Area Under the ROC Curve

The Area Under the ROC Curve (AUC) is a widely used metric for evaluating the performance of binary classifiers, particularly valuable when dealing with imbalanced datasets. Current research focuses on improving AUC optimization through various techniques, including novel loss functions, advanced algorithms like gradient boosting and support vector machines, and the incorporation of fairness constraints to mitigate bias. This work spans diverse applications, from medical diagnosis (e.g., predicting sepsis or cancer mortality) and image analysis (e.g., anomaly detection and segmentation) to natural language processing (e.g., sentiment analysis). The ongoing refinement of AUC-based methods enhances the reliability and fairness of machine learning models across numerous fields.