Selective neural network ensemble based on clustering

Authors:
Haixia Chen;Senmiao Yuan;Kai Jiang
Affiliations:
College of Computer Science and Technology, Jilin University, Changchun, China;College of Computer Science and Technology, Jilin University, Changchun, China;The 45th Research Institute of CETC, Beijing, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Year:
2006

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Abstract

To improve the generalization ability of neural network ensemble, a selective method based on clustering is proposed. The method follows the overproduce and choose paradigm. It first produces a large number of individual networks, and then clusters these networks according to their diversity. Networks with the highest classification accuracies in each cluster are selected for the final integration. Experiments on ten UCI data sets showed the superiority of the proposed algorithm to the other two similiar ensemble learning algorithms.