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Fuzzy Pattern Trees for Classification Problems Using Genetic Programming

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Genetic Programming (EuroGP 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14631))

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Abstract

Fuzzy Pattern Trees (FPTs) are tree-based structures in which the internal nodes are fuzzy operators, and the leaves are fuzzy features. This work uses Genetic Programming (GP) to evolve FPTs and assesses their performance on 20 benchmark classification problems. The results show improved accuracy for most of the problems in comparison with previous works using different approaches. Furthermore, we experiment using Lexicase Selection with FPTs and demonstrate that selection methods based on aggregate fitness, such as Tournament Selection, produce more accurate models before analysing why this is the case. We also propose new parsimony pressure methods embedded in Lexicase Selection, and analyse their ability to reduce the size of the solutions. The results show that for most problems, at least one method could reduce the size significantly while keeping a similar accuracy. We also introduce a new fuzzification scheme for categorical features with too many categories by using target encoding followed by the same scheme for numerical features, which is straightforward to implement, and avoids a much higher increase in the number of fuzzy features.

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Notes

  1. 1.

    https://github.com/bdsul/grape/tree/main/GP/ClassificationFPT.

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Author information

Authors and Affiliations

  1. University of Limerick, Limerick, Ireland

    Allan de Lima, Douglas Mota Dias & Conor Ryan

  2. Technological University of the Shannon: Midlands Midwest, Limerick, Ireland

    Samuel Carvalho & Joseph P. Sullivan

  3. Rio de Janeiro State University, Rio de Janeiro, Brazil

    Jorge Amaral

Authors
  1. Allan de Lima
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  2. Samuel Carvalho
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  3. Douglas Mota Dias
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  4. Jorge Amaral
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  5. Joseph P. Sullivan
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  6. Conor Ryan
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Correspondence to Allan de Lima or Conor Ryan .

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Editors and Affiliations

  1. University of Torino, Grugliasco, Italy

    Mario Giacobini

  2. Victoria University of Wellington, Wellington, New Zealand

    Bing Xue

  3. University of Trieste, Trieste, Italy

    Luca Manzoni

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de Lima, A., Carvalho, S., Dias, D.M., Amaral, J., Sullivan, J.P., Ryan, C. (2024). Fuzzy Pattern Trees for Classification Problems Using Genetic Programming. In: Giacobini, M., Xue, B., Manzoni, L. (eds) Genetic Programming. EuroGP 2024. Lecture Notes in Computer Science, vol 14631. Springer, Cham. https://doi.org/10.1007/978-3-031-56957-9_1

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  • DOI: https://doi.org/10.1007/978-3-031-56957-9_1

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