Binary and Multinomial Classification through Evolutionary Symbolic Regression

• URL: http://arxiv.org/abs/2206.12706v1
• Date: Sat, 25 Jun 2022 18:38:40 GMT
• Title: Binary and Multinomial Classification through Evolutionary Symbolic Regression
• Authors: Moshe Sipper
• Abstract summary: We present three evolutionary symbolic regression-based classification algorithms for binary and multinomial datasets: GPClf, 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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