Random Machines Regression Approach: an ensemble support vector regression model with free kernel choice
Abstract
Machine learning techniques always aim to reduce the generalized prediction error. In order to reduce it, ensemble methods present a good approach combining several models that results in a greater forecasting capacity. The Random Machines already have been demonstrated as strong technique, i.e: high predictive power, to classification tasks, in this article we propose an procedure to use the bagged-weighted support vector model to regression problems. Simulation studies were realized over artificial datasets, and over real data benchmarks. The results exhibited a good performance of Regression Random Machines through lower generalization error without needing to choose the best kernel function during tuning process.
Cite
@article{arxiv.2003.12643,
title = {Random Machines Regression Approach: an ensemble support vector regression model with free kernel choice},
author = {Anderson Ara and Mateus Maia and Samuel Macêdo and Francisco Louzada},
journal= {arXiv preprint arXiv:2003.12643},
year = {2020}
}
Comments
arXiv admin note: text overlap with arXiv:1911.09411