1997 Volume 12 Issue 3 Pages 421-429
This paper studies the problem of learning decision trees when the attributes of the domain are tree-structured. Quinlan suggests a pre-processing approach to this problem. When the size of the hierarchies used is huge, Quinlan's approach is not efficient and effective. We introduce our own approach which handles tree-structured attributes directly without the need for pre-processing. We present experiments on natural and artificial data that suggest that our direct approach leads to better generalization performance than the Quinlan-encoding approach and runs roughly two to four times faster.