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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/4655

Title: A Visualization Tool for Interactive Learning of Large Decision Trees
Authors: Nguyen, Trong Dung
Ho, Tu Bao
Shimodaira, Hiroshi
Issue Date: 2000-11
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Magazine name: Proceedings. 12th IEEE International Conference on Tools with Artificial Intelligence, 2000. ICTAI 2000.
Start page: 28
End page: 35
DOI: 10.1109/TAI.2000.889842
Abstract: Decision tree induction is certainly among the most applicable learning techniques due to its power and simplicity. However learning decision trees from large datasets, particularly in data mining, is quite different from learning from small or moderately sized datasets. When learning from large datasets, decision tree induction programs often produce very large trees. How to visualize efficiently trees in the learning process, particularly large trees, is still questionable and currently requires efficient tools. This paper presents an visualization tool for interactive learning of large decision trees, that includes a new visualization technique called T2.5D (satnds for Trees 2.5 Dimensions). After a brief discussion on requirements for tree visualizers and related work, the paper focuses on presenting developing techniques for two issues: (1) how to visualize efficiently large decision trees; and (2) how to visualize decision trees in the learning process.AR83
Rights: Copyright (c)2000 IEEE. Reprinted from Proceedings. 12th IEEE International Conference on Tools with Artificial Intelligence, 2000. ICTAI 2000., 13-15 Nov. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of JAIST's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
URI: http://hdl.handle.net/10119/4655
Material Type: publisher
Appears in Collections:a11-1. 会議発表論文 (Conference Papers)

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