Ensemble of Diverse Artificial Neural Networks

Artificial neural network Classifier Combination of Classifiers Diversity Ensemble methods Generalization error Neural Network Ensembles Ensemble Data Mining Methods, also known as Committee Methods or Model Combiners, which provides the power of multiple classifiers to achieve better prediction accuracy than any of the individual classifier could on their own. The diversity among the members of ensemble is used to determining its generalization error. The empirical results reveal that the performance of an ensemble is related to the diversity among individual learners in the ensemble and more diversity might be used to obtain better performance. Artificial Neural networks(ANN) are very flexible with respect to incomplete, missing and noisy data and also makes the data to use for dynamic environment. ANN is dependent on how best is the configuration of the net in terms of number of weights, neurons and layers. Diversity in an ensemble of neural networks can be handled by manipulating either input data or output data.

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