Binary and Multinomial Classification through Evolutionary Symbolic Regression

Moshe Sipper

doi:10.1145/3520304.3528922

Binary and Multinomial Classification through Evolutionary Symbolic Regression

Neural and Evolutionary Computing 2022-06-29 v1 Machine Learning

Authors: Moshe Sipper

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Abstract

We present three evolutionary symbolic regression-based classification algorithms for binary and multinomial datasets: GPLearnClf, CartesianClf, and ClaSyCo. Tested over 162 datasets and compared to three state-of-the-art machine learning algorithms -- XGBoost, LightGBM, and a deep neural network -- we find our algorithms to be competitive. Further, we demonstrate how to find the best method for one's dataset automatically, through the use of a state-of-the-art hyperparameter optimizer.

Keywords

classification

Cite

@article{arxiv.2206.12706,
  title  = {Binary and Multinomial Classification through Evolutionary Symbolic Regression},
  author = {Moshe Sipper},
  journal= {arXiv preprint arXiv:2206.12706},
  year   = {2022}
}

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