GPFIS-CLASS: A Genetic Fuzzy System based on Genetic Programming for classification problems
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- @Article{Koshiyama:2015:ASC,
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author = "Adriano S. Koshiyama and Marley M. B. R. Vellasco and
Ricardo Tanscheit",
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title = "{GPFIS-CLASS}: A Genetic Fuzzy System based on Genetic
Programming for classification problems",
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journal = "Applied Soft Computing",
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volume = "37",
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pages = "561--571",
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year = "2015",
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ISSN = "1568-4946",
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DOI = "doi:10.1016/j.asoc.2015.08.055",
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URL = "http://www.sciencedirect.com/science/article/pii/S1568494615005670",
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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.",
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keywords = "genetic algorithms, genetic programming, Genetic Fuzzy
System, Classification",
- }
Genetic Programming entries for
Adriano Soares Koshiyama
Marley Maria Bernardes Rebuzzi Vellasco
Ricardo Tanscheit
Citations