Development and Maintenance of Fuzzy Models in Financial Applications
نویسنده
چکیده
Our goal is to illustrate the typical life cycle of a fuzzy knowledge-based model, starting from its development, testing, optimization, and deployment, and ending with the maintenance of its knowledge base. We illustrate this process within the context of an underwriting insurance application. First we define some key concepts of soft computing models and discuss some design tradeoffs that must be addressed. Then we focus on the design and implementation of a fuzzy rule-based classifier (FRC). We establish a standard reference dataset (SRD), consisting of 3,000 insurance applications with their corresponding decisions. The SRD exemplifies the results achieved by an ideal, optimal classifier, and represents the target for our design. We apply evolutionary algorithms to perform an off-line optimization of the design parameters of the classifier, modifying its behavior to approximate this target. The SRD is also used as a reference for testing and performing a five-fold cross-validation of the classifiers. Finally, we focus on the monitoring and maintenance of the FRC. We describe a fusion architecture that supports an off-line quality assurance process of the on-line FRC. The fusion module takes the outputs of multiple classifiers, determines their degree of consensus, and compares their overall agreement with the decision made by the FRC. From this analysis, we can identify the most suitable cases to update the SRD, to audit, or to be reviewed by senior underwriters.
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تاریخ انتشار 2004