仅标记3.4%数据，实现光网络故障检测的高效概念漂移适应

Hybrid Active-Online Learning Framework for Label-Efficient Concept Drift Adaptation in Optical Network Failure Detection

• 结合主动学习与在线学习，基于置信度选择需标注样本
• 准确率和AUC接近理论上限，仅需标注3.4%流式数据
• 适合对延迟敏感、标注成本高的实时网络故障监测场景

我们提出一种混合主动-在线学习框架，用于光网络故障检测中的标签高效概念漂移适应。通过基于边缘的选样标注策略，该方法在仅查询3.4%流式样本的情况下，实现了接近理论上限的准确率和AUC得分，同时相比静态推理的延迟开销可忽略不计。

原文摘要 · Abstract (English)

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.

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