DoME: A deterministic technique for equation development and Symbolic Regression. (15th July 2022) - Rivero, Daniel; Fernandez-Blanco, Enrique; Pazos, Alejandro
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. Highlights: Novel technique for Machine Learning and Symbolic Regression. Possibility to explicitly set the complexity of the developed expression. Results compared to other Symbolic Regression techniques and better on a large number of datasets. Much simpler expressions than in GP.