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Ensemble methods have been widely used for improving the results of the bestsingle classificationmodel. A large body of works have achieved betterperformance mainly by applying one specific ensemble method. However, very fewworks have explored complex fusion schemes using het-erogeneous ensembles withnew aggregation strategies. This paper is three-fold: 1) It provides anoverview of the most popular ensemble methods, 2) analyzes several fusionschemes using MNIST as guiding thread and 3) introduces MNIST-NET10, a complexheterogeneous fusion architecture based on a degree of certainty aggregationapproach; it combines two heterogeneous schemes from the perspective of data,model and fusion strategy. MNIST-NET10 reaches a new record in MNISTwith only10 misclassified images. Our analysis shows that such complex heterogeneousfusionarchitectures based on the degree of certainty can be considered as a wayof taking benefit fromdiversity.

MNIST-NET10: A heterogeneous deep networks fusion based on the degree of certainty to reach 0.1 error rate. Ensembles overview and proposal