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

2026-06-29 · Yousuf Moiz Ali, Jaroslaw E. Prilepsky, João Pedro, Sasipim Srivallapanondh, Antonio Napoli, Sergei K. Turitsyn, Pedro Freire arxiv

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.

📄 PDF Abstract BibTeX arXiv:2606.30322

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ActDroid: An active learning framework for Android malware detection

2024-01-30 · Ali Muzaffar, Hani Ragab Hassen, Hind Zantout, Michael A Lones

The growing popularity of Android requires malware detection systems that can keep up with the pace of new software being released. According to a recent study, a new piece of malware appears online every 12 seconds. To …

Active Learning Android Malware Detection Malware Detection

Efficient Online Decision Tree Learning with Active Feature Acquisition

2023-05-03 · Arman Rahbar, Ziyu Ye, Yuxin Chen, Morteza Haghir Chehreghani

Constructing decision trees online is a classical machine learning problem. Existing works often assume that features are readily available for each incoming data point. However, in many real world applications, both fea…

Medical Diagnosis

QueryMarket: Cost-Aware Online Active Learning in Data Markets

2026-06-16 · Xiwen Huang, Pierre Pinson arxiv

Data acquisition is a major bottleneck for learning in real-time streams: analysts must decide on the fly which labels to purchase while respecting a rolling budget. However, existing online active learning rarely unifie…

Active Learning

Adapting Actively on the Fly: Relevance-Guided Online Meta-Learning with Latent Concepts for Geospatial Discovery

2026-02-19 · Jowaria Khan, Anindya Sarkar, Yevgeniy Vorobeychik, Elizabeth Bondi-Kelly arxiv

In environmental monitoring, data collection is often costly, sparse, and shaped by urgent public-health needs. This is particularly true for cancer-causing PFAS (Per- and polyfluoroalkyl substances) contamination, where…

Reinforcement Learning Active Learning

SMARTQUERY: An Active Learning Framework for Graph Neural Networks through Hybrid Uncertainty Reduction

2022-12-02 · Xiaoting Li, Yuhang Wu, Vineeth Rakesh, Yusan Lin 외

Graph neural networks have achieved significant success in representation learning. However, the performance gains come at a cost; acquiring comprehensive labeled data for training can be prohibitively expensive. Active …

Active Learning Graph Learning Graph Neural Network Representation Learning