Experiments on mining inconsistent data with bagging and the MLEM2 rule induction algorithm
by Clinton Eugene Cohagan; Jerzy Witold Grzymala-Busse; Zdzislaw S. Hippe
International Journal of Granular Computing, Rough Sets and Intelligent Systems (IJGCRSIS), Vol. 2, No. 3, 2012

Abstract: We report results of experiments on mining inconsistent data using bagging combined with the MLEM2 rule induction algorithm based on rough set theory. The main objective of this paper was to compare the quality of rule sets induced using two different approaches to inconsistency – lower and upper approximations – and three different approaches to ensemble voting – based on support, strength and majority – in the bagged MLEM2. Our main conclusion is that there is no significant difference in performance between one of the most successful techniques used in bagging, majority voting, and voting based on support (two-tailed Wilcoxon test, 5% level of significance).

Online publication date: Fri, 29-Aug-2014

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