Original Contribution: Stacked generalization
Neural Networks
Machine Learning
Machine Learning
An empirical evaluation of bagging and boosting
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Using artificial neural network ensembles to extract data content from noisy data
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
A method for optimal division of data sets for use in neural networks
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
Accuracy of neural network classifiers as a property of the size of the data set
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
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If voting is used by an ensemble to classify data, some data points may not be classified, but a higher proportion of those which are classified are classified correctly. This trade off is affected by ensemble size and voting threshold. This paper investigates the effect of ensemble size on the proportions of decisions made and correct decisions. It does this for majority voting and consensus voting on ensembles of neural network classifiers constructed using bagging. It also models the relationships in order to estimate the asymptotic values as the ensemble size increases.