Resumen de Synthesis of Fuzzy Pattern Trees by Cartesian Genetic Programming
This work presents a system for induction of fuzzy classifiers. Instead of the tra-ditional fuzzy based rules, it was used a model called Fuzzy Pattern Trees (FPT), which is a hierarchical tree-based model, having as internal nodes, fuzzy logical operators and the leaves are composed of a combination of fuzzy terms and the input attributes. The classifier was obtained by creating a tree for each class. This tree will be a ``logic class description'' which allows the interpretation of the re-sults. The learning method originally designed for generating a FPT was replaced by Cartesian Genetic Programming in order to provide a better exploration of the search space. The FPT classifier was compared against Support Vector Ma-chines, K Nearest Neighbors and Random Forests on several datasets from the UCI Machine Learning Repository and it presented competitive results. It was al-so compared with Fuzzy Pattern trees generated by the former learning method and presented comparable results with smaller trees.
