Using cultural algorithms to support re-engineering of rule-based expert systems in dynamic performance environments: a case study in fraud detection
Using cultural algorithms to support re-engineering of rule-based expert systems in dynamic performance environments: a case study in fraud detection
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使用文化算法支持动态性能环境中基于规则的专家系统的重新设计：欺诈检测的案例研究
DOI：
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发表时间：
1997
影响因子：
14.3
通讯作者：
R. Reynolds
中科院分区：
文献类型：
--
作者：
Michael Sternberg;R. Reynolds
在应用以规则为基础的专家系统方面出现的一个重要问题是如何重新设计这种系统以支持初始要求的变化。在动态性能环境中，变化的速度加快，重新设计的问题变得更加复杂。响应这种动态变化的一种机制是利用文化算法（CA）。CA提供了自适应能力，可以产生必要的信息，专家系统动态响应。为了说明这种方法，欺诈检测专家系统嵌入在CA。为了表示动态性能环境，使用了四个不同的应用程序目标。目标是表征欺诈性索赔、非欺诈性索赔、假阳性索赔和假阴性索赔。结果表明，文化使能的专家系统可以产生必要的信息，以应对动态性能环境。
A significant problem in the application of rule-based expert systems has arisen in the area of re-engineering such systems to support changes in initial requirements. In dynamic performance environments, the rate of change is accelerated and the re-engineering problem becomes significantly more complex. One mechanism to respond to such dynamic changes is to utilize a cultural algorithm (CA). The CA provides self-adaptive capabilities which can generate the information necessary for the expert system to respond dynamically. To illustrate the approach, a fraud detection expert system was embedded inside a CA. To represent a dynamic performance environment, four different application objectives were used. The objectives were characterizing fraudulent claims, nonfraudulent claims, false positive claims, and false negative claims. The results indicate that a culturally enabled expert system can produce the information necessary to respond to dynamic performance environments.