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PDFOS: PDF estimation based over-sampling for imbalanced two-class problems

Gao, M., Hong, X. ORCID:, Chen, S., Harris, C. J. and Khalaf, E. (2014) PDFOS: PDF estimation based over-sampling for imbalanced two-class problems. Neurocomputing, 138. pp. 248-259. ISSN 0925-2312

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To link to this item DOI: 10.1016/j.neucom.2014.02.006


This contribution proposes a novel probability density function (PDF) estimation based over-sampling (PDFOS) approach for two-class imbalanced classification problems. The classical Parzen-window kernel function is adopted to estimate the PDF of the positive class. Then according to the estimated PDF, synthetic instances are generated as the additional training data. The essential concept is to re-balance the class distribution of the original imbalanced data set under the principle that synthetic data sample follows the same statistical properties. Based on the over-sampled training data, the radial basis function (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 particle swarm optimisation algorithm based on the criterion of minimising the leave-one-out misclassification rate. The effectiveness of the proposed PDFOS approach is demonstrated by the empirical study on several imbalanced data sets.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:36567
Uncontrolled Keywords:Imbalanced classification, probability density function based over-sampling, radial basis function classifier, orthogonal forward selection, particle swarm optimisation

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