Class Imbalanced Learning

Class imbalanced learning tackles the challenge of building accurate machine learning models when datasets contain a disproportionate number of samples from different classes. Current research focuses on developing novel algorithms and loss functions to mitigate the bias towards the majority class, including techniques like re-sampling, cost-sensitive learning, and ensemble methods, often applied within transformer networks or other deep learning architectures. These advancements are crucial for improving the performance of models in various applications, particularly those dealing with rare events or skewed data distributions, such as medical diagnosis and fraud detection, where accurate prediction of minority classes is critical. The development of comprehensive toolboxes and standardized evaluation methods further enhances the field's accessibility and reproducibility.