  Multi-class ensemble classification remains a popular focus of investigation
within the research community. The popularization of cloud services has sped up
their adoption due to the ease of deploying large-scale machine-learning
models. It has also drawn the attention of the industrial sector because of its
ability to identify common problems in production. However, there are
challenges to conform an ensemble classifier, namely a proper selection and
effective training of the pool of classifiers, the definition of a proper
architecture for multi-class classification, and uncertainty quantification of
the ensemble classifier. The robustness and effectiveness of the ensemble
classifier lie in the selection of the pool of classifiers, as well as in the
learning process. Hence, the selection and the training procedure of the pool
of classifiers play a crucial role. An (ensemble) classifier learns to detect
the classes that were used during the supervised training. However, when
injecting data with unknown conditions, the trained classifier will intend to
predict the classes learned during the training. To this end, the uncertainty
of the individual and ensemble classifier could be used to assess the learning
capability. We present a novel approach for novel detection using ensemble
classification and evidence theory. A pool selection strategy is presented to
build a solid ensemble classifier. We present an architecture for multi-class
ensemble classification and an approach to quantify the uncertainty of the
individual classifiers and the ensemble classifier. We use uncertainty for the
anomaly detection approach. Finally, we use the benchmark Tennessee Eastman to
perform experiments to test the ensemble classifier's prediction and anomaly
detection capabilities.
