# Binary and Multinomial Classification through Evolutionary Symbolic Regression

arXiv: https://arxiv.org/abs/2206.12706 · HTML: https://mlsanity.com/paper/2206.12706/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 25 Jun 2022.

## Builds on (most cited first, 3 of 3)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison](https://mlsanity.com/paper/1703.00512/) (2017)

## Built on by (most cited first, 3 of 3)

- [Explainable Fraud Detection with Deep Symbolic Classification](https://mlsanity.com/paper/2312.00586/) (2023)
- [EC-KitY: Evolutionary Computation Tool Kit in Python with Seamless Machine Learning Integration](https://mlsanity.com/paper/2207.10367/) (2022)
- [A Melting Pot of Evolution and Learning](https://mlsanity.com/paper/2306.04971/) (2023)

## 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.