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02359nam a2200373Ia 4500 |
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10.1016-j.knosys.2018.08.007 |
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220706s2018 CNT 000 0 und d |
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|a 09507051 (ISSN)
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|a Real-time feature selection technique with concept drift detection using adaptive micro-clusters for data stream mining
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|b Elsevier B.V.
|c 2018
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|z View Fulltext in Publisher
|u https://doi.org/10.1016/j.knosys.2018.08.007
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|a 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. © 2018
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|a Classification (of information)
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|a Classification algorithm
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|a Concept drift detection
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|a Concept drifts
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|a Data mining
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|a Data stream classifications
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|a Data stream mining
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|a Data Stream Mining
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|a Electronic trading
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|a Feature extraction
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|a Intrusion detection
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|a Learning systems
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|a Real-time feature selection
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|a Real-time features
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|a Real-time sensor networks
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|a Sensor networks
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|a Situation assessment
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|a Statistical summary
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|a Badii, A.
|e author
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|a Hammoodi, M.S.
|e author
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|a Stahl, F.
|e author
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|t Knowledge-Based Systems
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