Abstract
This paper presents a new algorithm to construct a neural network ensemble (NNE) based on heterogeneous component neural networks with negative correlation learning. The constructive algorithm consists of two parts: a sub-algorithm to construct best heterogeneous component neural networks with negative correlation learning dynamically (CBHNN), and a sub-algorithm to construct heterogeneous NNE with trained heterogeneous neural networks incrementally (CHNNE). The experiment results showe that HNNE is better than the traditional homological NNE method.
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References
Krogh, A., Vedelsby, J.: Neural network ensembles, cross validation, and active learning. Advance in Neural Information Processing Systems 7, 231–238 (1995)
Optic, D.W., Shavlik, J.W.: Generating Accurate and Diverse Members of a Neural Network Ensemble. Advance in Neural Information Processing System 8, 535–543 (1996)
Rosen, B.E.: Ensemble learning using decorrelated neural networks. Connection Science 8, 353–373 (1996)
Liu, Y., Yao, X., et al.: Evolutionary ensembles with negative correlation learning. IEEE Transactions on Evolutionary Computation 4, 295–304 (2000)
Zhou, Z.H., Wu, J.X., et al.: Ensemble neural networks: Many could be better than all. Artificial Intelligence 137, 239–263 (2002)
Bakker, B., Heskes, T.: Clustering ensembles of neural network model. Neural Networks 16, 261–269 (2003)
Fahlman, S.E., Lebiere, C.: The Cascade-Correlation Learning Architecture. Advances in Neural Information Processing Systems 2, 524–532 (1990)
Hoehfeld, M., Fahlman, S.E.: Learning with Limited Numerical Precision Using the Cascade-Correlation Learning Algorithm. IEEE Transactions on Neural Networks 3, 602–611 (1992)
Blake, C., Keogh, E., Merz, C.J.: UCI repository of machine learning database, Department of Information and Computer Science, University of California, Irvine, CA (1998)
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Fu, X., Wang, Z., Feng, B. (2006). A Constructive Algorithm for Training Heterogeneous Neural Network Ensemble. In: Wang, GY., Peters, J.F., Skowron, A., Yao, Y. (eds) Rough Sets and Knowledge Technology. RSKT 2006. Lecture Notes in Computer Science(), vol 4062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11795131_57
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DOI: https://doi.org/10.1007/11795131_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36297-5
Online ISBN: 978-3-540-36299-9
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