Ensemble voting strategies for change point detection improve F1-score by 11% over Mozilla's T-test method on a new ground-truth dataset of 174 performance time series annotated by practitioners.
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2 Pith papers cite this work. Polarity classification is still indexing.
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A hybrid active-online learning framework maintains near-ceiling accuracy in optical network failure detection by labeling only 3.4% of streaming samples via margin-based selection.
citing papers explorer
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Exploring Statistical Change Point Detection Techniques for Performance Anomaly Detection at Mozilla
Ensemble voting strategies for change point detection improve F1-score by 11% over Mozilla's T-test method on a new ground-truth dataset of 174 performance time series annotated by practitioners.
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Hybrid Active-Online Learning Framework for Label-Efficient Concept Drift Adaptation in Optical Network Failure Detection
A hybrid active-online learning framework maintains near-ceiling accuracy in optical network failure detection by labeling only 3.4% of streaming samples via margin-based selection.