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It is common that a trained classification model is applied to the operatingdata that is deviated from the training data because of noise. This paperdemonstrates that an ensemble classifier, Diversified Multiple Tree (DMT), ismore robust in classifying noisy data than other widely used ensemble methods.DMT is tested on three real world biomedical data sets from differentlaboratories in comparison with four benchmark ensemble classifiers.Experimental results show that DMT is significantly more accurate than otherbenchmark ensemble classifiers on noisy test data. We also discuss a limitationof DMT and its possible variations.

Building Diversified Multiple Trees for Classification in High Dimensional Noisy Biomedical Data