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Enhancing rule interestingness for neuro-fuzzy systems

机译:增强神经模糊系统的规则趣味性

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Data Mining Algorithms extract patterns from large amounts of data.But these patterns will yield knowledge only if they are interesting,i.e.vallid,new,potentially useful,and understandable.Unfortunately,during pattern search most Data MIning Algorithms focus on validity only,which also holds true for Neuro-Fuzzy Systems.In this Paper we introduce a method to enhance the interesting ness of a rule base as a whole.In the first step,we aggregate the rule base through amalgamation of adjacent rules and eliminiation of redundant attributes.Supplementing this rather technical approach,we next sort rules with regard to their performance,as measured by their evidence.Finally,we compute reduced evidences,which penalize rules that are very similar to rules with a higher evidence.Rules sorted on reduced evidence are fed into an integrated rulebrowser,to allow for manual rule seelction according to personal and situation-dependent preference.This method was applied successfully to two real-life classification problems,the target group selection for a retail bank,and fault diagnosis for a large car manufacturer.Explicit reference is taken to the NEFCLASS algorithm,but the procedure is easily generalized to other systems.
机译:数据挖掘算法从大量数据中提取模式。但是,只有在模式有趣,valval,新,潜在有用且易于理解时,这些模式才会产生知识。不幸的是,在模式搜索过程中,大多数数据挖掘算法仅关注有效性,这也这对于Neuro-Fuzzy系统是成立的。本文介绍了一种增强规则库整体趣味性的方法。第一步,我们通过合并相邻规则并消除冗余属性来汇总规则库。这种比较技术性的方法,我们将根据其证据对规则的性能进行排序。最后,我们计算简化的证据,对与具有较高证据的规则非常相似的规则进行惩罚。将对简化证据进行排序的规则输入一个集成的规则浏览器,可以根据个人和与情况有关的偏好进行手动规则合并。该方法已成功应用于两个现实生活中分配问题,零售银行的目标群体选择和大型汽车制造商的故障诊断。对NEFCLASS算法进行了明确引用,但该过程很容易推广到其他系统。

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