A New Deterministic Technique for Symbolic Regression
This provides a more interpretable and efficient alternative for users needing mathematical expressions from datasets, though it appears incremental compared to existing methods.
The paper tackles symbolic regression by introducing a deterministic method that grows simple expressions to fit data, achieving results comparable to other machine learning methods with very low computational time.
This paper describes a new method for Symbolic Regression that allows to find mathematical expressions from a dataset. This method has a strong mathematical basis. As opposed to other methods such as Genetic Programming, this method is deterministic, and does not involve the creation of a population of initial solutions. Instead of it, a simple expression is being grown until it fits the data. The experiments performed show that the results are as good as other Machine Learning methods, in a very low computational time. Another advantage of this technique is that the complexity of the expressions can be limited, so the system can return mathematical expressions that can be easily analysed by the user, in opposition to other techniques like GSGP.