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

A combined SMOTE and PSO based RBF classifier for two-class imbalanced problems
A combined SMOTE and PSO based RBF classifier for two-class imbalanced problems
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.
0925-2312
3456-3466
Gao, Ming
954671c3-4167-48ad-a948-26fa5755cee3
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Harris, Chris
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Gao, Ming
954671c3-4167-48ad-a948-26fa5755cee3
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Harris, Chris
c4fd3763-7b3f-4db1-9ca3-5501080f797a

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.

Record type: Article

Abstract

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.

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Published date: September 2011
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 272803
URI: http://eprints.soton.ac.uk/id/eprint/272803
ISSN: 0925-2312
PURE UUID: 42d964dd-e8d6-4874-9320-3961ebf21608

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Date deposited: 19 Sep 2011 08:21
Last modified: 16 Dec 2019 20:40

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