Use of ensembles of Fourier spectra in capturing recurrent concepts in data streams

Date
2015-07-12
Authors
Sripirikas, S
Pears, RL
Supervisor
Item type
Conference Contribution
Degree name
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
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.

Description
Keywords
Fourier transform spectra; Data mining; Decision trees; Discrete Fourier transforms
Source
Neural Networks (IJCNN), 2015 International Joint Conference,12-17 July 2015, Killarney, Ireland.
Publisher's version
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