Hybrid Active-Online Learning Framework for Label-Efficient Concept Drift Adaptation in Optical Network Failure Detection
arXiv:2606.30322v1 Announce Type: new Abstract: 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.
The paper was just published, presenting a novel framework for more efficient AI in critical infrastructure. This research reflects ongoing trends in optimizing AI for real-world, high-stakes applications.
This development improves autonomous system reliability and efficiency, crucial for critical infrastructure where downtime is costly and often catastrophic. It enables more robust and scalable AI deployments in complex environments.
The accuracy of AI-driven failure detection in optical networks can be significantly improved with less data, reducing operational costs and latency while adapting to evolving conditions.
- · Telecommunications companies
- · AI infrastructure providers
- · Network operators
- · Critical infrastructure sectors
- · Manual network maintenance services
- · Systems with high reliance on large, labeled datasets
Optical networks become more resilient and operate with higher uptime due to proactive and accurate failure detection.
Reduced operational expenditures for network providers, potentially leading to faster expansion and new service offerings.
The methodology could be applied to other critical infrastructure failure detection, enhancing the robustness of smart cities and industrial control systems.
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Read at arXiv cs.LG