  Synthetic Minority Oversampling Technique (SMOTE) is a common rebalancing
strategy for handling imbalanced tabular data sets. However, few works analyze
SMOTE theoretically. In this paper, we prove that SMOTE (with default
parameter) simply copies the original minority samples asymptotically. We also
prove that SMOTE exhibits boundary artifacts, thus justifying existing SMOTE
variants. Then we introduce two new SMOTE-related strategies, and compare them
with state-of-the-art rebalancing procedures. Surprisingly, for most data sets,
we observe that applying no rebalancing strategy is competitive in terms of
predictive performances, with tuned random forests. For highly imbalanced data
sets, our new method, named Multivariate Gaussian SMOTE, is competitive.
Besides, our analysis sheds some lights on the behavior of common rebalancing
strategies, when used in conjunction with random forests.
