FePySR: Neural Feature Extraction for Symbolic Regression
FePySR is a two-stage framework for symbolic regression that first uses a heterogeneous neural network to extract valid features from observational data, then performs structural optimization with PySR. It addresses the NP-hard problem of recovering complex mathematical expressions by decomposing them into reusable nonlinear feature modules. On five standard benchmarks, FePySR achieves higher equation recovery rates than state-of-the-art methods. On 75 highly complex synthesized equations, it recovers 36 equations with substantially smaller mean squared error.
Key facts
- FePySR is a two-stage framework for symbolic regression.
- It uses a heterogeneous neural network for feature extraction.
- Structural optimization is performed using PySR.
- It reduces the SR search space by extracting valid features prior to equation search.
- Outperforms state-of-the-art methods on five standard benchmarks.
- Recovers 36 out of 75 highly complex synthesized equations.
- Produces substantially smaller mean squared error.
- Addresses NP-hard problem of recovering complex mathematical expressions.
Entities
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