  We present a new way of constructing an ensemble classifier, named the Guided
Random Forest (GRAF) in the sequel. GRAF extends the idea of building oblique
decision trees with localized partitioning to obtain a global partitioning. We
show that global partitioning bridges the gap between decision trees and
boosting algorithms. We empirically demonstrate that global partitioning
reduces the generalization error bound. Results on 115 benchmark datasets show
that GRAF yields comparable or better results on a majority of datasets. We
also present a new way of approximating the datasets in the framework of random
forests.
