  This paper evaluates six strategies for mitigating imbalanced data:
oversampling, undersampling, ensemble methods, specialized algorithms, class
weight adjustments, and a no-mitigation approach referred to as the baseline.
These strategies were tested on 58 real-life binary imbalanced datasets with
imbalance rates ranging from 3 to 120. We conducted a comparative analysis of
10 under-sampling algorithms, 5 over-sampling algorithms, 2 ensemble methods,
and 3 specialized algorithms across eight different performance metrics:
accuracy, area under the ROC curve (AUC), balanced accuracy, F1-measure,
G-mean, Matthew's correlation coefficient, precision, and recall. Additionally,
we assessed the six strategies on altered datasets, derived from real-life
data, with both low (3) and high (100 or 300) imbalance ratios (IR).
  The principal finding indicates that the effectiveness of each strategy
significantly varies depending on the metric used. The paper also examines a
selection of newer algorithms within the categories of specialized algorithms,
oversampling, and ensemble methods. The findings suggest that the current
hierarchy of best-performing strategies for each metric is unlikely to change
with the introduction of newer algorithms.
