A comprehensive ensemble classification techniques detecting and managing concept drift in dynamic imbalanced data streams

K. A. Mohamed Junaid; D. Paulraj; T. Sethukarasi

doi:10.1007/s11276-024-03742-0

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A comprehensive ensemble classification techniques detecting and managing concept drift in dynamic imbalanced data streams

K. A. Mohamed Junaid D. Paulraj T. Sethukarasi

Year: 2024 Journal: Wireless Networks Vol: 31 (1)Pages: 19-30 Publisher: Springer Science+Business Media

DOI: 10.1007/s11276-024-03742-0

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Keywords:

Computer science Concept drift Data stream mining Data mining Ensemble learning STREAMS Dynamic data Machine learning Artificial intelligence Ensemble forecasting Database

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Data Stream Mining Techniques

Physical Sciences → Computer Science → Artificial Intelligence

Network Security and Intrusion Detection

Physical Sciences → Computer Science → Computer Networks and Communications

Anomaly Detection Techniques and Applications

Physical Sciences → Computer Science → Artificial Intelligence

A comprehensive ensemble classification techniques detecting and managing concept drift in dynamic imbalanced data streams

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Citation History

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