Title: Experiments on mining inconsistent data with bagging and the MLEM2 rule induction algorithm
Authors: Clinton Eugene Cohagan; Jerzy Witold Grzymala-Busse; Zdzislaw S. Hippe
Kansas City Plant, National Nuclear Security Administration, Kansas City, MO 64141, USA.
Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USA; Institute of Computer Science, Polish Academy of Sciences, 01-237 Warsaw, Poland.
Department of Expert Systems and Artificial Intelligence, University of Information Technology and Management, 35-225 Rzeszow, Poland
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).
Keywords: data mining; inconsistent data; bagging; rule induction; rough set theory.
Int. J. of Granular Computing, Rough Sets and Intelligent Systems, 2012 Vol.2, No.3, pp.257 - 271
Date of acceptance: 24 Nov 2011
Available online: 24 May 2012