Hybrid Active-Online Learning Framework for Label-Efficient Concept Drift Adaptation in Optical Network Failure Detection
A hybrid active-online learning framework is proposed for label-efficient concept drift adaptation in optical network failure detection, achieving high accuracy and AUC scores with minimal samples and negligible latency.
The authors introduce a margin-based selective labeling method for label-efficient concept drift adaptation in optical network failure detection.
Before reading this…
Applications
- →Optical network failure detection
To understand this paper, make sure you know these concepts first:
- Understanding of concept drift and active learningfind papers →
- Familiarity with optical networksfind papers →
Abstract
More Like ThisWe 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.