Abstract
In this study, a novel kernel clustering algorithm based selective neural network ensemble method, i.e. KCASNNE, is proposed. In this model, on the basis of different training subsets generated by bagging algorithm, the feature extraction technique, kernel principal component analysis (KPCA), is used to extract their data features to train individual networks. Then kernel clustering algorithm (KCA) is used to select the appropriate number of ensemble members from the available networks. Finally, the selected members are aggregated into a linear ensemble model with simple average. For illustration and testing purposes, the proposed ensemble model is applied for economic forecasting.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Lin, J., Zhu, B.Z.: Improved principal component analysis and neural network ensemble based economic forecasting. In: Huang, D.-S., Li, K., Irwin, G.W. (eds.) ICIC 2006. LNCS, vol. 4113, pp. 135–145. Springer, Heidelberg (2006)
Hansen, L.K., Salamon, P.: Neural network ensembles. IEEE Trans. Pattern Analysis and Machine Intelligence 12(10), 993–1001 (1990)
Yu, L.A., Wang, S.Y., Lai, K.K.: A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates. Computer &Operation Research 32, 2523–2541 (2005)
Zhou, Z.H., Wu, J.X., Tang, W.: Ensemble neural networks: many could be batter than all. Artificial Intelligence 137(1-2), 239–263 (2002)
Pabitra, M., Murthy, C.A., Pal, S.K.: Unsupervised feature selection using feature similarity. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(3), 301–312 (2002)
Yu, L.A., Wang, S.Y., Lai, K.K., Huang, W.: A bias-variance-complexity trade-off framework for complex system modeling. In: Gavrilova, M., Gervasi, O., Kumar, V., Tan, C.J.K., Taniar, D., Laganà , A., Mun, Y., Choo, H. (eds.) ICCSA 2006. LNCS, vol. 3980, pp. 518–527. Springer, Heidelberg (2006)
Zhu, B.Z.: Real estate investment environment evaluation based on kernel principal composition analysis. In: Proceedings of ICCREM 2006, Orlando, Florida, USA, October 5-6, 2006, pp. 355–357 (2006)
Ji, Q.Y., Lin, J.: Clustering algorithm based on kernel method and its application. Journal of Beijing University of Aeronautics and Astronautics 32(6), 747–750 (2006)
Xiao, J.H.: Intelligent forecasting for the regional economic. Mathematics in Economics 22(1), 57–63 (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lin, J., Zhu, B. (2007). A Novel Kernel Clustering Algorithm Based Selective Neural Network Ensemble Model for Economic Forecasting. In: Kang, L., Liu, Y., Zeng, S. (eds) Advances in Computation and Intelligence. ISICA 2007. Lecture Notes in Computer Science, vol 4683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74581-5_34
Download citation
DOI: https://doi.org/10.1007/978-3-540-74581-5_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74580-8
Online ISBN: 978-3-540-74581-5
eBook Packages: Computer ScienceComputer Science (R0)