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Lithology Recognition by Neural Network Ensembles
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Evolving Committees of Support Vector Machines
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Applied Soft Computing
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ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
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Feature selection in heterogeneous structure of ensembles: a genetic algorithm approach
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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Expert Systems with Applications: An International Journal
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INES'10 Proceedings of the 14th international conference on Intelligent engineering systems
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Information Sciences: an International Journal
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Based on negative correlation learning and evolutionary learning, this paper presents evolutionary ensembles with negative correlation learning (EENCL) to address the issues of automatic determination of the number of individual neural networks (NNs) in an ensemble and the exploitation of the interaction between individual NN design and combination. The idea of EENCL is to encourage different individual NNs in the ensemble to learn different parts or aspects of the training data so that the ensemble can learn better the entire training data. The cooperation and specialization among different individual NNs are considered during the individual NN design. This provides an opportunity for different NNs to interact with each other and to specialize. Experiments on two real-world problems demonstrate that EENCL can produce NN ensembles with good generalization ability.