2015 IEEE Congress on Evolutionary Computation (CEC) 2015
DOI: 10.1109/cec.2015.7257284
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Evolutionary multi-objective optimization for evolving hierarchical fuzzy system

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Cited by 6 publications
(2 citation statements)
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“…In the different tests, we compared our model with other kind of classifiers like SVM [ [43] and Fuzzy system HFBFS [44]. It resulted in a satisfactory improvement with range around 3% with Leukemia dataset, between 4% and 6% with Lymphoma dataset, between 0.3% and 8% with lung cancer dataset, and more than 10% with Colon cancer dataset.…”
Section: Resultsmentioning
confidence: 66%
“…In the different tests, we compared our model with other kind of classifiers like SVM [ [43] and Fuzzy system HFBFS [44]. It resulted in a satisfactory improvement with range around 3% with Leukemia dataset, between 4% and 6% with Lymphoma dataset, between 0.3% and 8% with lung cancer dataset, and more than 10% with Colon cancer dataset.…”
Section: Resultsmentioning
confidence: 66%
“…Recently, hierarchical fuzzy design has attracted increasing attentions and many works have been proposed to build or to optimize these systems [16][17][18][19][20][21][22]. However, most of the existing hierarchical systems employed type-1 fuzzy models.…”
Section: Introductionmentioning
confidence: 99%