Abstract
Neural network ensemble (NNE) focuses on two aspects: how to generate component NNs and how to ensemble. The two interplayed aspects impact greatly on performance of NNE. Unfortunately, the two aspects were investigated separately in almost previous works. An integrated neural network ensemble (InNNE) is proposed in the paper, which was an integrated ensemble algorithm not only for dynamically adjusting weights of an ensemble, but also for generating component NNs based on clustering technology. InNNE classifies the training set into different subsets with clustering technology, which are used to train different component NNs. The weights of an ensemble are adjusted by the correlation of input data and the center of different training subsets. InNNE can increase the diversity of component NNs and decreases generalization error of ensemble. The paper provided both analytical and experimental evidence that support the novel algorithm.
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Liu, B., Hu, C. (2006). Integrated Neural Network Ensemble Algorithm Based on Clustering Technology. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893028_80
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DOI: https://doi.org/10.1007/11893028_80
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46479-2
Online ISBN: 978-3-540-46480-8
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