Enhancing Anomaly Detection Models for Industrial Applications through SVM-Based False Positive Classification
Unsupervised anomaly detection models are crucial for the efficiency of industrial applications.However, frequent false alarms hinder the widespread adoption of unsupervised anomaly detection, especially in fault detection tasks.To this HERBATINT 4C end, our research delves into the dependence of false alarms on the baseline anomaly detector by ana