Aug 2026· International Journal of Data Science and Analysis· Vol 22· 0 citations· 38 references
TL;DR
A data-driven semi-supervised framework for imbalanced binary image classification that does not depend on data augmentation, enabling reliable utilization of unlabeled data without introducing augmentation induced noise is introduced.
Experiments show that IRRL achieves balanced classification performance, with favorable F1-score and Matthews Correlation Coefficient results that reflect improved minority-class recognition quality, and robustness and consistency of the proposed representation learning strategy.
A novel semi-supervised framework that disentangles pseudo-label generation from the classification task via designing a dedicated pseudo-label generator to align the class distributions between labeled and unlabeled data is proposed.
Yifan Wang, Biao Liu, Xin Geng et al.· Frontiers of Computer Scienc...· 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
The results show that the improved Tri-training semi-supervised classification algorithm based on adaptive neighborhood entropy, denoted as ANET, effectively suppresses pseudo-label noise and has stronger robustness and practicality in real classification tasks.
Xiangxiang Cai, Song Li, Yulin Zhang· International Conference on...· 0 citations
Data streams in the real world often display severe class imbalance, with a critical under-representation of minority-class instances, resulting in biased and unreliable classification results from conventional deep learning frameworks. We introduce a new Imbalance-Resilient Deep Classification Framework that is define...
Soma Sekhar Gaddipati, T. Lakshmi, Nithya Krishnan et al.· 2026 International Conferenc...· 0 citations
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