Physical Sciences and Mathematics Commons^™

Minimizing Recommended Error Costs Under Noisy Inputs In Rule-Based Expert Systems, Forest D. Thola

CCE Theses and Dissertations

This dissertation develops methods to minimize recommendation error costs when inputs to a rule-based expert system are prone to errors. The problem often arises in web-based applications where data are inherently noisy or provided by users who perceive some benefit from falsifying inputs. Prior studies proposed methods that attempted to minimize the probability of recommendation error, but did not take into account the relative costs of different types of errors. In situations where these differences are significant, an approach that minimizes the expected misclassification error costs has advantages over extant methods that ignore these costs.

Building on the existing literature, …