Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/23267
Title: Monotonic decision tree for ordinal classification
Authors: Lee, JWT
Yeung, DS
Wang, X
Keywords: Decision making
Decision trees
Pattern classification
Regression analysis
Issue Date: 2003
Publisher: IEEE
Source: IEEE International Conference on Systems, Man and Cybernetics, 2003, 5-8 October 2003, v. 3, p. 2623-2628 How to cite?
Abstract: While ordinal classification problems are common in many situations, induction of ordinal decision trees has not been very extensiveness studied. They are commonly treated as nominal classification problem or regression problem in tree induction. On the other hand a monotonic decision tree is often desirable to aid decision making in such situations as credit rating and student admission. This paper proposes a novel approach called MDT to monotonic decision tree induction. Experiments show that generally this new approach produces decision trees that are more succinct and more effective predictors of the original implicit ordering, apart from being monotonic.
URI: http://hdl.handle.net/10397/23267
ISBN: 0-7803-7952-7
ISSN: 1062-922X
DOI: 10.1109/ICSMC.2003.1244279
Appears in Collections:Conference Paper

Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page view(s)

31
Last Week
1
Last month
Checked on Nov 19, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.