Extreme Learning Tree
Machine Learning
2019-12-20 v1 Machine Learning
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.
Cite
@article{arxiv.1912.09087,
title = {Extreme Learning Tree},
author = {Anton Akusok and Emil Eirola and Kaj-Mikael Björk and Amaury Lendasse},
journal= {arXiv preprint arXiv:1912.09087},
year = {2019}
}