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

Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203 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 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.

Additional Information	Special issue: Artificial Intelligence 2010
Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/30156
Identification Number/DOI	10.1016/j.knosys.2011.06.016
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
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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