Data mining: concepts and techniques
Data mining: concepts and techniques
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Fast learning in networks of locally-tuned processing units
Neural Computation
A constructive algorithm for training cooperative neural network ensembles
IEEE Transactions on Neural Networks
Activities Prediction of Drug Molecules by Using the Optimal Ensemble Based on Uniform Design
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues
Estimation of the future earthquake situation by using neural networks ensemble
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
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Neural networks ensemble is a hot topic in machine learning community, which can significantly improve the generalization ability of single neural networks. However, the design of ensemble architecture still relies on either a tedious trial-and-error process or the experts' experience. This paper proposes a novel method called CERNN (Constructive Ensemble of RBF Neural Networks), in which the number of individuals, the number of hidden nodes and training epoch of each individual are determined automatically. The generalization performance of CERNN can be improved by using different training subsets and individuals with different architectures. Experiments on UCI datasets demonstrate that CERNN is effective to release the user from the tedious trial-and-error process, so is it when applied to earthquake prediction.