  In this paper, a novel approach to classifier ensemble creation is presented.
While other ensemble creation techniques are based on careful selection of
existing classifiers or preprocessing of the data, the presented approach
automatically creates an optimal labelling for a number of classifiers, which
are then assigned to the original data instances and fed to classifiers. The
approach has been evaluated on high-dimensional biomedical datasets. The
results show that the approach outperformed individual approaches in all cases.
