| Summary: | Within the Machine Learning field, the emergence of ensembles, combinations of learning models, has been boosting the performance of several algorithms. Under this context, Artificial Neural Networks (ANNs) make a fruitful arena, once they are inherently stochastic. In this work, ensembles of ANNs are approached, being used several output combination methods and two heuristic ensemble construction strategies. These were applied to real world classification and regression tasks. The results reveal some improvements of ensembles over single ANNs, favoring the combination of ANNs with distinct complexity (topologies) and the weighted averaging of the outputs as the combination method. The proposed approach is also able to perform automatic model selection.
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