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A Multi-dimensional Genetic Programming Approach for Multi-class Classification Problems

  • Conference paper
Genetic Programming (EuroGP 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8599))

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Abstract

Classification problems are of profound interest for the machine learning community as well as to an array of application fields. However, multi-class classification problems can be very complex, in particular when the number of classes is high. Although very successful in so many applications, GP was never regarded as a good method to perform multi-class classification. In this work, we present a novel algorithm for tree based GP, that incorporates some ideas on the representation of the solution space in higher dimensions. This idea lays some foundations on addressing multi-class classification problems using GP, which may lead to further research in this direction. We test the new approach on a large set of benchmark problems from several different sources, and observe its competitiveness against the most successful state-of-the-art classifiers.

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Author information

Authors and Affiliations

  1. INESC-ID, Lisbon, Portugal

    Vijay Ingalalli & Sara Silva

  2. LIRMM, Montpellier, France

    Vijay Ingalalli

  3. IRSTEA, Montpellier, France

    Vijay Ingalalli

  4. LabMAg, FCUL, University of Lisbon, 1749-016, Lisbon, Portugal

    Sara Silva

  5. CISUC, Universidade de Coimbra, 3030-290, Coimbra, Portugal

    Sara Silva

  6. ISEGI, Universidade Nova de Lisboa, 1070-312, Lisbon, Portugal

    Mauro Castelli & Leonardo Vanneschi

Authors
  1. Vijay Ingalalli
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  2. Sara Silva
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  3. Mauro Castelli
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  4. Leonardo Vanneschi
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Editor information

Editors and Affiliations

  1. University College Dublin, UCD CASL, Belfield, Dublin 4, Ireland

    Miguel Nicolau

  2. Institute of Computing Science, Poznan University of Technology, Piotowo 2, 60-965, Poznań, Poland

    Krzysztof Krawiec

  3. Faculty of Computer Science, Dalhousie University, University Avenue 6050, B3H 1W5, Halifax, NS, Canada

    Malcolm I. Heywood

  4. Instituto Superior de Estatística e Gestão de Informaticão, Universidade Nova de Lisboa, 1070-312, Lisbon, Portugal

    Mauro Castelli

  5. Departamento de Arquitectura y Tecnología de Computadores (ETS), Universidad de Granada, 18071, Granada, Spain

    Pablo García-Sánchez  & Juan J. Merelo  & 

  6. Departamento de Informática, Universidad de Jaén, 23071, Jaén, Spain

    Victor M. Rivas Santos

  7. Institute for Informatics and Digital Innovation, Edinburgh Napier University, Merchiston Campus, EH10 5DT, Edinburgh, UK

    Kevin Sim

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Ingalalli, V., Silva, S., Castelli, M., Vanneschi, L. (2014). A Multi-dimensional Genetic Programming Approach for Multi-class Classification Problems. In: Nicolau, M., et al. Genetic Programming. EuroGP 2014. Lecture Notes in Computer Science, vol 8599. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44303-3_5

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  • DOI: https://doi.org/10.1007/978-3-662-44303-3_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-44302-6

  • Online ISBN: 978-3-662-44303-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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