Biclustering-driven ensemble of Bayesian belief network classifiers for underdetermined problems

Authors:
Tatdow Pansombut;William Hendrix;Zekai J. Gao;Brent E. Harrison;Nagiza F. Samatova
Affiliations:
Department of Computer Science, North Carolina State University, Raleigh, North Carolina and Oak Ridge National Laboratory, Oak Ridge, TN;Department of Computer Science, North Carolina State University, Raleigh, North Carolina and Oak Ridge National Laboratory, Oak Ridge, TN;Department of Computer Science, Zhejiang University, Hangzhou, P.R. China;Department of Computer Science, North Carolina State University, Raleigh, North Carolina and Oak Ridge National Laboratory, Oak Ridge, TN;Department of Computer Science, North Carolina State University, Raleigh, North Carolina and Oak Ridge National Laboratory, Oak Ridge, TN
Venue:
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Year:
2011

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Abstract

In this paper, we present BENCH (Biclustering-driven ENsemble of Classifiers), an algorithm to construct an ensemble of classifiers through concurrent feature and data point selection guided by unsupervised knowledge obtained from biclustering. BENCH is designed for underdetermined problems. In our experiments, we use Bayesian Belief Network (BBN) classifiers as base classifiers in the ensemble; however, BENCH can be applied to other classification models as well. We show that BENCH is able to increase prediction accuracy of a single classifier and traditional ensemble of classifiers by up to 15% on three microarray datasets using various weighting schemes for combining individual predictions in the ensemble.