Knowledge acquisition is a bottleneck for expert system design. One way to overcome this bottleneck is to induce expert system rules from sample data. This paper presents Q new induction approach called CRIS. The key notion employed in CRIS is that nommal and nonnomma! attributes have different characteristics and hence should be analyzed differently. In the beginning of the paper, the benefits of this approach are deseribed. Next, the basic elements of the CRIS approach are discussed and illustrated. 1 his is followed by a series of empirical comparisons of the predictive validity of CRIS versus two entropy-based induction methods (ACLS and PLSl). stalistical discriminant analysis, and the back propagation method in neural networks. These comparisons all indicate thai CRIS has higher predictive validity. The implications of the findings for expert systems design are discussed m the conclusion of the paper
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