Biclustering-Driven Ensemble of Bayesian Belief Network Classifiers for Underdetermined Problems
نویسندگان
چکیده
In this paper, we present BENCH (Biclusteringdriven 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.
منابع مشابه
Pansombut, Tatdow. Advanced Learning Techniques for Improved Inference of Bayesian Belief Networks from Uncertain and High-dimensional Data. (under the Direction of Prof. Advanced Learning Techniques for Improved Inference of Bayesian Belief Networks from Uncertain and High-dimensional Data
PANSOMBUT, TATDOW. Advanced Learning Techniques for Improved Inference of Bayesian Belief Networks from Uncertain and High-dimensional Data. (Under the direction of Prof. Nagiza F. Samatova and Prof. Dennis R. Bahler.) A Bayesian Belief Network (BBN) is a powerful probabilistic learning model, it has been used successfully in many problem domains, such as medical diagnostics, computational biol...
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