  Ensemble methods have been widely used for improving the results of the best
single classificationmodel. A large body of works have achieved better
performance mainly by applying one specific ensemble method. However, very few
works have explored complex fusion schemes using het-erogeneous ensembles with
new aggregation strategies. This paper is three-fold: 1) It provides an
overview of the most popular ensemble methods, 2) analyzes several fusion
schemes using MNIST as guiding thread and 3) introduces MNIST-NET10, a complex
heterogeneous fusion architecture based on a degree of certainty aggregation
approach; it combines two heterogeneous schemes from the perspective of data,
model and fusion strategy. MNIST-NET10 reaches a new record in MNISTwith only
10 misclassified images. Our analysis shows that such complex heterogeneous
fusionarchitectures based on the degree of certainty can be considered as a way
of taking benefit fromdiversity.
