Abdoulaye Sakho

Abdoulaye Sakho, Emmanuel Malherbe, Erwan Scornet

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 derive several non-asymptotic upper bound on SMOTE density. From these results, we prove that SMOTE (with default parameter) tends to copy the original minority samples asymptotically. We confirm and illustrate empirically this first theoretical behavior on a real-world data-set.bFurthermore, we prove that SMOTE density vanishes near the boundary of the support of the minority class distribution. We then adapt SMOTE based on our theoretical findings to introduce two new variants. These strategies are compared on 13 tabular data sets with 10 state-of-the-art rebalancing procedures, including deep generative and diffusion models. One of our key findings is that, for most data sets, applying no rebalancing strategy is competitive in terms of predictive performances, would it be with LightGBM, tuned random forests or logistic regression. However, when the imbalance ratio is artificially augmented, one of our two modifications of SMOTE leads to promising predictive performances compared to SMOTE and other state-of-the-art strategies.

Abdoulaye Sakho, Emmanuel Malherbe, Carl-Erik Gauthier et al.

This study investigates rare event detection on tabular data within binary classification. Standard techniques to handle class imbalance include SMOTE, which generates synthetic samples from the minority class. However, SMOTE is intrinsically designed for continuous input variables. In fact, despite SMOTE-NC-its default extension to handle mixed features (continuous and categorical variables)-very few works propose procedures to synthesize mixed features. On the other hand, many real-world classification tasks, such as in banking sector, deal with mixed features, which have a significant impact on predictive performances. To this purpose, we introduce MGS-GRF, an oversampling strategy designed for mixed features. This method uses a kernel density estimator with locally estimated full-rank covariances to generate continuous features, while categorical ones are drawn from the original samples through a generalized random forest. Empirically, contrary to SMOTE-NC, we show that MGS-GRF exhibits two important properties: (i) the coherence i.e. the ability to only generate combinations of categorical features that are already present in the original dataset and (ii) association, i.e. the ability to preserve the dependence between continuous and categorical features. We also evaluate the predictive performances of LightGBM classifiers trained on data sets, augmented with synthetic samples from various strategies. Our comparison is performed on simulated and public real-world data sets, as well as on a private data set from a leading financial institution. We observe that synthetic procedures that have the properties of coherence and association display better predictive performances in terms of various predictive metrics (PR and ROC AUC...), with MGS-GRF being the best one. Furthermore, our method exhibits promising results for the private banking application, with development pipeline being compliant with regulatory constraints.