Do we need rebalancing strategies? A theoretical and empirical study around SMOTE and its variants. - Laboratoire de Probabilités, Statistique et Modélisation Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2024

Do we need rebalancing strategies? A theoretical and empirical study around SMOTE and its variants.

Résumé

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.
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Dates et versions

hal-04438941 , version 1 (05-02-2024)
hal-04438941 , version 2 (21-05-2024)
hal-04438941 , version 3 (22-05-2024)
hal-04438941 , version 4 (31-05-2024)

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Abdoulaye Sakho, Emmanuel Malherbe, Erwan Scornet. Do we need rebalancing strategies? A theoretical and empirical study around SMOTE and its variants.. 2024. ⟨hal-04438941v2⟩
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