Published Conference Proceedings - Paper
Preceding Rule Induction with Instance Reduction Methods
Othman, O & Bryant, C 2013, Preceding Rule Induction with Instance Reduction Methods, in: Perner, P (ed.), 'Proceedings of the 9th International Conference on Machine Learning and Data Mining in Pattern Recognition.', Springer-Verlag, Berlin, Germany, pp.209-218. Conference details: MLDM 2013, 19-25 July 2013, New York, USA..
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.
Lecture Notes in Computer Science, Volume 7988
Bryant, C & Othman, eds. 2013, Proceedings of the 9th International Conference on Machine Learning and Data Mining in Pattern Recognition., Springer-Verlag, Berlin, Germany, pp.209-218.
MLDM 2013, 19-25 July 2013, New York, USA.