Binary and Multinomial Classification through Evolutionary Symbolic Regression
This work addresses classification tasks for users needing interpretable models, but it is incremental as it applies existing evolutionary symbolic regression techniques to classification without a major breakthrough.
The authors tackled classification problems by developing three evolutionary symbolic regression-based algorithms (GPLearnClf, CartesianClf, ClaSyCo) and tested them on 162 datasets, finding them competitive with state-of-the-art methods like XGBoost, LightGBM, and deep neural networks.
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.