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

Sripirikas, S
Pears, RL
Item type
Conference Contribution
Degree name
Journal Title
Journal ISSN
Volume Title

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.

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