Imbalanced Classification

Imbalanced classification addresses the challenge of building accurate predictive models when one class significantly outnumbers others in a dataset, leading to biased model performance. Current research focuses on developing novel algorithms and adapting existing models (like SVMs, neural networks, and graph neural networks) to mitigate this bias, often employing techniques such as cost-sensitive learning, data augmentation (including GAN-based methods), and resampling strategies (oversampling, undersampling, and hybrid approaches). These advancements are crucial for improving the reliability and fairness of machine learning models across diverse applications, particularly in domains with inherently skewed data distributions like medical diagnosis and fraud detection. The field is also actively exploring the impact of balancing methods on model behavior and the development of more robust evaluation metrics.