One-Class Classification

One-Class Classification (OCC) is a classification algorithm specifically designed to handle scenarios where negative class data are missing, under-sampled, or unclear. Its primary goal is to learn from only positive class samples and establish a classification boundary that can accurately distinguish normal instances from potential anomalies or novel patterns. OCC has significant application value in anomaly detection and concept learning, effectively identifying unseen abnormal patterns and enhancing the robustness and security of systems.