Binary and Multinomial Classification through Evolutionary Symbolic Regression
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 3 of its 18 references that are in the map. First posted on arXiv 25 Jun 2022.
Builds on
3 of 18 references.
References · 3
- 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
- 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,455
- 2017PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparisoncited by 139
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Citing papers per year: 2022: 1; 2023: 2.
3 papers.