Sakthithasan, S., Pears, R., Bifet, A., & Pfahringer, B. (2015). Use of Ensembles of Fourier Spectra in Capturing Recurrent Concepts in Data Streams. In 2015 International Joint Conference on Neural Networks (IJCNN) (pp. 1–8). Killarney, Ireland: IEEE. https://doi.org/10.1109/IJCNN.2015.7280583
Permanent Research Commons link: https://hdl.handle.net/10289/11224
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.
This is an author’s accepted version of an article published in the Proceedings of 2015 International Joint Conference on Neural Networks (IJCNN). ©2015 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.