Journal of the Japanese Society for Artificial Intelligence
Online ISSN : 2435-8614
Print ISSN : 2188-2266
Print ISSN:0912-8085 until 2013
On Handling Tree-Structured Attributes in Decision Tree Learning
Hussein ALMUALLIMYasuhiro AKIBAShigeo KANEDA
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1997 Volume 12 Issue 3 Pages 421-429

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Abstract

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.

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© 1997 The Japaense Society for Artificial Intelligence
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