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Jmax-pruning: a facility for the information theoretic pruning of modular classification rules

Stahl, F. and Bramer, M. (2012) Jmax-pruning: a facility for the information theoretic pruning of modular classification rules. Knowledge-Based Systems, 29. pp. 12-19. ISSN 0950-7051

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To link to this item DOI: 10.1016/j.knosys.2011.06.016

Abstract/Summary

The Prism family of algorithms induces modular classification rules in contrast to the Top Down Induction of Decision Trees (TDIDT) approach which induces classification rules in the intermediate form of a tree structure. Both approaches achieve a comparable classification accuracy. However in some cases Prism outperforms TDIDT. For both approaches pre-pruning facilities have been developed in order to prevent the induced classifiers from overfitting on noisy datasets, by cutting rule terms or whole rules or by truncating decision trees according to certain metrics. There have been many pre-pruning mechanisms developed for the TDIDT approach, but for the Prism family the only existing pre-pruning facility is J-pruning. J-pruning not only works on Prism algorithms but also on TDIDT. Although it has been shown that J-pruning produces good results, this work points out that J-pruning does not use its full potential. The original J-pruning facility is examined and the use of a new pre-pruning facility, called Jmax-pruning, is proposed and evaluated empirically. A possible pre-pruning facility for TDIDT based on Jmax-pruning is also discussed.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:30156
Uncontrolled Keywords:J-pruning; Jmax-pruning; Modular classification rule induction; Pre-pruning; Classification
Additional Information:Special issue: Artificial Intelligence 2010
Publisher:Elsevier

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