@inproceedings{381f926a150648ec80d42085032fcbbb,
title = "GPF-CLASS: A genetic fuzzy model for classification",
abstract = "This work presents a Genetic Fuzzy Classification System (GFCS) called Genetic Programming Fuzzy Classification System (GPF-CLASS). This model differs from the traditional approach of GFCS, which uses the metaheuristic as a way to learn 'if-then' fuzzy rules. This classical approach needs several changes and constraints on the use of genetic operators, evaluation and selection, which depends primarily on the metaheuristic used. Genetic Programming makes this implementation costly and explores few of its characteristics and potentialities. The GPF-CLASS model seeks for a greater integration with the metaheuristic: Multi-Gene Genetic Programming (MGGP), exploring its potential of terminals selection (input features) and functional form and at the same time aims to provide the user with a comprehension of the classification solution. Tests with 22 benchmarks datasets for classification have been performed and, as well as statistical analysis and comparisons with others Genetic Fuzzy Systems proposed in the literature.",
keywords = "classification, genetic fuzzy systems, multi-gene genetic programming",
author = "Koshiyama, \{A. S.\} and T. Escovedo and Dias, \{D. M.\} and Vellasco, \{M. M.B.R.\} and R. Tanscheit",
year = "2013",
doi = "10.1109/CEC.2013.6557971",
language = "English",
isbn = "9781479904549",
series = "2013 IEEE Congress on Evolutionary Computation, CEC 2013",
pages = "3275--3282",
booktitle = "2013 IEEE Congress on Evolutionary Computation, CEC 2013",
note = "2013 IEEE Congress on Evolutionary Computation, CEC 2013 ; Conference date: 20-06-2013 Through 23-06-2013",
}