TL;DR
This study proposes complexity-guided cluster-based adaptive oversampling (CCAS), a novel resampling technique for low- to mid-sized imbalanced datasets that outperforms the benchmark methods across metric-classifier combinations.
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.
Multi-class imbalanced classification remains difficult because minority classes can be poorly recognised even when aggregate performance appears acceptable. Many imbalance-handling methods still rely on a fixed technique, a single selected technique, or one level of adaptation, despite the fact that technique suitabil...
Class imbalance remains a critical challenge in supervised learning, often biasing classifiers toward majority classes. While resampling techniques like Synthetic Minority Oversampling Technique (SMOTE) are widely used, the combined effect of data balancing and hyperparameter optimization across diverse datasets is rar...
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