Machine Learning
Ensemble learning via negative correlation
Neural Networks
The Knowledge Engineering Review
Generate different neural networks by negative correlation learning
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Simultaneous training of negatively correlated neural networks inan ensemble
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A constructive algorithm for training cooperative neural network ensembles
IEEE Transactions on Neural Networks
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Neural network ensemble (NNE) has been shown to outperform single neural network (NN) in terms of generalization ability. The performance of NNE is therefore depends on well diversity among component NNs. Popular NNE methods, such as bagging and boosting, follow data sampling technique to achieve diversity. In such methods, NN is trained independently with a particular training set that is probabilistically created. Due to independent training strategy there is a lack of interaction among component NNs. To achieve training time interaction, negative correlation learning (NCL) has been proposed for simultaneous training. NCL demands direct communication among component NNs; which is not possible in bagging and boosting. In this study, first we modify the NCL from simultaneous to sequential style and then induce in bagging and boosting for interaction purpose. Empirical studies exhibited that sequential training time interaction increased diversity among component NNs and outperformed conventional methods in generalization ability.