GPF-CLASS: A Genetic Fuzzy Model for Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Koshiyama:2013:CEC,

• article_id = "1709",
• author = "Adriano Koshiyama and Tatiana Escovedo and Douglas Dias and Marley Vellasco and Ricardo Tanscheit",
• title = "GPF-CLASS: A Genetic Fuzzy Model for Classification",
• booktitle = "2013 IEEE Conference on Evolutionary Computation",
• volume = "1",
• year = "2013",
• month = jun # " 20-23",
• editor = "Luis Gerardo {de la Fraga}",
• pages = "3275--3282",
• address = "Cancun, Mexico",
• keywords = "genetic algorithms, genetic programming, multi-gene genetic programming",
• isbn13 = "978-1-4799-0453-2",
• DOI = "doi:10.1109/CEC.2013.6557971",
• size = "8 pages",
• 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.",
• notes = "CEC 2013 - A joint meeting of the IEEE, the EPS and the IET.",

}

Genetic Programming entries for Adriano Soares Koshiyama Tatiana Escovedo Douglas Mota Dias Marley Maria Bernardes Rebuzzi Vellasco Ricardo Tanscheit

Citations