Preceding rule induction with instance reduction methods

Othman, Osama and Bryant, CH ORCID: https://orcid.org/0000-0002-9002-8343 2013, 'Preceding rule induction with instance reduction methods' , in: Proceedings of the 9th International Conference on Machine Learning and Data Mining in Pattern Recognition. , Lecture Notes in Computer Science (7988) , Springer, Berlin, pp. 209-218.

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Abstract

A new prepruning technique for rule induction is presented which applies instance reduction before rule induction. An empirical evaluation records the predictive accuracy and size of rule-sets generated from 24 datasets from the UCI Machine Learning Repository. Three instance reduction algorithms (Edited Nearest Neighbour, AllKnn and DROP5) are compared. Each one is used to reduce the size of the training set, prior to inducing a set of rules using Clark and Boswell's modification of CN2. A hybrid instance reduction algorithm (comprised of AllKnn and DROP5) is also tested. For most of the datasets, pruning the training set using ENN, AllKnn or the hybrid significantly reduces the number of rules generated by CN2, without adversely affecting the predictive performance. The hybrid achieves the highest average predictive accuracy.

Item Type:	Book Section
Editors:	Perner, P
Additional Information:	MLDM 2013 was held between 19-25 July 2013 in New York, USA.
Uncontrolled Keywords:	Rule Induction, Overfitting, Noise Filtering, Instance Reduction
Themes:	Subjects outside of the University Themes
Schools:	Schools > School of Computing, Science and Engineering Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Publisher:	Springer
Refereed:	Yes
Series Name:	Lecture Notes in Computer Science
ISBN:	9783642397110
Related URLs:	Author Publisher Publication http://www.mldm.de/
Funders:	Non funded research
Depositing User:	Dr Chris H. Bryant
Date Deposited:	08 Aug 2013 07:29
Last Modified:	16 Feb 2019 19:22
URI:	http://usir.salford.ac.uk/id/eprint/29340

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