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

Hammoodi, M. S., Stahl, F. and Badii, A. (2018) Real-time feature selection technique with concept drift detection using adaptive micro-clusters for data stream mining. Knowledge-Based Systems, 161. pp. 205-239. ISSN 0950-7051

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To link to this item DOI: 10.1016/j.knosys.2018.08.007

Abstract/Summary

Data streams are unbounded, sequential data instances that are generated with high Velocity. Classifying sequential data instances is a very challenging problem in machine learning with applications in network intrusion detection, financial markets and applications requiring real-time sensor-networks-based situation assessment. Data stream classification is concerned with the automatic labelling of unseen instances from the stream in real-time. For this the classifier needs to adapt to concept drifts and can only have a single pass through the data if the stream is fast moving. This research paper presents work on a real-time pre-processing technique, in particular feature tracking. The feature tracking technique is designed to improve Data Stream Mining (DSM) classification algorithms by enabling and optimising real-time feature selection. The technique is based on tracking adaptive statistical summaries of the data and class label distributions, known as Micro-Clusters. Currently the technique is able to detect concept drifts and identify which features have been influential in the drift.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:78678
Uncontrolled Keywords:Data Stream Mining, real-time Feature Selection, Concept Drift Detection
Publisher:Elsevier

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