Rare Class

Rare class classification in machine learning focuses on improving the accuracy and reliability of models when dealing with datasets where certain classes have significantly fewer examples than others. Current research emphasizes techniques like generative models (e.g., GANs) to augment scarce data, active learning strategies to efficiently select informative samples for annotation, and novel loss functions or regularization methods to mitigate the inherent biases of imbalanced datasets. Addressing this challenge is crucial for numerous applications, from medical diagnosis (e.g., identifying rare diseases) to fraud detection, where accurate identification of rare events is critical despite limited training data.