Induction of Expert System Rules from Databases Based on Rough Set Theory and Resampling Methods

Automated knowledge acquisition is an important research issue to solve the bottleneck problem in developing expert systems. There have been proposed several methods of inductive learning, such as induction of decision trees, AQ method, and neural networks for this purpose. However, most of the approaches focus on inducing some rules which classify cases correctly. On the contrary, medical experts also learn other information which is important for medical diagnostic procedures from databases. In this paper, a ruleinduction system, called PRIMEROSEREX (Probabilistic Rule Induction Method based on Rough Sets and Resampling methods for Expert systems), is introduced. This program extracts not only classification rules for differential diagnosis, but also other medical knowledge needed for other diagnostic procedures, based on a diagnosing model of a medical expert system RHINOS (Rule-based Headache and facial pain INformation Orgranizing System). This system is evaluated by using training samples of RHINOS domain, and the induced results are compared with rules acquired from medical experts. The results show that our proposed method correctly induces RHINOS rules and estimate the statistical measures of rules.

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