Improving Generalization of Genetic Programming for High-Dimensional Symbolic Regression with Shapley Value Based Feature Selection

Symbolic Regression (SR) on high-dimensional datasets often encounters significant challenges, resulting in models with poor generalization capabilities. While feature selection has the potential to enhance the generalization and learning performance in general, its application in Genetic Programming (GP) for high-dimensional SR remains a complex problem. Originating from game theory, the Shapley value is applied to additive feature attribution approaches where it distributes the difference between a model output and a baseline average across input variables. By providing an accurate assessment of each feature importance, the Shapley value offers a robust approach to select features. In this paper, we propose a novel feature selection method leveraging the Shapley value to identify and select important features in GP for high-dimensional SR. Through a series of experiments conducted on ten high-dimensional regression datasets, the results indicate that our algorithm surpasses standard GP and other GP-based feature selection methods in terms of learning and generalization performance on most datasets. Further analysis reveals that our algorithm generates more compact models, focusing on the inclusion of important features.