Symbolic-Regression Boosting
Neural and Evolutionary Computing
2022-06-27 v1 Machine Learning
Abstract
Modifying standard gradient boosting by replacing the embedded weak learner in favor of a strong(er) one, we present SyRBo: Symbolic-Regression Boosting. Experiments over 98 regression datasets show that by adding a small number of boosting stages -- between 2--5 -- to a symbolic regressor, statistically significant improvements can often be attained. We note that coding SyRBo on top of any symbolic regressor is straightforward, and the added cost is simply a few more evolutionary rounds. SyRBo is essentially a simple add-on that can be readily added to an extant symbolic regressor, often with beneficial results.
Keywords
Cite
@article{arxiv.2206.12082,
title = {Symbolic-Regression Boosting},
author = {Moshe Sipper and Jason H Moore},
journal= {arXiv preprint arXiv:2206.12082},
year = {2022}
}