- 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 - ECTA",
- year = "2020",
- pages = "71--80",
- address = "Online",
- month = "2-4 " # nov,
- organization = "INSTICC",
- publisher = "SciTePress",
- note = "Best Student paper",
- keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Pattern Trees, Fuzzy Logic",
- isbn13 = "978-989-758-475-6",
- ISSN = "2184-2825",
-
URL = "
http://www.human-competitive.org/sites/default/files/murphy_humies.txt",
-
URL = "
http://www.human-competitive.org/sites/default/files/ecta20.pdf",
-
DOI = "
doi:10.5220/0010111900710080",
- size = "10 pages",
- 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.",
-
notes = "Entered 2021 HUMIES
University of Limerick, Limerick, Ireland",
