Presentation 1996/1/18
Decision Tree Learner Handling Tree-Structured Attributes
Yasuhiro AKIBA, Hussein ALMUALLIM, Shigeo KANEDA,
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Abstract(in English) 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, his 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.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Machine Learning / Knowledge Acquisition / Natural Language Processing
Paper # AI95-45
Date of Issue

Conference Information
Committee AI
Conference Date 1996/1/18(1days)
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Paper Information
Registration To Artificial Intelligence and Knowledge-Based Processing (AI)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Decision Tree Learner Handling Tree-Structured Attributes
Sub Title (in English)
Keyword(1) Machine Learning
Keyword(2) Knowledge Acquisition
Keyword(3) Natural Language Processing
1st Author's Name Yasuhiro AKIBA
1st Author's Affiliation NTT Communication Science Labs()
2nd Author's Name Hussein ALMUALLIM
2nd Author's Affiliation King Fahd University of Petroleum and Minerals
3rd Author's Name Shigeo KANEDA
3rd Author's Affiliation NTT Communication Science Labs
Date 1996/1/18
Paper # AI95-45
Volume (vol) vol.95
Number (no) 460
Page pp.pp.-
#Pages 8
Date of Issue