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A Fuzzy Association Rule Mining Expert-Driven (FARME-D) approach to Knowledge Acquisition

机译:知识获取的模糊关联规则挖掘专家驱动(FARME-D)方法

摘要

Fuzzy Association Rule Mining Expert-Driven (FARME-D) approach to knowledge acquisition is proposed in this paper as a viable solution to the challenges of rule-based unwieldiness and sharp boundary problem in building a fuzzy rule-based expert system. The fuzzy models were based on domain experts’ opinion about the data description. The proposed approach is committed to modelling of audcompact Fuzzy Rule-Based Expert Systems. It is also aimed at providing a platform for instant update of the knowledge-base in case new knowledge is discovered. The insight to the new approach strategies and underlining assumptions, the structure of FARME-D and itsudpractical application in medical domain was discussed. Also, the modalities for the validation of the FARME-D approach were discussed.
机译:本文提出了一种基于模糊关联规则挖掘的专家驱动(FARME-D)知识获取方法,以解决基于规则的繁琐性和尖锐边界问题在构建基于模糊规则的专家系统中的挑战。模糊模型基于领域专家对数据描述的观点。所提出的方法致力于对基于模糊规则的专家系统进行建模。它还旨在提供一个平台,以便在发现新知识的情况下即时更新知识库。讨论了对新方法策略的认识和强调的假设,FARME-D的结构及其在医学领域的实际应用。此外,讨论了验证FARME-D方法的方式。

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