dc.contributor.author | Sripirikas, S | en_NZ |
dc.contributor.author | Pears, RL | en_NZ |
dc.date.accessioned | 2016-01-19T23:35:40Z | |
dc.date.available | 2016-01-19T23:35:40Z | |
dc.date.copyright | 2015-07-12 | en_NZ |
dc.identifier.citation | Neural Networks (IJCNN), 2015 International Joint Conference,12-17 July 2015, Killarney, Ireland. | en_NZ |
dc.identifier.uri | http://hdl.handle.net/10292/9369 | |
dc.description.abstract | In 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.publisher | IEEE | |
dc.rights | Copyright © 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.subject | Fourier transform spectra; Data mining; Decision trees; Discrete Fourier transforms | |
dc.title | Use of ensembles of Fourier spectra in capturing recurrent concepts in data streams | en_NZ |
dc.type | Conference Contribution | |
dc.rights.accessrights | OpenAccess | en_NZ |
dc.identifier.doi | 10.1109/IJCNN.2015.7280583 | |
pubs.elements-id | 181965 | |