Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.20472 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908607096815616 |
|---|---|
| author | Ahmad, Touqeer Kalan, Mohammadreza M. Portier, François Stupfler, Gilles |
| author_facet | Ahmad, Touqeer Kalan, Mohammadreza M. Portier, François Stupfler, Gilles |
| contents | Synthetic oversampling of minority examples using SMOTE and its variants is a leading strategy for addressing imbalanced classification problems. Despite the success of this approach in practice, its theoretical foundations remain underexplored. We develop a theoretical framework to analyze the behavior of SMOTE and related methods when classifiers are trained on synthetic data. We first derive a uniform concentration bound on the discrepancy between the empirical risk over synthetic minority samples and the population risk on the true minority distribution. We then provide a nonparametric excess risk guarantee for kernel-based classifiers trained using such synthetic data. These results lead to practical guidelines for better parameter tuning of both SMOTE and the downstream learning algorithm. Numerical experiments are provided to illustrate and support the theoretical findings |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_20472 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Concentration and excess risk bounds for imbalanced classification with synthetic oversampling Ahmad, Touqeer Kalan, Mohammadreza M. Portier, François Stupfler, Gilles Machine Learning Synthetic oversampling of minority examples using SMOTE and its variants is a leading strategy for addressing imbalanced classification problems. Despite the success of this approach in practice, its theoretical foundations remain underexplored. We develop a theoretical framework to analyze the behavior of SMOTE and related methods when classifiers are trained on synthetic data. We first derive a uniform concentration bound on the discrepancy between the empirical risk over synthetic minority samples and the population risk on the true minority distribution. We then provide a nonparametric excess risk guarantee for kernel-based classifiers trained using such synthetic data. These results lead to practical guidelines for better parameter tuning of both SMOTE and the downstream learning algorithm. Numerical experiments are provided to illustrate and support the theoretical findings |
| title | Concentration and excess risk bounds for imbalanced classification with synthetic oversampling |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.20472 |