Real-time feature selection technique with concept drift detection using adaptive micro-clusters for data stream mining

Mahmood Shakir Hammoodi; Frederic Stahl; Atta Badii

doi:10.1016/j.knosys.2018.08.007

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Real-time feature selection technique with concept drift detection using adaptive micro-clusters for data stream mining

Mahmood Shakir Hammoodi Frederic Stahl Atta Badii

Year: 2018 Journal: Knowledge-Based Systems Vol: 161 Pages: 205-239 Publisher: Elsevier BV

DOI: 10.1016/j.knosys.2018.08.007

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Keywords:

Concept drift Computer science Data stream mining Data stream Feature selection Data mining Classifier (UML) Intrusion detection system Artificial intelligence Feature (linguistics) Tracking (education) Pattern recognition (psychology) Machine learning

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104

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0.90

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Topics

Data Stream Mining Techniques

Physical Sciences → Computer Science → Artificial Intelligence

Time Series Analysis and Forecasting

Physical Sciences → Computer Science → Signal Processing

Anomaly Detection Techniques and Applications

Physical Sciences → Computer Science → Artificial Intelligence

Real-time feature selection technique with concept drift detection using adaptive micro-clusters for data stream mining

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Citation History

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