Design of hierarchical fuzzy model for classification problem using GAs

Guo, Nai-Wen; Li, Tzuu-Hseng S.; Kuo, Chao-Lin

doi:10.1016/j.cie.2005.06.007

Cited by 10 publications

(1 citation statement)

References 29 publications

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“…From previous experience in [33]- [35], the classification rate of the Iris dataset is lower than the Wine data set. The reason might be that the optimum classification rate occurred only when the training samples contain sufficient information.…”

Section: Resultsmentioning

confidence: 99%

Advanced Hierarchical Fuzzy Classification Model Adopting Symbiosis Based DNA-ABC Optimization Algorithm

Feng¹,

Li²

2016

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This paper offers a symbiosis based hybrid modified DNA-ABC optimization algorithm which combines modified DNA concepts and artificial bee colony (ABC) algorithm to aid hierarchical fuzzy classification. According to literature, the ABC algorithm is traditionally applied to constrained and unconstrained problems, but is combined with modified DNA concepts and implemented for fuzzy classification in this present research. Moreover, from the best of our knowledge, previous research on the ABC algorithm has not combined it with DNA computing for hierarchical fuzzy classification to explore the merits of cooperative coevolution. Therefore, this paper is the first to apply the mechanism of symbiosis to create a hybrid modified DNA-ABC algorithm for hierarchical fuzzy classification applications. In this study, the partition number and the shape of the membership function are extracted by the symbiosis based hybrid modified DNA-ABC optimization algorithm, which provides both sufficient global exploration and also adequate local exploitation for hierarchical fuzzy classification. The proposed optimization algorithm is applied on five benchmark University of Irvine (UCI) data sets, and the results prove the efficiency of the algorithm.

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Section: Resultsmentioning

confidence: 99%