Extreme Learning Tree

TL;DR

This paper introduces an Extreme Learning Tree, a decision tree variant with randomization and non-linear transformations, which outperforms linear models and could enhance ensemble methods like Random Forest.

Contribution

It presents a novel decision tree variant combining randomization and non-linear transformations, potentially advancing ensemble learning techniques.

Findings

Outperforms linear models on benchmark datasets

Utilizes non-linear data transformation and randomization

Potential building block for future Random Forest variants

Abstract

The paper proposes a new variant of a decision tree, called an Extreme Learning Tree. It consists of an extremely random tree with non-linear data transformation, and a linear observer that provides predictions based on the leaf index where the data samples fall. The proposed method outperforms linear models on a benchmark dataset, and may be a building block for a future variant of Random Forest.