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Towards real-time feature tracking technique using adaptive micro-clusters

Shakir Hammoodi, M., Stahl, F., Tennant, M. and Badii, A. (2017) Towards real-time feature tracking technique using adaptive micro-clusters. Expert Update, 17 (1). ISSN 1465-4091 (Special Issue on the 1st BCS SGAI Workshop on Data Stream Mining Techniques and Applications)

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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 sensor networks. 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. This research paper presents our work on a real-time pre-processing technique, in particular a feature tracking technique that takes concept drift into consideration. The feature tracking technique is designed to improve Data Stream Mining (DSM) classification algorithms by enabling real-time feature selection. The technique is based on adaptive summaries of the data and class distributions, known as Micro-Clusters. Currently the technique is able to detect concept drift and identifies which features have been involved.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:70046
Publisher:BCS Specialist Group on Artifical Intelligence

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