Synthetic data augmentation is widely used to mitigate class imbalance, but its theoretical effects on score-based classification remain poorly understood. This paper develops a framework for characterizing when synthetic minority augmentation can improve threshold-integrated and threshold-optimized metrics, including AUROC, AUPRC, best-threshold balanced accuracy, and best-threshold \(\F_1\) score.
arXiv:2606. 26053v1 Announce Type: cross Abstract: Synthetic data augmentation is widely used to mitigate class imbalance, but its theoretical effects on score-based classification remain poorly understood.
By Zhengchi Ma, Pengfei Lyu, Anru R. Zhang
arXiv:2607. 20787v1 Announce Type: cross Abstract: For two decades, the standard remedy for class-imbalanced learning has been to fabricate synthetic minority examples, and the standard evidence of their validity has been a check that cannot fail: synthetic points are scored against the very data that generated them.
By Ahmad B. Hassanat, Ahmad S. Tarawneh, Ghada A. Altarawneh
SAGE (Subpopulation-Aware Generative Enhancement) is a two-stage generative augmentation framework designed to mitigate spurious correlations in machine learning when group labels are unavailable. It uses cluster-derived sub-labels and class labels to fine‑tune a conditional generative model and text encoder, producing synthetic data that fills underrepresented regions and creates a balanced validation set for last‑layer reweighting. Experiments show SAGE improves worst‑group accuracy to 89.5%, 85.7%, and 79.1% on Waterbirds, CelebA, and MetaShift, outperforming existing group‑label‑free baselines by up to 7.7 percentage points.
By Yiming Luo, Rongqiang Zhao, Jie Liu
arXiv:2505. 13518v3 Announce Type: replace-cross Abstract: Imbalanced datasets, where one class significantly outnumbers others, remain a persistent challenge in machine learning, often biasing predictions toward the majority class and degrading classifier performance.
By Behnam Yousefimehr, Mehdi Ghatee, Javad Fazli, Shervin Ghaffari, Zahra Rafei, Mohammad Amin Seifi, Sajed Tavakoli, Abolfazl Nikahd, Mahdi Razi Gandomani, Alireza Orouji, Ramtin Mahmoudi Kashani, Sarina Heshmati, Negin Sadat Mousavi
arXiv:2510. 09783v2 Announce Type: replace-cross Abstract: Oversampling is one of the most widely used approaches for addressing imbalanced classification.
By Dang Nguyen, Sunil Gupta, Kien Do, Thin Nguyen, Taylor Braund, Alexis Whitton, Svetha Venkatesh
The paper introduces a collaborative optimization Boosting model for multiclass imbalanced learning that integrates density and confidence factors to create a noise‑resistant weight update mechanism and a dynamic sampling strategy. The modules are tightly coupled to coordinate weight updates, sample region partitioning, and region‑guided sampling. Experiments on 40 public imbalanced datasets show the model significantly outperforms seven state‑of‑the‑art baselines.
By Chuantao Li, Zhi Li, Jiahao Xu, Jie Li, Sheng Li
The paper presents a formal framework for majority‑minority learning tasks, demonstrating that standard gradient‑descent training can amplify bias by favoring majority groups and producing stereotypical predictors that ignore minority‑specific features. It identifies three key theoretical findings: the close proximity between full‑data and stereotypical predictors, the dominance of a region where training the entire model mainly captures majority traits, and a lower bound on the extra training needed to mitigate this effect. Experiments on deep learning for tabular and image classification illustrate these results.
By Fran\c{c}ois Bachoc (LPP), J\'er\^ome Bolte (TSE-R), Ryan Boustany (TSE-R), Jean-Michel Loubes (IMT, REGALIA)
arXiv:2509. 07605v2 Announce Type: replace-cross Abstract: Class imbalance poses a significant challenge to supervised classification, particularly in critical domains like medical diagnostics and anomaly detection where minority class instances are rare.
By Ali Nawaz, Amir Ahmad, Shehroz S. Khan
arXiv:2409. 13007v3 Announce Type: replace-cross Abstract: Class imbalance poses a significant challenge in classification tasks, often causing standard learning algorithms to become biased toward the majority class.
By Asif Newaz, Asif Ur Rahman Adib, Taskeed Jabid
arXiv:2607. 09816v1 Announce Type: new Abstract: Class imbalance poses a fundamental challenge in risk-sensitive applications such as fraud detection and medical diagnosis, where minority-class samples are scarce yet critical for accurate classification.
By Yanxuan Yu, Dong liu, Renata Borovica-Gajic, Ying Nian Wu
arXiv:2501. 15790v2 Announce Type: replace Abstract: Synthetic minority oversampling is typically designed and evaluated against a predictive objective, generating samples that improve downstream classification.
By Pankaj Yadav, Vivek Vijay