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A Review on MOEA and Metaheuristics for Feature-Selection

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Innovations in Bio-Inspired Computing and Applications (IBICA 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 419))

Abstract

In the areas of machine-learning/big data, feature selection is normally regarded as a very important problem to be solved, as it directly impacts both data analysis and model creation. The problem of optimizing the selected features of a given dataset is not always trivial, however, throughout the years various ways to counter this optimization problem have been presented. This work presents how feature-selection fits in the larger context of multi-objective problems as well as a review of how both multi-objective evolutionary algorithms and metaheuristics are being used in order to solve feature selection problems.

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Notes

  1. 1.

    Pareto optimality can be roughly defined as a state at which resources in a given system are optimized such that one dimension cannot improve without a second worsening. “The main idea (...) is that a society is enjoying maximum ophelimity when no one can be made better off without making someone else worse off” [7].

  2. 2.

    Involving or serving as an aid to learning, discovery, or problem-solving by experimental and especially trial-and-error methods (in Merriam-Webster Dictionary).

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Acknowledgments

This article is a result of the project “Criação de um Núcleo de I&D para a geração de novo conhecimento nas áreas de Inteligência Artificial, Machine Learning, Intelligent Marketing e One-2-One Marketing”, supported by Operational Programme for Competitiveness and Internationalization (COMPETE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF).

Author information

Authors and Affiliations

  1. E-goi, Matosinhos, Portugal

    Duarte Coelho & Ivo Pereira

  2. Interdisciplinary Studies Research Center (ISRC), ISEP/IPP, Porto, Portugal

    Ana Madureira & Ivo Pereira

  3. University Fernando Pessoa (UFP), Porto, Portugal

    Ivo Pereira

  4. Universidade de Trás-os-Montes e Alto Douro (UTAD), Vila Real, Portugal

    Duarte Coelho & Ramiro Gonçalves

Authors
  1. Duarte Coelho
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  2. Ana Madureira
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  3. Ivo Pereira
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  4. Ramiro Gonçalves
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Corresponding author

Correspondence to Ivo Pereira .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. Departamento de Engenharia Informática, Instituto Superior de Engenharia do Port, Porto, Portugal

    Ana Maria Madureira

  3. Department of Construction Management and Real Estate, Vilnius Gediminas Technical University, Vilnius, Lithuania

    Arturas Kaklauskas

  4. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Niketa Gandhi

  5. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Anu Bajaj

  6. Faculty of Information Communication Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia

    Azah Kamilah Muda

  7. Vilnius University, Kaunas, Lithuania

    Dalia Kriksciuniene

  8. Lisbon University Institute, Lisbon, Portugal

    João Carlos Ferreira

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Coelho, D., Madureira, A., Pereira, I., Gonçalves, R. (2022). A Review on MOEA and Metaheuristics for Feature-Selection. In: Abraham, A., et al. Innovations in Bio-Inspired Computing and Applications. IBICA 2021. Lecture Notes in Networks and Systems, vol 419. Springer, Cham. https://doi.org/10.1007/978-3-030-96299-9_21

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