Improving the Classification Accuracy of RBF and MLP Neural Networks Trained with Imbalanced Samples
In practice, numerous applications exist where the data are imbalanced. It supposes a damage in the performance of the classifier. In this paper, an appropriate metric for imbalanced data is applied as a filtering technique in the context of Nearest Neighbor rule, to improve the classification accuracy in RBF and MLP neural networks. We diminish atypical or noisy patterns of the majority-class keeping all samples of the minority-class. Several experiments with these preprocessing techniques are performed in the context of RBF and MLP neural networks.
KeywordsRadial Basis Function Near Neighbor Radial Basis Function Neural Network Weighted Distance Minority Class
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- 2.Barandela, R., Cortés, N., Palacios, A.: The nearest neighbour rule and the reduction of the training sample size. In: 9th. Spanish Symposium on Pattern Recognition and Image Analysis, Benicassim, Spain, vol. 1, pp. 103–108 (2001)Google Scholar
- 6.Ding, S.Q., Xiang, C.: From multilayer perceptrons to radial basis function networks: a comparative study. In: IEEE. Conference on Cybernetics and Intelligent Systems, Singapore, December 1-3, vol. 1, pp. 69–74 (2004)Google Scholar
- 7.Fu, X., Wang, L., Chua, K.S., Chu, F.: Training RBF neural networks on unbalanced data. In: IX International Conference on Neural Information Processing (ICONIP 2002), Singapore, pp. 1016–1020 (2002)Google Scholar
- 8.Haykin, S.: Neuronal Networks - a comprehensive foundation, 2nd edn., pp. 278–282. Prentice-Hall, Englewood Cliffs (1999)Google Scholar
- 9.Hutchinson, J.M., Lo, A., Poggio, T.: A Nonparametric Approach to Pricing and Hedging Derivates Securities Via Learning Networks. Technical Report, Artificial Intelligence Laboratory and Center for Biological and Computational Learning, MIT, memo 1471, no. 92 (1994)Google Scholar
- 10.Kubat, M., Matwin, S.: Addressing the curse of imbalanced training set: one-sided selection. In: 14th International Conference on Machine Learning, Nashville, USA, pp. 179–186 (1997)Google Scholar
- 11.Lu, Y., Guo, H., Feldkamp, L.: Robust neural learning from unbalanced data examples. In: IEEE International Joint Conference on Neural Networks, pp. 1816–1821 (1998)Google Scholar
- 12.Pao, Y.H.: Adaptive Patter Recognition and Neuronal Networks. Addison-Wesley, Reading (1989)Google Scholar