A combined SMOTE and PSO based RBF classifier for two-class imbalanced problems
Gao, Ming, Hong, Xia, Chen, Sheng and Harris, Chris (2011) A combined SMOTE and PSO based RBF classifier for two-class imbalanced problems. Neurocomputing, 74, (17), 3456-3466.
- Published Version
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 dataset and three real imbalanced datasets are presented to demonstrate the effectiveness of our proposed algorithm.
|Divisions:||Faculty of Physical and Applied Science > Electronics and Computer Science > Comms, Signal Processing & Control
|Date Deposited:||19 Sep 2011 08:21|
|Last Modified:||25 Aug 2012 02:50|
|Contributors:||Gao, Ming (Author)
Hong, Xia (Author)
Chen, Sheng (Author)
Harris, Chris (Author)
|Further Information:||Google Scholar|
|ISI Citation Count:||2|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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