An approach based on kernel SMOTE (Synthetic Minority Over-sampling Technique) to solve classification on imbalance data set by Support Vector Machine (SVM) is presented.The method first oversamples the minority class in feature space by kernel SMOTE algorithm

then the pre-images of the synthetic instances are found based on a distance relation between feature space and input space.Finally

these pre-images are appended to the original data set to train a SVM.Experiments on real data sets indicate that compared with SMOTE approach

the samples constructed by the kernel SMOTE algorithm have the higher quality.As a result

the effectiveness of classification by SVM on imbalance data set is improved.