Training RBF neural networks on unbalanced data

This paper presents a new algorithm for the construction and training of an RBF neural network with unbalanced data. In applications, minority classes with much fewer samples are often present in data sets. The learning process of a neural network usually is biased towards classes with majority populations. Our study focused on improving the classification accuracy of minority classes while maintaining the overall classification performance.

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