DoME: A Deterministic Technique for Equation Development and Symbolic Regression
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DoME: A Deterministic Technique for Equation Development and Symbolic RegressionData
2022-03-04Cita bibliográfica
Daniel Rivero, Enrique Fernandez-Blanco, Alejandro Pazos, DoME: A deterministic technique for equation development and Symbolic Regression, Expert Systems with Applications, Volume 198, 2022, 116712, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2022.116712. (https://www.sciencedirect.com/science/article/pii/S0957417422001889)
Resumo
[Abstract] Based on a solid mathematical background, this paper proposes a method for Symbolic Regression that enables the extraction of mathematical expressions from a dataset. Contrary to other approaches, such as Genetic Programming, the proposed method is deterministic and, consequently, does not require the creation of a population of initial solutions. Instead, a simple expression is grown until it fits the data. This method has been compared with four well-known Symbolic Regression techniques with a large number of datasets. As a result, on average, the proposed method returns better performance than the other techniques, with the advantage of returning mathematical expressions that can be easily used by different systems. Additionally, this method makes it possible to establish a threshold at the complexity of the expressions generated, i.e., the system can return mathematical expressions that are easily analyzed by the user, as opposed to other techniques that return very large expressions.
Palabras chave
Symbolic regression
Machine learning
Artificial intelligence
Machine learning
Artificial intelligence
Descrición
Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG
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Dereitos
Atribución 4.0 Internacional
ISSN
0957-4174