New Decomposable Neuro-Symbolic Regression Method Improves Interpretability of Complex Mathematical Models
Researchers have developed a new symbolic regression method that combines transformer models, genetic algorithms, and genetic programming to discover interpretable mathematical expressions from data. The approach addresses a key limitation of existing methods by prioritizing both prediction accuracy and the interpretability of the underlying equations. The work demonstrates competitive performance on benchmark datasets while consistently recovering the original mathematical structure of complex systems.
The paper presents a decomposable symbolic regression (SR) method designed to discover mathematical expressions that explain how complex systems work, rather than simply minimizing prediction errors. The approach uses a Multi-Set Transformer to generate univariate symbolic representations for each variable, then employs genetic algorithms to select high-quality candidates and genetic programming to merge them while preserving their structure. The method concludes with coefficient optimization via genetic algorithms. Evaluation on controlled noise problems and the Feynman dataset shows the approach achieves lower or comparable errors to existing methods while consistently learning expressions matching the original mathematical structure, with high symbolic solution recovery rates.
What's missing
The paper does not discuss computational complexity or runtime comparisons with baseline methods, nor does it address scalability to very high-dimensional problems or real-world deployment considerations.
What different sources said
- arXiv cs.AICenter
EditSR: Enhancing Neural Symbolic Regression via Edit-based Rectification
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