Experimental Demonstration of Online Learning-Based Concept Drift Adaptation for Failure Detection in Optical Networks

We present a novel online learning-based approach for concept drift adaptation in optical network failure detection, achieving up to a 70% improvement in performance over conventional static models while maintaining low latency.