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On combination of SMOTE and particle swarm optimization based radial basis function classifier for imbalanced problems

On combination of SMOTE and particle swarm optimization based radial basis function classifier for imbalanced problems
On combination of SMOTE and particle swarm optimization based radial basis function classifier for imbalanced problems
The combination of the synthetic minority oversampling technique (SMOTE) and the radial basis function (RBF) classifier is proposed to deal with classification for imbalanced two-class data. 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 imbalanced data sets are presented to demonstrate the effectiveness of our proposed algorithm.
1146-1153
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) On combination of SMOTE and particle swarm optimization based radial basis function classifier for imbalanced problems. International Joint Conference on Neural Networks. 31 Jul - 05 Aug 2011. pp. 1146-1153 .

Record type: Conference or Workshop Item (Other)

Abstract

The combination of the synthetic minority oversampling technique (SMOTE) and the radial basis function (RBF) classifier is proposed to deal with classification for imbalanced two-class data. 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 imbalanced data sets are presented to demonstrate the effectiveness of our proposed algorithm.

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Published date: August 2011
Additional Information: Event Dates: July 31 - August 5, 2011
Venue - Dates: International Joint Conference on Neural Networks, 2011-07-31 - 2011-08-05
Organisations: Southampton Wireless Group

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Local EPrints ID: 272679
URI: http://eprints.soton.ac.uk/id/eprint/272679
PURE UUID: 689210ca-fc01-4132-bb9b-d8ab25453018

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Date deposited: 18 Aug 2011 08:26
Last modified: 30 Jul 2019 18:57

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