  In this research we address the problem of capturing recurring concepts in a
data stream environment. Recurrence capture enables the re-use of previously
learned classifiers without the need for re-learning while providing for better
accuracy during the concept recurrence interval. We capture concepts by
applying the Discrete Fourier Transform (DFT) to Decision Tree classifiers to
obtain highly compressed versions of the trees at concept drift points in the
stream and store such trees in a repository for future use. Our empirical
results on real world and synthetic data exhibiting varying degrees of
recurrence show that the Fourier compressed trees are more robust to noise and
are able to capture recurring concepts with higher precision than a meta
learning approach that chooses to re-use classifiers in their originally
occurring form.
