Show simple item record

dc.contributor.authorKithulgoda, CIen_NZ
dc.contributor.authorPears, Ren_NZ
dc.contributor.authorNaeem, MAen_NZ
dc.date.accessioned2018-01-31T23:54:04Z
dc.date.available2018-01-31T23:54:04Z
dc.date.copyright2018-05-01en_NZ
dc.identifier.citationExpert Systems with Applications, 97, 1-17.
dc.identifier.issn0957-4174en_NZ
dc.identifier.urihttp://hdl.handle.net/10292/11148
dc.description.abstractTwo major performance bottlenecks with decision tree based classifiers in a data stream environment are the depth of the tree and the update overhead of maintaining leaf node statistics on an instance-wise basis to ensure that classification is consistent with the current state of the data stream. Previous research has shown that classifiers based on Fourier spectra derived from decision trees produce compact array structures that can be searched and maintained much more efficiently than deep tree based structures. However, the key issue of incrementally adapting the spectrum to changes has not been addressed. In this research we present a strategy for incremental maintenance of the Fourier spectrum to changes in concept that take place in data stream environments. Along with the incremental approach we also propose schemes for feature selection and synopsis generation that enable the coefficient array to be refreshed efficiently on a periodic basis. Our empirical evaluation on a number of widely used stream classifiers reveals that the Fourier classifier outperforms them, both in terms of classification accuracy as well as speed of classification.en_NZ
dc.publisherElsevier
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S095741741730845X?via%3Dihub
dc.rightsCopyright © 2018 Elsevier Ltd. All rights reserved. This is the author’s pre-print version of a work that was accepted for publication in (see Citation). 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. The definitive version was published in (see Citation). The original publication is available at (see Publisher's Version).
dc.subjectData Stream; Ensemble Classifier; Discrete Fourier Transform; Concept Drift; Fourier Spectrum; Feature Selection
dc.titleThe incremental Fourier classifier: Leveraging the discrete Fourier transform for classifying high speed data streamsen_NZ
dc.typeJournal Article
dc.rights.accessrightsOpenAccessen_NZ
dc.identifier.doi10.1016/j.eswa.2017.12.023en_NZ
aut.relation.endpage17
aut.relation.startpage1
aut.relation.volume97en_NZ
pubs.elements-id322865
aut.relation.journalExpert Systems with Applicationsen_NZ


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record