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Inexact Simplification of Symbolic Regression Expressions with Locality-sensitive Hashing
April 10, 2024, 4:42 a.m. | Guilherme Seidyo Imai Aldeia (Federal University of ABC), Fabricio Olivetti de Franca (Federal University of ABC), William G. La Cava (Boston Children
cs.LG updates on arXiv.org arxiv.org
Abstract: Symbolic regression (SR) searches for parametric models that accurately fit a dataset, prioritizing simplicity and interpretability. Despite this secondary objective, studies point out that the models are often overly complex due to redundant operations, introns, and bloat that arise during the iterative process, and can hinder the search with repeated exploration of bloated segments. Applying a fast heuristic algebraic simplification may not fully simplify the expression and exact methods can be infeasible depending on size …
abstract arxiv cs.lg cs.ne dataset hashing hinder interpretability iterative operations parametric process regression simplicity studies type
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