SLUG: Feature Selection Using Genetic Algorithms and Genetic Programming

Nuno M. Rodrigues, João E. Batista, William La Cava, Leonardo Vanneschi, Sara Silva

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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5 Downloads (Pure)

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.
Original languageEnglish
Title of host publicationGenetic Programming
Subtitle of host publication25th European Conference, EuroGP 2022, Held as Part of EvoStar 2022, Madrid, Spain, April 20–22, 2022, Proceedings
EditorsEric Medvet, Gisele Pappa, Bing Xue
PublisherSpringer
Chapter5
Pages68-84
Number of pages17
ISBN (Electronic)978-3-031-02056-8
ISBN (Print)978-3-031-02055-1
DOIs
Publication statusPublished - 13 Apr 2022
Event 25th European Conference on Genetic Programming - Virtual
Duration: 20 Apr 202222 Apr 2022
Conference number: 25
http://www.evostar.org/2022/eurogp/

Publication series

NameLecture Notes in Computer Science
Volume13223
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference 25th European Conference on Genetic Programming
Abbreviated titleEuroGP 2022
Period20/04/2222/04/22
Internet address

Keywords

  • Feature Selection
  • Epistasis
  • Genetic Programming
  • Genetic Algorithms
  • Machine Learning

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