GPF-CLASS: A Genetic Fuzzy Model for Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  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