SLUG: Feature Selection Using Genetic Algorithms and Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@InProceedings{Rodrigues:2022:EuroGP,

• author = "Nuno Rodrigues and Joao Batista and William {La Cava} and Leonardo Vanneschi and Sara Silva",
• title = "{SLUG}: Feature Selection Using Genetic Algorithms and Genetic Programming",
• booktitle = "EuroGP 2022: Proceedings of the 25th European Conference on Genetic Programming",
• year = "2022",
• editor = "Eric Medvet and Gisele Pappa and Bing Xue",
• series = "LNCS",
• volume = "13223",
• publisher = "Springer Verlag",
• address = "Madrid, Spain",
• pages = "68--84",
• month = "20-22 " # apr,
• organisation = "EvoStar, Species",
• keywords = "genetic algorithms, genetic programming, Feature Selection, Epistasis, Machine Learning",
• isbn13 = "978-3-031-02055-1",
• DOI = "doi:10.1007/978-3-031-02056-8_5",
• abstract = "We present SLUG, a method that uses genetic algorithms as a wrapper for genetic programming (GP), to perform feature selection while inducing models. This method is first tested on four regular binary classification datasets, and then on 10 synthetic datasets produced by GAMETES, a tool for embedding epistatic gene-gene interactions into noisy datasets. We compare the results of SLUG with the ones obtained by other GP-based methods that had already been used on the GAMETES problems, concluding that the proposed approach is very successful, particularly on the epistatic datasets. We discuss the merits and weaknesses of SLUG and its various parts, i.e. the wrapper and the learner, and we perform additional experiments, aimed at comparing SLUG with other state-of-the-art learners, like decision trees, random forests and extreme gradient boosting. Despite the fact that SLUG is not the most efficient method in terms of training time, it is confirmed as the most effective method in terms of accuracy.",
• notes = "http://www.evostar.org/2022/eurogp/ Part of \cite{Medvet:2022:GP} EuroGP'2022 held inconjunction with EvoApplications2022 EvoCOP2022 EvoMusArt2022",

}

Genetic Programming entries for Nuno Miguel Rodrigues Domingos Joao E Batista William La Cava Leonardo Vanneschi Sara Silva

Citations