The combination of the synthetic minority over-sampling technique (SMOTE) and the radial basis function (RBF) classifier is proposed to deal with classification for imbalancedtwo-classdata. In order to enhance the s...
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ISBN:
(纸本)9781424496365
The combination of the synthetic minority over-sampling technique (SMOTE) and the radial basis function (RBF) classifier is proposed to deal with classification for imbalancedtwo-classdata. In order to enhance the significance of the small and specific region belonging to the positive class in the decision region, the SMOTE is applied to generate synthetic instances for the positive class to balance the training data set. Based on the over-sampled training data, the RBF classifier is constructed by applying the orthogonal forward selection procedure, in which the classifier structure and the parameters of RBF kernels are determined using a particle swarm optimization algorithm based on the criterion of minimizing the leave-one-out misclassification rate. The experimental results on both simulated and real imbalanceddata sets are presented to demonstrate the effectiveness of our proposed algorithm.
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