Class Imbalanced Data

Class imbalance, where one class significantly outnumbers others in a dataset, poses a major challenge for machine learning models, hindering their ability to accurately predict minority classes. Current research focuses on addressing this issue through various techniques, including data augmentation (e.g., using generative models like VAEs and GANs to synthesize minority class samples), algorithmic modifications (e.g., adjusting loss functions like using maximum margin loss or incorporating re-weighting strategies), and hybrid approaches combining resampling and algorithmic methods. Overcoming class imbalance is crucial for improving the reliability and fairness of machine learning models across diverse applications, particularly in domains like finance, healthcare, and anomaly detection where accurate minority class prediction is critical.