A cluster-induced distribution shift simulation framework is proposed and used to evaluate six batch adaptation strategies including cluster-local ADWIN on five benchmark datasets.
Mining Concept-Drifting Data Streams Using Ensemble Classi- fiers
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The study theoretically examines concept drift and evaluates drift detection algorithms across categories on diverse streaming datasets.
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Cluster-Specific Localized Drift Detection for Efficient Batch Model Adaptation under Controlled Distribution Shift
A cluster-induced distribution shift simulation framework is proposed and used to evaluate six batch adaptation strategies including cluster-local ADWIN on five benchmark datasets.
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Learner-based Concept Drift Detection: Analysis and Evaluation
The study theoretically examines concept drift and evaluates drift detection algorithms across categories on diverse streaming datasets.