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A Novel Framework to Design Fuzzy Rule-Based Ensembles Using Diversity Induction and Evolutionary Algorithms-Based Classifier Selection and Fusion

机译：基于分集归纳和进化算法的分类器选择与融合设计基于模糊规则的集成框架

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1. Multiobjective genetic classifier selection for random oracles fuzzy rule-based classifier ensembles: How beneficial is the additional diversity? [J] . Krzysztof Trawinski, Oscar Cordon, Arnaud Quirin, Knowledge-Based Systems . 2013,第deca期

机译：随机预言机的多目标遗传分类器选择基于模糊规则的分类器集成：附加多样性有何益处？
2. Evolutionary multiobjective optimization for the design of fuzzy rule-based ensemble classifiers [J] . Hisao Ishibuchi, Yusuke Nojima International Journal of Hybrid Intelligent Systems . 2006,第3期

机译：基于模糊规则的集成分类器设计的进化多目标优化
3. Mutual Information-based Feature Selection And Partition Design In Fuzzy Rule-based Classifiers From Vague Data [J] . Luciano Sanchez, M. Rosario Suarez, J.R. Villar, International Journal of Approximate Reasoning . 2008,第3期

机译：模糊数据的模糊规则分类器中基于互信息的特征选择和分区设计
4. A Novel Framework to Design Fuzzy Rule-Based Ensembles Using Diversity Induction and Evolutionary Algorithms-Based Classifier Selection and Fusion [C] . Oscar Cordon, Krzysztof Trawinski International work-conference on artificial neural networks . 2013

机译：基于分集归纳和基于进化算法的分类器选择和融合设计模糊规则集成的新框架
5. Evolutionary algorithms-based parallel simulation-optimization framework for solving inverse problems. [D] . Mirghani, BahaEldin Yousif Ahmed. 2007

机译：解决问题的基于进化算法的并行仿真优化框架。
6. Enhancing the Efficiency of the Interval-Valued Fuzzy Rule-Based Classifier with Tuning and Rule Selection [O] . José Antonio Sanz, Tiago da Cruz Asmus, Borja de la Osa, -1

机译：通过调整和规则选择提高基于区间值的模糊规则分类器的效率
7. Evolutionary multiobjective optimization for generating an ensemble of fuzzy rule-based classifiers [O] . Hisao Ishibuchi, Takashi Yamamoto 2003

机译：用于生成基于模糊规则的分类器集合的进化多目标优化

A Novel Framework to Design Fuzzy Rule-Based Ensembles Using Diversity Induction and Evolutionary Algorithms-Based Classifier Selection and Fusion

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