  Imbalanced data set is a problem often found and well-studied in financial
industry. In this paper, we reviewed and compared some popular methodologies
handling data imbalance. We then applied the under-sampling/over-sampling
methodologies to several modeling algorithms on UCI and Keel data sets. The
performance was analyzed for class-imbalance methods, modeling algorithms and
grid search criteria comparison.
