Use of Ensembles of Fourier Spectra in Capturing Recurrent Concepts in Data Streams
This work addresses memory and efficiency issues in data stream mining for applications requiring real-time concept adaptation, but it is incremental as it builds on prior Fourier-based methods.
The paper tackles the problem of capturing recurring concepts in data streams, especially in volatile environments, by proposing an ensemble approach using Fourier spectra, which outperforms single-spectrum methods in classification accuracy, memory usage, and execution time on real-world and synthetic data.
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.