Busca avançada
Ano de início
Entree
(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Interaction-Transformation Evolutionary Algorithm for Symbolic Regression

Texto completo
Autor(es):
de Franca, F. O. [1] ; Aldeia, I, G. S.
Número total de Autores: 2
Afiliação do(s) autor(es):
[1] I, Fed Univ ABC, Ctr Math Computat & Cognit, Heurist Anal & Learning Lab, Santo Andre, SP - Brazil
Número total de Afiliações: 1
Tipo de documento: Artigo Científico
Fonte: EVOLUTIONARY COMPUTATION; v. 29, n. 3, p. 367-390, SEP 1 2021.
Citações Web of Science: 0
Resumo

Interaction-Transformation (IT) is a new representation for Symbolic Regression that reduces the space of solutions to a set of expressions that follow a specific structure. The potential of this representation was illustrated in prior work with the algorithm called SymTree. This algorithm starts with a simple linear model and incrementally introduces new transformed features until a stop criterion is met. While the results obtained by this algorithm were competitive with the literature, it had the drawback of not scaling well with the problem dimension. This article introduces a mutation-only Evolutionary Algorithm, called ITEA, capable of evolving a population of IT expressions. One advantage of this algorithm is that it enables the user to specify the maximum number of terms in an expression. In order to verify the competitiveness of this approach, ITEA is compared to linear, nonlinear, and Symbolic Regression models from the literature. The results indicate that ITEA is capable of finding equal or better approximations than other Symbolic Regression models while being competitive to state-of-the-art nonlinear models. Additionally, since this representation follows a specific structure, it is possible to extract the importance of each original feature of a data set as an analytical function, enabling us to automate the explanation of any prediction. In conclusion, ITEA is competitive when comparing to regression models with the additional benefit of automating the extraction of additional information of the generated models. (AU)

Processo FAPESP: 18/14173-8 - Generalização e Aplicações do Tipo de Dado Algébrico Interação-Transformação
Beneficiário:Fabricio Olivetti de França
Modalidade de apoio: Auxílio à Pesquisa - Regular