Guided Random Forest and its application to data approximation
This work addresses a specific problem in machine learning for researchers and practitioners by offering an incremental improvement in ensemble methods.
The paper tackles the problem of improving ensemble classifiers by introducing Guided Random Forest (GRAF), which uses global partitioning to bridge decision trees and boosting, and empirically shows it reduces generalization error bounds, achieving comparable or better results on 115 benchmark datasets.
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.