Operator Feature Neural Network for Symbolic Regression
This addresses symbolic regression for researchers by improving expression recovery, though it appears incremental as it builds on existing methods with a focus on operator features.
The paper tackled symbolic regression by introducing the operator feature neural network (OF-Net), which uses operator representation and implicit feature encoding to predict mathematical expressions, achieving superior recovery rates and high R² scores on public datasets.
Symbolic regression is a task aimed at identifying patterns in data and representing them through mathematical expressions, generally involving skeleton prediction and constant optimization. Many methods have achieved some success, however they treat variables and symbols merely as characters of natural language without considering their mathematical essence. This paper introduces the operator feature neural network (OF-Net) which employs operator representation for expressions and proposes an implicit feature encoding method for the intrinsic mathematical operational logic of operators. By substituting operator features for numeric loss, we can predict the combination of operators of target expressions. We evaluate the model on public datasets, and the results demonstrate that the model achieves superior recovery rates and high $R^2$ scores. With the discussion of the results, we analyze the merit and demerit of OF-Net and propose optimizing schemes.