Abstract
Several clustering algorithms have been considered to determine the centers and dispersions of the hidden layer neurons of Radial Basis Function Neural Networks (RBFNNs) when applied both to regression and classification tasks. Most of the proposed approaches use unsupervised clustering techniques. However, for data classification, by performing supervised clustering it is expected that the obtained clusters represent meaningful aspects of the dataset. We therefore compared the original versions of k-means, Neural-Gas (NG) and Adaptive Radius Immune Algorithm (ARIA) along with their variants that use labeled information. The first two had already supervised versions in the literature, and we extended ARIA toward a supervised version. Artificial and real-world datasets were considered in our experiments and the results showed that supervised clustering is better indicated in problems with unbalanced and overlapping classes, and also when the number of input features is high.
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References
Barra, T., Bezerra, G., de Castro, L., Von Zuben, F.: An Immunological Density-Preserving Approach to the Synthesis of RBF Neural Networks for Classification. In: IEEE International Joint Conference on Neural Networks, pp. 929–935 (2006)
Bezerra, G., Barra, T., De Castro, L., Von Zuben, F.: Adaptive Radius Immune Algorithm for Data Clustering. In: Jacob, C., Pilat, M.L., Bentley, P.J., Timmis, J.I. (eds.) ICARIS 2005. LNCS, vol. 3627, pp. 290–303. Springer, Heidelberg (2005)
Bruzzone, L., Prieto, D.: A technique for the selection of kernel-function parameters in RBF neural networks for classification of remote-sensing images. IEEE Transactions on Geoscience and Remote Sensing 37(2), 1179–1184 (1999)
Cevikalp, H., Larlus, D., Jurie, F.: A supervised clustering algorithm for the initialization of RBF neural network classifiers. In: 15th IEEE Signal Processing and Communications Applications, pp. 1–4 (2007)
Frank, A., Asuncion, A.: UCI machine learning repository (2010), http://archive.ics.uci.edu/ml
Gan, M., Peng, H., Dong, X.: A hybrid algorithm to optimize RBF network architecture and parameters for nonlinear time series modeling. Applied Mathematical Modelling (2011)
Guillén, A., Pomares, H., Rojas, I., González, J., Herrera, L., Rojas, F., Valenzuela, O.: Studying possibility in a clustering algorithm for RBFNN design for function approximation. Neural Computing and Applications 17(1), 75–89 (2008)
Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice-Hall (1999)
Lamirel, J., Mall, R., Cuxac, P., Safi, G.: Variations to incremental growing neural gas algorithm based on label maximization. In: IEEE International Joint Conference on Neural Networks (IJCNN), pp. 956–965 (2011)
Martinetz, T.M., Berkovich, S.G., Schulten, K.J.: “Neural-Gas” network for vector quantization and its application to time-series prediction. IEEE Transactions on Neural Networks 4(4), 558–569 (1993)
Okamoto, K., Ozawa, S., Abe, S.: A Fast Incremental Learning Algorithm of RBF Networks with Long-Term Memory. In: Proceedings of the International Joint Conference on Neural Networks, pp. 102–107 (2003)
Qian, Q., Chen, S., Cai, W.: Simultaneous clustering and classification over cluster structure representation. Pattern Recognition 45(6), 2227–2236 (2012)
Spiegelhalter, D., Best, N., Carlin, B., Van Der Linde, A.: Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society. Series B: Statistical Methodology 64(4), 583–616 (2002)
Veroneze, R., Gonçalves, A.R., Von Zuben, F.J.: A Multiobjective Analysis of Adaptive Clustering Algorithms for the Definition of RBF Neural Network Centers in Regression Problems. In: Yin, H., Costa, J.A.F., Barreto, G. (eds.) IDEAL 2012. LNCS, vol. 7435, pp. 127–134. Springer, Heidelberg (2012)
Wang, X., Syrmos, V.: Optimal cluster selection based on Fisher class separability measure. In: Proceedings of American Control Conference, pp. 1929–1934 (2005)
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Gonçalves, A.R., Veroneze, R., Madeiro, S., Azevedo, C.R.B., Von Zuben, F.J. (2012). The Influence of Supervised Clustering for RBFNN Centers Definition: A Comparative Study. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds) Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33266-1_19
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DOI: https://doi.org/10.1007/978-3-642-33266-1_19
Publisher Name: Springer, Berlin, Heidelberg
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