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A combined SMOTE and PSO based RBF classifier for two-class imbalanced problems

Gao, M., Hong, X., Chen, S. and Harris, C. J. (2011) A combined SMOTE and PSO based RBF classifier for two-class imbalanced problems. Neurocomputing, 74 (17). pp. 3456-3466. ISSN 0925-2312

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To link to this item DOI: 10.1016/j.neucom.2011.06.010

Abstract/Summary

This contribution proposes a powerful technique for two-class imbalanced classification problems by combining the synthetic minority over-sampling technique (SMOTE) and the particle swarm optimisation (PSO) aided radial basis function (RBF) classifier. 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's structure and the parameters of RBF kernels are determined using a PSO algorithm based on the criterion of minimising the leave-one-out misclassification rate. The experimental results obtained on a simulated imbalanced data set and three real imbalanced data sets are presented to demonstrate the effectiveness of our proposed algorithm.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:21113
Uncontrolled Keywords:imbalanced classification, synthetic minority over-sampling technique, radial basis function classifier, orthogonal forward selection, particle swarm optimisation
Publisher:Elsevier

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