Guided Random Forest and its application to data approximation

TL;DR

This paper introduces the Guided Random Forest (GRAF), an ensemble method that uses global partitioning to improve classification accuracy and dataset approximation, bridging decision trees and boosting.

Contribution

The paper proposes GRAF, a novel ensemble classifier that extends oblique decision trees with global partitioning, demonstrating improved generalization and dataset approximation.

Findings

GRAF achieves comparable or better results than existing methods on benchmark datasets.

Global partitioning reduces the generalization error bound.

GRAF provides a new approach to dataset approximation within random forests.

Abstract

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.