- 著者
-
アルモアリム フセイン
秋葉 泰弘
金田 重郎
- 出版者
- 社団法人人工知能学会
- 雑誌
- 人工知能学会誌 (ISSN:09128085)
- 巻号頁・発行日
- vol.12, no.3, pp.421-429, 1997-05-01
- 被引用文献数
-
7
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.