Biclustering-Driven Ensemble of Bayesian Belief Network Classifiers for Underdetermined Problems
Tatdow Pansombut, William Hendrix, Zekai Jacob Gao, Brent E. Harrison, Nagiza F. Samatova
In this paper, we present BENCH (BiclusteringdrivenENsemble of Classifiers), an algorithm toconstruct an ensemble of classifiers through concurrentfeature and data point selection guided byunsupervised knowledge obtained from biclustering.BENCH is designed for underdeterminedproblems. In our experiments, we use Bayesian BeliefNetwork (BBN) classifiers as base classifiers inthe ensemble; however, BENCH can be applied toother classification models as well. We show thatBENCH is able to increase prediction accuracy ofa single classifier and traditional ensemble of classifiersby up to 15% on three microarray datasetsusing various weighting schemes for combining individualpredictions in the ensemble.