Class-Imbalanced Learning

It studies how skewed class frequencies, long tails, and rare events bias models toward majority classes, a challenge in medical diagnosis, fraud detection, and computer vision. Common approaches rebalance data through over- or undersampling and synthetic examples, adjust objectives with class weights, focal or logit-adjusted losses, and improve representations with prototypes, contrastive learning, or class-aware sampling. Active learning, calibration, and federated or semi-supervised methods address settings with limited labels, distributed data, or shifting class priors. Results depend strongly on data structure and deployment conditions: reweighting and synthetic data can improve minority recall but may also harm performance or calibration, so evaluations need class-wise and operational metrics, not accuracy alone.