Abstract
Radial basis function (RBF) neural networks represent established machine learning tool with various interesting applications to nonlinear regression modeling. However, their performance may be substantially influenced by outlying measurements (outliers). Promising modifications of RBF network training have been available for the classification of data contaminated by outliers, but there remains a gap of robust training of RBF networks in the regression context. A novel robust approach based on backward subsample selection (i.e. instance selection) is proposed and presented in this paper, which searches sequentially for the most reliable subset of observations and finally performs outlier deletion. The novel approach is investigated in numerical experiments and is also applied to robustify a multilayer perceptron. The results on data containing outliers reveal the improved performance compared to conventional approaches.
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Acknowledgements
We thank Barbora Peštová for technical assistance and six anonymous referees for valuable suggestions leading to improvements of the paper. The work is supported by the projects 19-05704S (J. Kalina) and 18-23827S (P. Vidnerová) of the Czech Science Foundation.
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Kalina, J., Vidnerová, P. (2019). Robust Training of Radial Basis Function Neural Networks. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2019. Lecture Notes in Computer Science(), vol 11508. Springer, Cham. https://doi.org/10.1007/978-3-030-20912-4_11
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