GPFIS-CLASS: A Genetic Fuzzy System based on Genetic Programming for classification problems

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@Article{Koshiyama:2015:ASC,
  author =       "Adriano S. Koshiyama and Marley M. B. R. Vellasco and 
                 Ricardo Tanscheit",
  title =        "GPFIS-CLASS: A Genetic Fuzzy System based on Genetic
                 Programming for classification problems",
  journal =      "Applied Soft Computing",
  volume =       "37",
  pages =        "561--571",
  year =         "2015",
  ISSN =         "1568-4946",
  DOI =          "doi:10.1016/j.asoc.2015.08.055",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1568494615005670",
  abstract =     "Genetic Fuzzy Systems (GFSs) are models capable of
                 integrating accuracy and high comprehensibility in
                 their results. In the case of GFSs for classification,
                 more emphasis has been given to improving the
                 {"}Genetic{"} component instead of its {"}Fuzzy{"}
                 counterpart. This paper focus on the Fuzzy Inference
                 component to obtain a more accurate and interpretable
                 system, presenting the so-called Genetic Programming
                 Fuzzy Inference System for Classification
                 (GPFIS-CLASS). This model is based on Multi-Gene
                 Genetic Programming and aims to explore the elements of
                 a Fuzzy Inference System. GPFIS-CLASS has the following
                 features: (i) it builds fuzzy rules premises employing
                 t-norm, t-conorm, negation and linguistic hedge
                 operators; (ii) it associates to each rule premise a
                 suitable consequent term; and (iii) it improves the
                 aggregation process by using a weighted mean computed
                 by restricted least squares. It has been evaluated in
                 two sets of benchmarks, comprising a total of 45
                 datasets, and has been compared with eight different
                 classifiers, six of them based on GFSs. The results
                 obtained in both sets demonstrate that GPFIS-CLASS
                 provides better results for most benchmark datasets.",
  keywords =     "genetic algorithms, genetic programming, Genetic Fuzzy
                 System, Classification",
}

Genetic Programming entries for Adriano Soares Koshiyama Marley Maria Bernardes Rebuzzi Vellasco Ricardo Tanscheit

Citations