Do We Need Rebalancing Strategies? A Theoretical and Empirical Study Around SMOTE and Its Variants

Abdoulaye SAKHO, Emmanuel Malherbe, Erwan Scornet
Proceedings of The 29th International Conference on Artificial Intelligence and Statistics, PMLR 300:2035-2043, 2026.

Abstract

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. Furthermore, 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. First, 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. Second, when the imbalance ratio is artificially augmented, one of our two modifications of SMOTE leads to promising predictive performances compared to SMOTE and other strategies.

Cite this Paper

BibTeX
@InProceedings{pmlr-v300-sakho26a, title = { Do We Need Rebalancing Strategies? A Theoretical and Empirical Study Around SMOTE and Its Variants }, author = {SAKHO, Abdoulaye and Malherbe, Emmanuel and Scornet, Erwan}, booktitle = {Proceedings of The 29th International Conference on Artificial Intelligence and Statistics}, pages = {2035--2043}, year = {2026}, editor = {Khan, Emtiyaz and Li, Yingzhen and Solin, Arno and Ramdas, Aaditya}, volume = {300}, series = {Proceedings of Machine Learning Research}, month = {02--05 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v300/main/assets/sakho26a/sakho26a.pdf}, url = {https://proceedings.mlr.press/v300/sakho26a.html}, abstract = { 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. Furthermore, 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. First, 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. Second, when the imbalance ratio is artificially augmented, one of our two modifications of SMOTE leads to promising predictive performances compared to SMOTE and other strategies. } }
Endnote
%0 Conference Paper %T Do We Need Rebalancing Strategies? A Theoretical and Empirical Study Around SMOTE and Its Variants %A Abdoulaye SAKHO %A Emmanuel Malherbe %A Erwan Scornet %B Proceedings of The 29th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2026 %E Emtiyaz Khan %E Yingzhen Li %E Arno Solin %E Aaditya Ramdas %F pmlr-v300-sakho26a %I PMLR %P 2035--2043 %U https://proceedings.mlr.press/v300/sakho26a.html %V 300 %X 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. Furthermore, 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. First, 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. Second, when the imbalance ratio is artificially augmented, one of our two modifications of SMOTE leads to promising predictive performances compared to SMOTE and other strategies.
APA
SAKHO, A., Malherbe, E. & Scornet, E.. (2026). Do We Need Rebalancing Strategies? A Theoretical and Empirical Study Around SMOTE and Its Variants . Proceedings of The 29th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 300:2035-2043 Available from https://proceedings.mlr.press/v300/sakho26a.html.

Related Material