Abstract-A novel probability density function (PDF) estimation based over-sampling approach is proposed for twoclass 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 et al. REF propose probability density function estimation based on over-sampling approach for two classimbalanced classification problems.
Probability density function estimation based over-sampling for imbalanced two-class problems
The 2012 International Joint Conference on Neural Networks (IJCNN)
The 2012 International Joint Conference on Neural Networks (IJCNN)
Computer Science