AUTOMATED EXTRACTION OF ATTRIBUTE HIERARCHIES FOR AN IMPROVED DECISION-TREE CLASSIFIER

A method to automatically extract inherent hierarchies of attribute values from data is proposed to improve the performance of a decision-tree classifier. When attributes used for a decision tree take continuous values, simple decision rules such as ''IF a certain attribute is less than a certain value'' yield good results in many cases. The rationale of this type of rule is the natural tendency that two nearby attribute values have a ''similar meaning'' in the sense that they suggest the same class with high probability. When only discrete-type, unordered attributes are available for tree generation, however, such efficient decision rules are hard to obtain because ''distance relationships'' between the attribute values are seldom known beforehand. In order to solve this problem, the proposed method estimates from the training data set a conceptual distance between each pair of attribute values, and by iteratively grouping two attribute values with minimum distance, generates hierarchies of attribute values, which are then utilized for making the decision tree. This method is applied to the task of fault diagnosis of a certain printed circuit board, and it is indicated that the generated attribute hierarchies can reduce the size of a decision tree sufficiently, which results in a significant improvement of its classification accuracy.