imbalanced classification

In the field of machine learning, the classification task on imbalanced datasets is referred to as imbalanced classification. This task aims to address the issue where the number of positive and negative samples differs significantly, by optimizing algorithms and evaluation metrics to improve the recognition rate of the minority class and overall classification performance. Imbalanced classification has significant application value in critical areas such as financial fraud detection, medical diagnosis, and network intrusion detection.