  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.
