Improving the Performance of the RBF Neural Networks Trained with Imbalanced Samples
Recently, the class imbalance problem in neural networks, is receiving growing attention in works of machine learning and data mining. This problem appears when the samples of some classes are much smaller than those in the other classes. The classes with small size can be ignored in the learning process and the convergence of these classes is very slow. This paper studies empirically the class imbalance problem in the context of the RBF neural network trained with backpropagation algorithm. We propose to introduce a cost function in the training process to compensate imbalance class and one strategy to reduce the impact of the cost function in the data probability distribution.
KeywordsCost Function Mean Square Error Minority Class Class Imbalance Imbalance Problem
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- 4.Bruzzone, L., Serpico, S.: Training of neural networks for classification of imbalanced remote-sensing data. In: IEEE Transactions on Geocience and Remote Sensing, 1202–1204 (1997)Google Scholar
- 6.Kukar, M., Kononenko, I.: Cost-sensitive learning with neural networks. In: 13th European Conference on Artificial Intelligence, pp. 445–449 (1998)Google Scholar
- 8.Serpico, S., Roli, F., Pellegretti, P., Vemazza, G.: Structured neural networks for the classification of multisensor remote-sensing images. In: Int. Geosci. Remote Sensing Symp., pp. 907–909 (1993)Google Scholar