TR2023-002

GSR: A Generalized Symbolic Regression Approach


    •  Tohme, T., Liu, D., Youcef-Toumi, K., "GSR: A Generalized Symbolic Regression Approach", Transactions on Machine Learning Research, January 2023.
      BibTeX TR2023-002 PDF
      • @article{Tohme2023jan,
      • author = {Tohme, Tony and Liu, Dehong and Youcef-Toumi, Kamal},
      • title = {GSR: A Generalized Symbolic Regression Approach},
      • journal = {Transactions on Machine Learning Research},
      • year = 2023,
      • month = jan,
      • url = {https://www.merl.com/publications/TR2023-002}
      • }
  • MERL Contact:
  • Research Areas:

    Artificial Intelligence, Machine Learning, Optimization

Abstract:

Identifying the mathematical relationships that best describe a dataset remains a very challenging problem in machine learning, and is known as Symbolic Regression (SR). In contrast to neural networks which are often treated as black boxes, SR attempts to gain insight into the underlying relationships between the independent variables and the target variable of a given dataset by assembling analytical functions. In this paper, we present GSR, a Generalized Symbolic Regression approach, by modifying the conventional SR optimization problem formulation, while keeping the main SR objective intact. In GSR, we infer mathematical relationships between the independent variables and some transformation of the target variable. We constrain our search space to a weighted sum of basis functions, and propose a genetic programming approach with a matrix-based encoding scheme. We show that our GSR method is competitive with strong SR benchmark methods, achieving promising experimental performance on the well-known SR benchmark problem sets. Finally, we high- light the strengths of GSR by introducing SymSet, a new SR benchmark set which is more challenging relative to the existing benchmarks.