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

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-network...

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Bibliographic Details
Main Authors: Badii, A. (Author), Hammoodi, M.S (Author), Stahl, F. (Author)
Format: Article
Language:English
Published: Elsevier B.V. 2018
Subjects:
Online Access:View Fulltext in Publisher
LEADER 02359nam a2200373Ia 4500
001 10.1016-j.knosys.2018.08.007
008 220706s2018 CNT 000 0 und d
020 |a 09507051 (ISSN) 
245 1 0 |a Real-time feature selection technique with concept drift detection using adaptive micro-clusters for data stream mining 
260 0 |b Elsevier B.V.  |c 2018 
856 |z View Fulltext in Publisher  |u https://doi.org/10.1016/j.knosys.2018.08.007 
520 3 |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 
650 0 4 |a Classification (of information) 
650 0 4 |a Classification algorithm 
650 0 4 |a Concept drift detection 
650 0 4 |a Concept drifts 
650 0 4 |a Data mining 
650 0 4 |a Data stream classifications 
650 0 4 |a Data stream mining 
650 0 4 |a Data Stream Mining 
650 0 4 |a Electronic trading 
650 0 4 |a Feature extraction 
650 0 4 |a Intrusion detection 
650 0 4 |a Learning systems 
650 0 4 |a Real-time feature selection 
650 0 4 |a Real-time features 
650 0 4 |a Real-time sensor networks 
650 0 4 |a Sensor networks 
650 0 4 |a Situation assessment 
650 0 4 |a Statistical summary 
700 1 |a Badii, A.  |e author 
700 1 |a Hammoodi, M.S.  |e author 
700 1 |a Stahl, F.  |e author 
773 |t Knowledge-Based Systems