This paper concentrates on the development of precise fuzzy rule-based classification systems for high-dimensional and multi-class problems. The approach begins with the extraction on potential fuzzy if-then rules using fitness sharing based genetic algorithms, this ensures effective searching for productive niches, thereby evolving and maintaining a diverse, cooperative population. Subsequently, for the purpose of combining the obtained fuzzy rules and eliminating their conflicts, an adaboost ensemble method is utilized, enhancing the accuracy of the fuzzy classification systems.

中文翻译：

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基于适应度共享的遗传搜索和基于提升的集成构建模糊分类系统

本文专注于针对高维和多类问题开发基于精确模糊规则的分类系统。该方法首先使用基于适应度共享的遗传算法提取潜在的模糊 if-then 规则，这确保了有效搜索生产性生态位，从而进化和维持多样化的合作群体。随后，为了组合所获得的模糊规则并消除它们的冲突，利用adaboost集成方法，提高了模糊分类系统的准确性。