  In this paper we introduce a significant improvement to the popular
tree-based Stochastic Gradient Boosting algorithm using a wavelet decomposition
of the trees. This approach is based on harmonic analysis and approximation
theoretical elements, and as we show through extensive experimentation, our
wavelet based method generally outperforms existing methods, particularly in
difficult scenarios of class unbalance and mislabeling in the training data.
