A Composite Approach to Inducing Knowledge for Expert Systems Design

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 a new induction approach called CRIS. The key notion employed in CRIS is that nominal and nonnominal attributes have different characteristics and hence should be analyzed differently. In the beginning of the paper, the benefits of this approach are described. Next, the basic elements of the CRIS approach are discussed and illustrated. This is followed by a series of empirical comparisons of the predictive validity of CRIS versus two entropy-based induction methods (ACLS and PLS1), statistical discriminant analysis, and the backpropagation method in neural networks. These comparisons all indicate that CRIS has higher predictive validity. The implications of the findings for expert systems design are discussed in the conclusion of the paper.