Feature Selection on Epistatic Problems Using Genetic Algorithms with Nested Classifiers

Pedro Carvalho, Bruno Ribeiro, Nuno M. Rodrigues, João E. Batista, Leonardo Vanneschi, Sara Silva

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Abstract

Feature selection is becoming an essential part of machine learning pipelines, including the ones generated by recent AutoML tools. In case of datasets with epistatic interactions between the features, like many datasets from the bioinformatics domain, feature selection may even become crucial. A recent method called SLUG has outperformed the state-of-the-art algorithms for feature selection on a large set of epistatic noisy datasets. SLUG uses genetic programming (GP) as a classifier (learner), nested inside a genetic algorithm (GA) that performs feature selection (wrapper). In this work, we pair GA with different learners, in an attempt to match the results of SLUG with less computational effort. We also propose a new feedback mechanism between the learner and the wrapper to improve the convergence towards the key features. Although we do not match the results of SLUG, we demonstrate the positive effect of the feedback mechanism, motivating additional research in this area to further improve SLUG and other existing feature selection methods.
Original languageEnglish
Title of host publicationApplications of Evolutionary Computation
Subtitle of host publication26th European Conference, EvoApplications 2023 Held as Part of EvoStar 2023 Brno, Czech Republic, April 12–14, 2023 Proceedings
EditorsJoão Correia, Stephen Smith, Raneem Qaddoura
Place of PublicationGewerbestrasse
PublisherSpringer
Chapter42
Pages656-671
Number of pages16
ISBN (Electronic)978-3-031-30229-9
ISBN (Print)978-3-031-30228-2
DOIs
Publication statusPublished - Apr 2023
Event26th International Conference on the Applications of Evolutionary Computation - Brno, Brno, Czech Republic
Duration: 12 Apr 202314 Apr 2023
Conference number: 26
https://www.evostar.org/2023/evoapps/

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume13989
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference26th International Conference on the Applications of Evolutionary Computation
Abbreviated titleEvoApplications 2023
Country/TerritoryCzech Republic
CityBrno
Period12/04/2314/04/23
Internet address

Keywords

  • Feature Selection
  • Epistasis
  • Genetic Algorithms
  • Genetic Programming
  • Decision Trees
  • Machine Learning
  • Genome-Wide Association Studies

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