Jul 2026· International Journal of Data Science and Analysis· Vol 22· 0 citations· 45 references
TL;DR
This study introduces a novel approach, named SDCGAN, which oversamples with a Conditional Generative Adversarial Network (CGAN) where the generator is fed with strengthened distribution information extracted from a curated set of minority samples.
This paper introduces a modified Generative Adversarial Network (MGAN), which generates synthetic images of the minority class (128 × 128 × 3 pixels), thereby enhancing classifier resilience and surpasses conventional data balancing methods in medical imaging contexts.
Roaa Razaq, Ebtesam N. Alshemmary, Zhentai Lu· Iraqi Journal of Science· 0 citations
SafeVAE-GAN is proposed, a resampling framework that integrates a conditional β-Variational Autoencoder with Wasserstein GAN to generate high-quality minority samples and provides a robust and scalable solution for multi-class imbalanced learning across diverse domains.
Nhiem Ba Nguyen, Sinh Van Nguyen, B. Nguyễn· Vietnam Journal of Computer...· 0 citations
SMOG, an adaptive hybrid oversampling framework that integrates the Synthetic Minority Over-sampling Technique with a Conditional Generative Adversarial Network (GAN)-based difficulty-aware learning strategy, highlights the effectiveness of adaptive hybrid generative strategies for intelligent learning on imbalanced da...
Jatinder Kaur, Vimal Parmar, B. K. Rao et al.· Evolutionary Intelligence· 0 citations
A novel oversampling algorithm: the adaptive weighting–synthetic minority oversampling technique (AW-SMOTE), which combines the two perspectives of boundary tightness and local density and provides global sample enhancement support.
The results demonstrate that SVED-SMOTE significantly outperforms state-of-the-art oversampling techniques across multiple metrics, particularly in datasets with complex distributions and severe class overlap.
Jiao Wang, N. Awang· International Journal of Mac...· 1 citation
PAPT++ is introduced, a risk-aware adversarial generation-training framework for SDG that progressively exposes the classifier to challenging yet semantically consistent variations.
Zhi-Peng Xu, De Cheng, Xinyang Jiang et al.· 0 citations
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