as imbalanced data classification, causes low predictive
performance for the minority class examples. Thus, the prediction model is
unreliable although the overall model accuracy can be acceptable. Oversampling
and undersampling techniques are well-known strategies to deal with this
problem by balancing the number of examples of each …

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