Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/40963
Title: Generalization of Classification Rules
Authors: Xie, Z.
Hsu, W. 
Lee, M.L. 
Issue Date: 2003
Source: Xie, Z.,Hsu, W.,Lee, M.L. (2003). Generalization of Classification Rules. Proceedings of the International Conference on Tools with Artificial Intelligence : 522-529. ScholarBank@NUS Repository.
Abstract: Traditional classification rules are in the form of production rules. Recent works in hybrid classification algorithms have proposed the generation of contextual rules, whereby the right-hand side of the production rule is replaced by a classifier, to achieve higher accuracy. In this work, we present a framework to further generalize classification rules such that the left-hand side of a production rule is expressed as a conjunction of classifiers, called space splitters. An intelligent divide-and-conquer approach is designed to construct such generalized classification rules. The construction algorithm, GCTree, is elegant, efficient and scalable. The resulting classifier is able to achieve high predictive accuracy that outperforms Naïve Bayes and C4.5. Experiments demonstrate that GCTree is compact and stable.
Source Title: Proceedings of the International Conference on Tools with Artificial Intelligence
URI: http://scholarbank.nus.edu.sg/handle/10635/40963
ISSN: 10636730
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

53
checked on Dec 9, 2017

Google ScholarTM

Check


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