Grammar-based Fuzzy Pattern Trees for Classification Problems

Aidan Murphy, Muhammad Sarmad Ali, Douglas Mota Dias, Douglas Mota Dias, Jorge Amaral, Enrique Naredo, Conor Ryan

2020

Abstract

This paper introduces a novel approach to induce Fuzzy Pattern Trees (FPT) using Grammatical Evolution (GE), FGE, and applies to a set of benchmark classification problems. While conventionally a set of FPTs are needed for classifiers, one for each class, FGE needs just a single tree. This is the case for both binary and multi-classification problems. Experimental results show that FGE achieves competitive and frequently better results against state of the art FPT related methods, such as FPTs evolved using Cartesian Genetic Programming (FCGP), on a set of benchmark problems. While FCGP produces smaller trees, FGE reaches a better classification performance. FGE also benefits from a reduction in the number of necessary user-selectable parameters. Furthermore, in order to tackle bloat or solutions growing too large, another version of FGE using parsimony pressure was tested. The experimental results show that FGE with this addition is able to produce smaller trees than those using FCGP, frequently without compromising the classification performance.

Paper Citation

in Harvard Style

Murphy A., Ali M., Dias D., Amaral J., Naredo E. and Ryan C. (2020). Grammar-based Fuzzy Pattern Trees for Classification Problems. In Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - Volume 1: ECTA; ISBN 978-989-758-475-6, SciTePress, pages 71-80. DOI: 10.5220/0010111900710080

in Bibtex Style

@conference{ecta20,
author={Aidan Murphy and Muhammad Sarmad Ali and Douglas Mota Dias and Jorge Amaral and Enrique Naredo and Conor Ryan},
title={Grammar-based Fuzzy Pattern Trees for Classification Problems},
booktitle={Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - Volume 1: ECTA},
year={2020},
pages={71-80},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010111900710080},
isbn={978-989-758-475-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - Volume 1: ECTA
TI - Grammar-based Fuzzy Pattern Trees for Classification Problems
SN - 978-989-758-475-6
AU - Murphy A.
AU - Ali M.
AU - Dias D.
AU - Amaral J.
AU - Naredo E.
AU - Ryan C.
PY - 2020
SP - 71
EP - 80
DO - 10.5220/0010111900710080
PB - SciTePress