Summary of Spinex_ Symbolic Regression: Similarity-based Symbolic Regression with Explainable Neighbors Exploration, by Mz Naser et al.
SPINEX_ Symbolic Regression: Similarity-based Symbolic Regression with Explainable Neighbors Exploration
by MZ Naser, Ahmed Z Naser
First submitted to arxiv on: 5 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation (stat.CO)
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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 SPINEX_SymbolicRegression algorithm uses a similarity-based approach to identify high-merit expressions that satisfy accuracy- and structural similarity metrics. This medium-difficulty summary highlights the algorithm’s performance benchmarked against over 180 mathematical functions from international problem sets, including randomly generated and physically inspired expressions. The results demonstrate consistent performance improvements compared to leading algorithms, with the added benefit of explainability capabilities demonstrated through in-depth experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to solve math problems using symbolic regression. Imagine having an algorithm that can figure out how to express complex mathematical formulas by analyzing what’s similar about different equations. That’s what this new algorithm, called SPINEX_SymbolicRegression, does. The researchers tested it against many different math problems and found that it performed well and sometimes even better than other algorithms. |
Keywords
» Artificial intelligence » Regression
