Inducing Rules for Expert System Development: An Example Using Default and Bankruptcy Data
通过实验评估归纳算法在金融数据中发现预测知识结构的有效性，并与判别分析、个人判断和群体判断的结果比较，发现归纳产生的知识结构在所有测试案例中表现更优。
With rapidly growing interest in the development of knowledge-based computer consulting systems for various problem domains, the difficulties associated with knowledge acquisition have special importance. This paper reports on the results of experiments designed to assess the effectiveness of an inductive algorithm in discovering predictive knowledge structures in financial data. The quality of the results are evaluated by comparing them to results generated by discriminant analysis, individual judgments, and group judgments. A partial intersection of predictive attributes occurs. More importantly, for all cases tested, the inductively produced knowledge structures perform better than the competing models.