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.
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