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Probability density function estimation based over-sampling for imbalanced two-class problems

Probability density function estimation based over-sampling for imbalanced two-class problems
Probability density function estimation based over-sampling for imbalanced two-class problems
A novel probability density function (PDF) estimation based over-sampling approach is proposed for two-class imbalanced classification problems. The Parzen-window kernel function is applied to estimate the PDF of the positive class, from which synthetic instances are generated as additional training data to re-balance the class distribution. Utilising the re-balanced over-sampled training data, a radial basis function (RBF) classifier is constructed by applying an orthogonal forward regression, in which the classifier's structure and the parameters of RBF kernels are determined using a particle swarm optimisation algorithm based on the criterion of minimising the leave-one-out misclassification rate. The effectiveness of the proposed approach is demonstrated by an empirical study on several imbalanced data sets.
Gao, Ming
954671c3-4167-48ad-a948-26fa5755cee3
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Harris, Chris J.
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 J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a

Gao, Ming, Hong, Xia, Chen, Sheng and Harris, Chris J. (2012) Probability density function estimation based over-sampling for imbalanced two-class problems. International Joint Conference on Neural Networks, Australia. 10 - 15 Jun 2012.

Record type: Conference or Workshop Item (Paper)

Abstract

A novel probability density function (PDF) estimation based over-sampling approach is proposed for two-class imbalanced classification problems. The Parzen-window kernel function is applied to estimate the PDF of the positive class, from which synthetic instances are generated as additional training data to re-balance the class distribution. Utilising the re-balanced over-sampled training data, a radial basis function (RBF) classifier is constructed by applying an orthogonal forward regression, in which the classifier's structure and the parameters of RBF kernels are determined using a particle swarm optimisation algorithm based on the criterion of minimising the leave-one-out misclassification rate. The effectiveness of the proposed approach is demonstrated by an empirical study on several imbalanced data sets.

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More information

Published date: 2012
Venue - Dates: International Joint Conference on Neural Networks, Australia, 2012-06-10 - 2012-06-15
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 338823
URI: http://eprints.soton.ac.uk/id/eprint/338823
PURE UUID: f1960391-a1ca-477b-ac06-a4da913e82b3

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Date deposited: 17 May 2012 15:11
Last modified: 09 Dec 2019 20:07

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