A comparison of three voting methods for bagging with the MLEM2 algorithm

Authors:
Clinton Cohagan;Jerzy W. Grzymala-Busse;Zdzislaw S. Hippe
Affiliations:
Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS and Kansas City Plant, National Nuclear Security Administration, Kansas City, MO;Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS and Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland;Department of Expert Systems and Artificial Intelligence, University of Information Technology and Management, Rzeszow, Poland
Venue:
IDEAL'10 Proceedings of the 11th international conference on Intelligent data engineering and automated learning
Year:
2010

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Abstract

This paper presents results of experiments on some data sets using bagging on the MLEM2 rule induction algorithm. Three different methods of ensemble voting, based on support (a non-democratic voting in which ensembles vote with their strengths), strength only (an ensemble with the largest strength decides to which concept a case belongs) and democratic voting (each ensemble has at most one vote) were used. Our conclusions are that though in most cases democratic voting was the best, it is not significantly better than voting based on support. The strength voting was the worst voting method.