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dc.contributor.authorSripirakas, S
dc.contributor.authorPears, R
dc.date.accessioned2014-11-19T03:32:17Z
dc.date.available2014-11-19T03:32:17Z
dc.date.copyright2014
dc.identifier.citationPublished in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol.8646 LNCS, pp.439 - 451
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.urihttp://hdl.handle.net/10292/7943
dc.description.abstractIn this research we address the problem of capturing recurring concepts in a data stream environment. Recurrence capture enables the re-use of previously learned classifiers without the need for re-learning while providing for better accuracy during the concept recurrence interval. We capture concepts by applying the Discrete Fourier Transform (DFT) to Decision Tree classifiers to obtain highly compressed versions of the trees at concept drift points in the stream and store such trees in a repository for future use. Our empirical results on real world and synthetic data exhibiting varying degrees of recurrence show that the Fourier compressed trees are more robust to noise and are able to capture recurring concepts with higher precision than a meta learning approach that chooses to re-use classifiers in their originally occurring form.
dc.publisherarXiv
dc.relation.urihttp://arxiv.org/abs/1406.6114
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in (see Citation). The original publication is available at (see Publisher's Version).
dc.titleMining recurrent concepts in data streams using the discrete Fourier transform
dc.typeConference Contribution
dc.rights.accessrightsOpenAccess
aut.relation.endpage451
aut.relation.startpage439
aut.relation.volume8646 LNCS
pubs.elements-id168397


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