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