Shape Constraints in Symbolic Regression using Penalized Least Squares
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911979098079232 |
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| author | Martinek, Viktor Reuter, Julia Frotscher, Ophelia Mostaghim, Sanaz Richter, Markus Herzog, Roland |
| author_facet | Martinek, Viktor Reuter, Julia Frotscher, Ophelia Mostaghim, Sanaz Richter, Markus Herzog, Roland |
| contents | We study the addition of shape constraints (SC) and their consideration during the parameter identification step of symbolic regression (SR). SC serve as a means to introduce prior knowledge about the shape of the otherwise unknown model function into SR. Unlike previous works that have explored SC in SR, we propose minimizing SC violations during parameter identification using gradient-based numerical optimization. We test three algorithm variants to evaluate their performance in identifying three symbolic expressions from synthetically generated data sets. This paper examines two benchmark scenarios: one with varying noise levels and another with reduced amounts of training data. The results indicate that incorporating SC into the expression search is particularly beneficial when data is scarce. Compared to using SC only in the selection process, our approach of minimizing violations during parameter identification shows a statistically significant benefit in some of our test cases, without being significantly worse in any instance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_20800 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Shape Constraints in Symbolic Regression using Penalized Least Squares Martinek, Viktor Reuter, Julia Frotscher, Ophelia Mostaghim, Sanaz Richter, Markus Herzog, Roland Machine Learning Symbolic Computation We study the addition of shape constraints (SC) and their consideration during the parameter identification step of symbolic regression (SR). SC serve as a means to introduce prior knowledge about the shape of the otherwise unknown model function into SR. Unlike previous works that have explored SC in SR, we propose minimizing SC violations during parameter identification using gradient-based numerical optimization. We test three algorithm variants to evaluate their performance in identifying three symbolic expressions from synthetically generated data sets. This paper examines two benchmark scenarios: one with varying noise levels and another with reduced amounts of training data. The results indicate that incorporating SC into the expression search is particularly beneficial when data is scarce. Compared to using SC only in the selection process, our approach of minimizing violations during parameter identification shows a statistically significant benefit in some of our test cases, without being significantly worse in any instance. |
| title | Shape Constraints in Symbolic Regression using Penalized Least Squares |
| topic | Machine Learning Symbolic Computation |
| url | https://arxiv.org/abs/2405.20800 |