Pruning classification rules with instance reduction methods

Othman, O and Bryant, CH ORCID: https://orcid.org/0000-0002-9002-8343 2015, 'Pruning classification rules with instance reduction methods' , International Journal of Machine Learning and Computing, 5 (3) , pp. 187-191.

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Abstract

Generating classification rules from data often leads to large sets of rules that need to be pruned. A new pre-pruning technique for rule induction is presented which applies instance reduction before rule induction. Training three rule classifiers on datasets that have been reduced earlier with instance reduction methods leads to a statistically significant lower number of generated rules, without adversely affecting the predictive performance. The search strategies used by the three algorithms vary in terms of both type (depth-first or beam search) and direction (general-to-specific or specific-to-general).

Item Type:	Article
Themes:	Subjects outside of the University Themes
Schools:	Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Journal or Publication Title:	International Journal of Machine Learning and Computing
Publisher:	International Association of Computer Science and Information Technology Press (IACSIT)
Refereed:	Yes
ISSN:	2010-3700
Related URLs:	Author
Funders:	Non funded research
Depositing User:	Dr Chris H. Bryant
Date Deposited:	07 Jan 2015 11:20
Last Modified:	18 Feb 2019 18:59
URI:	http://usir.salford.ac.uk/id/eprint/33262

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