While addressing the problem of imbalanced data classification, most existing resampling
methods primarily focus on balancing class distribution. However, they often overlook class …

Artificial intelligence technology has demonstrated remarkable diagnostic efficacy in modern
biomedical image analysis. However, the practical application of artificial intelligence is …

Handling imbalanced datasets in deep learning presents a significant challenge, often
resulting in biased model performance. While large models with high parameter counts can …

Data imbalance is the main factor causing inaccurate classification results. Data-level
methods have achieved certain advantages in dealing with data imbalance problems, but …

For imbalanced classification problem, algorithm-level methods can effectively avoid the
information loss and noise introduction of data-level methods. However, the differences in …

Data-level methods often suffer from mode collapse when the minority class has multiple
distribution patterns. Some studies have tried addressing the problem using similarity …

RGB-D food nutrition assessment entails the direct prediction of nutritional content in pairs of
RGB and depth food images using signal processing technology. However, existing …

Broad Learning System (BLS) perform well in classification tasks with good computational
efficiency. However, its effectiveness decreases when faced with imbalanced data …