SAFARI: a structured approach for automatic rule

This paper describes a new algorithm for obtaining rules automatically from training examples. The algorithm is applicable to examples involving both objects: with discrete and continuous-valued attributes. The paper explains a new quantization procedure fur continuous-valued attributes and shows how appropriate ranges of values of various attributes are obtained. The algorithm uses a decision-tree-based approach for obtaining rules, but unlike other tree-based algorithms such as ID3, it allows more than one attribute at a node which greatly improves its performance. The ability of the algorithm to obtain a measure of partial match further enhances its generalization characteristic. The algorithm produces the same rules irrespective of the order of presentation of training examples. The algorithm has been demonstrated on classification problems. The results have compared favorably with those obtained by existing inductive learning algorithms