Show simple item record

dc.contributor.authorSripirikas, Sen_NZ
dc.contributor.authorPears, RLen_NZ
dc.date.accessioned2016-01-19T23:35:40Z
dc.date.available2016-01-19T23:35:40Z
dc.date.copyright2015-07-12en_NZ
dc.identifier.citationNeural Networks (IJCNN), 2015 International Joint Conference,12-17 July 2015, Killarney, Ireland.en_NZ
dc.identifier.urihttp://hdl.handle.net/10292/9369
dc.description.abstractIn this research, we apply ensembles of Fourier encoded spectra to capture and mine recurring concepts in a data stream environment. Previous research showed that compact versions of Decision Trees can be obtained by applying the Discrete Fourier Transform to accurately capture recurrent concepts in a data stream. However, in highly volatile environments where new concepts emerge often, the approach of encoding each concept in a separate spectrum is no longer viable due to memory overload and thus in this research we present an ensemble approach that addresses this problem. Our empirical results on real world data and synthetic data exhibiting varying degrees of recurrence reveal that the ensemble approach outperforms the single spectrum approach in terms of classification accuracy, memory and execution time.
dc.publisherIEEE
dc.rightsCopyright © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.subjectFourier transform spectra; Data mining; Decision trees; Discrete Fourier transforms
dc.titleUse of ensembles of Fourier spectra in capturing recurrent concepts in data streamsen_NZ
dc.typeConference Contribution
dc.rights.accessrightsOpenAccessen_NZ
dc.identifier.doi10.1109/IJCNN.2015.7280583
pubs.elements-id181965


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record