Minority Class

Minority class problems arise in machine learning when one or more classes in a dataset are significantly underrepresented compared to others, leading to biased models that poorly classify these crucial minority instances. Current research focuses on addressing this imbalance through techniques like cost-sensitive learning (adjusting penalties for misclassifying minority examples), resampling methods (oversampling minority or undersampling majority instances), and generative adversarial networks (creating synthetic minority data). These advancements are vital for improving the fairness and accuracy of machine learning models across diverse applications, particularly in domains like healthcare and anomaly detection where correctly identifying rare events is critical.