Population Subset Selection for the Use of a Validation Dataset for Overfitting Control in Genetic Programming

Use este enlace para citar
http://hdl.handle.net/2183/26190Coleccións
- Investigación (FIC) [1628]
Metadatos
Mostrar o rexistro completo do ítemTítulo
Population Subset Selection for the Use of a Validation Dataset for Overfitting Control in Genetic ProgrammingData
2019-07-31Cita bibliográfica
Rivero D, Fernandez-Blanco E, Fernandez-Lozano C, Pazos A. Population subset selection for the use of a validation dataset for overfitting control in genetic programming. J Exp Theor Artif Intell. 2020; 32(2):243-271
Resumo
[Abstract] Genetic Programming (GP) is a technique which is able to solve different problems through the evolution of mathematical expressions. However, in order to be applied, its tendency to overfit the data is one of its main issues. The use of a validation dataset is a common alternative to prevent overfitting in many Machine Learning (ML) techniques, including GP. But, there is one key point which differentiates GP and other ML techniques: instead of training a single model, GP evolves a population of models. Therefore, the use of the validation dataset has several possibilities because any of those evolved models could be evaluated. This work explores the possibility of using the validation dataset not only on the training-best individual but also in a subset with the training-best individuals of the population. The study has been conducted with 5 well-known databases performing regression or classification tasks. In most of the cases, the results of the study point out to an improvement when the validation dataset is used on a subset of the population instead of only on the training-best individual, which also induces a reduction on the number of nodes and, consequently, a lower complexity on the expressions.
Palabras chave
Genetic programming
Overfitting
Validation
Evolutionary computation
Overfitting
Validation
Evolutionary computation
Versión do editor
Dereitos
This is an accepted manuscript of an articled published by Taylor & Francis in "Journal of Experimental & Theoretical Artificial Intelligence", avaliable at Taylor & Francis Online
ISSN
0952-813X