This study analyzes and evaluates concept drift detection algorithms across various categories using synthetic and real-world streaming datasets. It examines drift characteristics and evaluates detector performance under abrupt and gradual drift scenarios to improve understanding of drift behavior and detector applicability.
Learner-based Concept Drift Detection: Analysis and Evaluation
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