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  • AUT Faculties
  • Faculty of Design and Creative Technologies (Te Ara Auaha)
  • School of Engineering, Computer and Mathematical Sciences - Te Kura Mātai Pūhanga, Rorohiko, Pāngarau
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Use of ensembles of Fourier spectra in capturing recurrent concepts in data streams

Sripirikas, S; Pears, RL
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http://hdl.handle.net/10292/9369
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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.
Keywords
Fourier transform spectra; Data mining; Decision trees; Discrete Fourier transforms
Date
July 12, 2015
Source
Neural Networks (IJCNN), 2015 International Joint Conference,12-17 July 2015, Killarney, Ireland.
Item Type
Conference Contribution
Publisher
IEEE
DOI
10.1109/IJCNN.2015.7280583
Rights Statement
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.

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