Machine Learning
Neural network design
Ensemble learning via negative correlation
Neural Networks
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Ensembling neural networks: many could be better than all
Artificial Intelligence
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Practical Genetic Algorithms with CD-ROM
Practical Genetic Algorithms with CD-ROM
Evolutionary ensembles with negative correlation learning
IEEE Transactions on Evolutionary Computation
Learning Ensembles of Neural Networks by Means of a Bayesian Artificial Immune System
IEEE Transactions on Neural Networks
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Ensemble Methods (EMs) are sets of models that combine their decisions, or their learning algorithms, or different data to obtain good predictions. The motivations are the possibility of improving the generalization capability and the overall system performance. However, several issues are at stake in EM development, such as the design of models that disagree as much as possible on the same data, the selection of some of them and their optimal combination to enhance the robustness of the ensemble. Since there is no unified procedure to implement these steps, this paper proposes a new methodology to design Neural Network (NN) ensembles using a Genetic Algorithm (GA). Firstly, a set of NNs with high degree of diversity is produced. The aim is to draw a different training data set for each NN by applying bootstrap. The architecture of the NN is selected by varying the number of hidden neurons, activation functions and initialization of weights. Secondly, a GA is employed to select both the best subset of NNs and the optimal combination strategy for ensuring the accuracy and the robustness of the ensemble. Experiments on well-known data sets are reported to evaluate the effectiveness of the proposed methodology.