Probability density function estimation based over-sampling for imbalanced two-class problems
Ming Gao, Xia Hong, Sheng Chen, Chris J. Harris · 2012
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.