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.
