SPINEX_ Symbolic Regression: Similarity-based Symbolic Regression with Explainable Neighbors Exploration
arXiv:2411.03358 · doi:10.1007/s11227-025-07132-x
Abstract
This article introduces a new symbolic regression algorithm based on the SPINEX (Similarity-based Predictions with Explainable Neighbors Exploration) family. This new algorithm (SPINEX_SymbolicRegression) adopts a similarity-based approach to identifying high-merit expressions that satisfy accuracy- and structural similarity metrics. We conducted extensive benchmarking tests comparing SPINEX_SymbolicRegression to over 180 mathematical benchmarking functions from international problem sets that span randomly generated expressions and those based on real physical phenomena. Then, we evaluated the performance of the proposed algorithm in terms of accuracy, expression similarity in terms of presence operators and variables (as compared to the actual expressions), population size, and number of generations at convergence. The results indicate that SPINEX_SymbolicRegression consistently performs well and can, in some instances, outperform leading algorithms. In addition, the algorithm's explainability capabilities are highlighted through in-depth experiments.
References in corpus (10)
- Interpretable Scientific Discovery with Symbolic Regression: A Review
- Discovery of Algebraic Reynolds-Stress Models Using Sparse Symbolic Regression
- Where are we now? A large benchmark study of recent symbolic regression methods
- A computational framework for physics-informed symbolic regression with straightforward integration of domain knowledge
- Deep symbolic regression for physics guided by units constraints: toward the automated discovery of physical laws
- Deep Symbolic Regression for Recurrent Sequences
- Interpretability in Symbolic Regression: a benchmark of Explanatory Methods using the Feynman data set
- Efficient Generator of Mathematical Expressions for Symbolic Regression
- Symbolic-Regression Boosting
- SPINEX: Similarity-based Predictions and Explainable Neighbors Exploration for Regression and Classification Tasks in Machine Learning