Abstract
In this paper, we present the improving capability of accuracy and the parallel efficiency of self-organizing neural groves (SONGs) for classification on a MIMD parallel computer. Self-generating neural networks (SGNNs) are originally proposed on adopting to classification or clustering by automatically constructing self-generating neural tree (SGNT) from given training data. The SONG is composed of plural SGNTs each of which is independently generated by shuffling the order of the given training data, and the output of the SONG is voted all outputs of the SGNTs. We allocate each of SGNTs to each of processors in the MIMD parallel computer. Experimental results show that the more the number of processors increases, the more the classification accuracy increases for all problems.
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References
Wen, W.X., Pang, V., Jennings, A.: Self-generating vs. self-organizing, what’s different? In: Simpson, P.K. (ed.) Neural Networks Theory, Technology, and Applications. IEEE Technology Update Series, pp. 210–214. IEEE Technical Activities Board, Piscataway (1996)
Kohonen, T.: Self-Organizing Maps. Springer, Berlin (1995)
Wen, W.X., Jennings, A., Liu, H.: Learning a neural tree. In: The International Joint Conference on Neural Networks, Beijing, China, vol. 2, pp. 751–756, November 3-6 (1992)
Inoue, H., Narihisa, H.: Efficiency of Self-Generating Neural Networks Applied to Pattern Recognition. Int. J. of Mathematical and Computer Modelling 38(11-13), 1225–1232 (2003)
Inoue, H., Narihisa, H.: Improving generalization ability of self-generating neural networks through ensemble averaging. In: Terano, T., Liu, H., Chen, A.L.P. (eds.) PAKDD 2000. LNCS (LNAI), vol. 1805, pp. 177–180. Springer, Heidelberg (2000)
Haykin, S.: Neural Networks: A comprehensive foundation, ch. 7, 2nd edn. Prentice-Hall, Upper Saddle River (1999)
Inoue, H.: Self-organizing neural grove: Efficient multiple classifier system with pruned self-generating neural trees. In: Proc. The 18th International Conference on Artificial Neural Networks, Part I, Prague, Czech Rep., pp. 613–622, September 3-6 (2008)
Breiman, L., Friedman, J., Olshen, R., Stone, C.: Classification and Regression Trees. Wadsworth, California (1984)
Breiman, L.: Bagging predictors. Machine Learning 24(2), 123–140 (1996)
Asuncion, A., Newman, D.J.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html
Stone, M.: Cross-validation: A review. Math. Operationsforsch. Statist., Ser. Statistics 9(1), 127–139 (1978)
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Inoue, H. (2010). Self-Organizing Neural Grove and Its Distributed Performance. In: Hsu, CH., Yang, L.T., Park, J.H., Yeo, SS. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2010. Lecture Notes in Computer Science, vol 6082. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13136-3_15
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DOI: https://doi.org/10.1007/978-3-642-13136-3_15
Publisher Name: Springer, Berlin, Heidelberg
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