1.4LGFeb 11
Experimental Demonstration of Online Learning-Based Concept Drift Adaptation for Failure Detection in Optical NetworksYousuf Moiz Ali, Jaroslaw E. Prilepsky, João Pedro et al.
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.
2.1LGJun 29
Hybrid Active-Online Learning Framework for Label-Efficient Concept Drift Adaptation in Optical Network Failure DetectionYousuf Moiz Ali, Jaroslaw E. Prilepsky, João Pedro et al.
We propose a hybrid active-online learning framework for label-efficient concept drift adaptation in optical network failure detection. Using margin-based selective labeling, our method achieves nearceiling accuracy and AUC scores while querying only 3.4% of streaming samples, with negligible latency overhead compared to static inference.