Summary of Shape Constraints in Symbolic Regression Using Penalized Least Squares, by Viktor Martinek and Julia Reuter and Ophelia Frotscher and Sanaz Mostaghim and Markus Richter and Roland Herzog
Shape Constraints in Symbolic Regression using Penalized Least Squares
by Viktor Martinek, Julia Reuter, Ophelia Frotscher, Sanaz Mostaghim, Markus Richter, Roland Herzog
First submitted to arxiv on: 31 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Symbolic Computation (cs.SC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method adds shape constraints (SC) to symbolic regression (SR), allowing prior knowledge about model function shapes to be introduced. This paper minimizes SC violations during the parameter identification step using gradient-based optimization and tests three algorithm variants on synthetically generated datasets with varying noise levels and reduced training data. The results show that incorporating SC is particularly beneficial when data is scarce, and the proposed approach outperforms the traditional method in some cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to find simple expressions for unknown functions is being studied. This method adds rules about what shapes these expressions can take before looking at the data. The goal is to make this process more accurate by using a special kind of math called gradient-based optimization. Three different ways of doing this were tested on fake datasets with varying levels of noise and less training data. The results show that adding these shape rules helps especially when there’s not much data available. |
Keywords
» Artificial intelligence » Optimization » Regression
