Grammar-based Fuzzy Pattern Trees for Classification Problems

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


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.


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