Journal
NEUROCOMPUTING
Volume 74, Issue 17, Pages 3456-3466Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2011.06.010
Keywords
Imbalanced classification; Synthetic minority over-sampling technique; Radial basis function classifier; Orthogonal forward selection; Particle swarm optimisation
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Funding
- UK EPSRC
- Engineering and Physical Sciences Research Council [EP/G026858/1] Funding Source: researchfish
- EPSRC [EP/G026858/1] Funding Source: UKRI
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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. (C) 2011 Elsevier B.V. All rights reserved.
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