Abstract
Artificial neural networks (ANNs) have been successfully applied to many areas due to its powerful ability both for classification and regression problems. For some difficult problems, ANN ensemble classifiers are considered, instead of a single ANN classifier. In the previous study, the authors presented the systematic trajectory search algorithm (STSA) to train the ANN. The STSA utilizes the orthogonal array (OA) to uniformly generate the initial population to globally explore the solution space, and then applies a novel trajectory search method to exploit the promising areas thoroughly. In this paper, an evolutionary constructing algorithm, called the ESTSA, of the ANN ensemble is proposed. Based on the STSA, the authors introduce a penalty term to the error function in order to guarantee the diversity of ensemble members. The performance of the proposed algorithm is evaluated by applying it to train a class of feedforward neural networks to solve the large n-bit parity problems. By comparing with the previous studies, the experimental results revealed that the neural network ensemble classifiers trained by the ESTSA have very good classification ability.
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References
Yao, X.: Evolving Artificial Neural Networks. Proc. IEEE 87(9), 1423–1447 (1999)
Mendes, R., Cortez, P., Rocha, M., Neves, J.: Particle Swarms for Feedforward Neural Network Training. In: Proceedings of the International Joint Conference on Neural Networks, pp. 1895–1899. IEEE Press, New York (2002)
Nikolaev, N., Iba, H.: Learning Polynomial Feedforward Neural Networks by Genetic Programming and Backpropagation. IEEE Trans. Neural Netw. 14(2), 337–350 (2003)
Tsai, J.T., Chou, J.H., Liu, T.K.: Tuning the Structure and Parameters of a Neural Network by Using Hybrid Taguchi-Genetic Algorithm. IEEE Trans. Neural Netw. 17(1), 69–80 (2006)
Tseng, L.Y., Chen, W.C.: A Two-Phase Genetic Local Search Algorithm for Feedforward Neural Network Training. In: Proceedings of the International Joint Conference on Neural Networks, pp. 5221–5225. IEEE Press, New York (2006)
Sharkey, A.J.C.: On Combining Artificial Neural Nets. Connect. Sci. 8(3/4), 299–314 (1996)
Hansen, L.K., Salamon, P.: Neural Network Ensembles. IEEE Trans. Pattern Anal. Mach. Intell. 12(10), 993–1001 (1990)
Brown, G., Wyatt, J., Harris, R., Yao, X.: Diversity Creation Methods: A Survey and Categorisation. Journal of Information Fusion 6, 5–20 (2005)
Liu, Y., Yao, X.: Ensemble Learning via Negative Correlation. Neural Networks 12, 1399–1404 (1999)
Liu, Y., Yao, X., Higuchi, T.: Evolutionary Ensembles with Negative Correlation Learning. IEEE Trans. on Evol. Comput. 4(4), 380–387 (2000)
Yao, X., Islam, M.M.: Evolving Artificial Neural Network Ensembles. IEEE Computational Intelligence Magazine 3(1), 31–42 (2008)
Stork, D.G., Allen, J.D.: How to Solve the N-bit Parity Problem with Two Hidden Units. Neural Networks 5(6), 923–926 (1992)
Hohil, M.E., Liu, D., Smith, S.H.: Solving the N-bit Parity Problem Using Neural Networks. Neural Networks 12(9), 1321–1323 (1999)
Tseng, L.Y., Chen, W.C.: The Systematic Trajectory Search Algorithm for Feedforward Neural Network Training. In: Proceedings of International Joint Conference on Neural Networks, pp. 1174–1179. IEEE Press, New York (2007)
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Tseng, LY., Chen, WC. (2010). Solving Large N-Bit Parity Problems with the Evolutionary ANN Ensemble. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13278-0_50
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DOI: https://doi.org/10.1007/978-3-642-13278-0_50
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