TY - GEN
T1 - Grammar-based Fuzzy Pattern Trees for Classification Problems
AU - Murphy, Aidan
AU - Ali, Muhammad Sarmad
AU - Dias, Douglas Mota
AU - Amaral, Jorge
AU - Naredo, Enrique
AU - Ryan, Conor
N1 - Publisher Copyright:
© 2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.
PY - 2020
Y1 - 2020
N2 - 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 userselectable 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.
AB - 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 userselectable 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.
KW - Fuzzy Logic
KW - Grammatical Evolution
KW - Pattern Trees
UR - http://www.scopus.com/inward/record.url?scp=85190696494&partnerID=8YFLogxK
U2 - 10.5220/0010111900710080
DO - 10.5220/0010111900710080
M3 - Conference contribution
AN - SCOPUS:85103815080
SN - 9789897584756
T3 - International Joint Conference on Computational Intelligence
SP - 71
EP - 80
BT - Proceedings of the 12th International Joint Conference on Computational Intelligence, IJCCI 2020
A2 - Merelo, Juan Julian
A2 - Garibaldi, Jonathan
A2 - Wagner, Christian
A2 - Bäck, Thomas
A2 - Madani, Kurosh
A2 - Warwick, Kevin
PB - Science and Technology Publications, Lda
T2 - 12th International Joint Conference on Computational Intelligence, IJCCI 2020
Y2 - 2 November 2020 through 4 November 2020
ER -