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Towards online concept drift detection with feature selection for data stream classification

Hammoodi, M., Stahl, F. ORCID: and Tennant, M. (2016) Towards online concept drift detection with feature selection for data stream classification. In: 22nd European Conference on Artificial Intelligence, 29th August - 2nd September, The Hague, Holland, pp. 1549-1550.

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Data Streams are unbounded, sequential data instances that are generated very rapidly. The storage, querying and mining of such rapid flows of data is computationally very challenging. Data Stream Mining (DSM) is concerned with the mining of such data streams in real-time using techniques that require only one pass through the data. DSM techniques need to be adaptive to reflect changes of the pattern encoded in the stream (concept drift). The relevance of features for a DSM classification task may change due to concept drifts and this paper describes the first step towards a concept drift detection method with online feature tracking capabilities.

Item Type:Conference or Workshop Item (Paper)
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:68360


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