A simplified directional KeRF algorithm

TL;DR

This paper introduces a simplified directional KeRF algorithm, a kernel-based variation of random forests, and proves its asymptotic equivalence to centered KeRF, supported by numerical experiments.

Contribution

It presents a new simplified directional KeRF algorithm and establishes its theoretical equivalence to centered KeRF, enhancing understanding of kernel-based random forests.

Findings

Asymptotic equivalence between simplified directional KeRF and centered KeRF.

Numerical experiments support the theoretical results.

The algorithm simplifies existing KeRF methods.

Abstract

Random forest methods belong to the class of non-parametric machine learning algorithms. They were first introduced in 2001 by Breiman and they perform with accuracy in high dimensional settings. In this article, we consider, a simplified kernel-based random forest algorithm called simplified directional KeRF (Kernel Random Forest). We establish the asymptotic equivalence between simplified directional KeRF and centered KeRF, with additional numerical experiments supporting our theoretical results.