Abstract: In SMOTE(Synthetic Minority Over-sampling TEchnique),only minority class samples nearest to neighbors are computed when samples are synthesized,causing the problem that the density of the minority class samples remains unchanged after oversampling.This paper proposed an improved NKSMOTE(New Kernel Synthetic Minority Over-Sampling Technique) algorithm to overcome the shortage of SMOTE.Firstly,a nonlinear mapping function is used to map samples to a high-dimensional kernel space,and then the K nearest neighbors of samples of minority class from the whole samples are computed.In addition,different over-sampling rates are set on different minority samples to change the imbalanced multiplying power according to the influencecaused by the distribution of minority class samples on the classification performance of algorithm.In the experiments,some classical oversampling methods were compared with the proposed oversampling method,and Decision Tree(DT),error BackPropagation(BP) and Random Forest(RF) were chosen as base classifier.Experimental results on UCI data sets show better classification performance of NKSMOTE algorithm.

Key words: Classification, Imbalanced rate, Kernel space, Over-sampling, SMOTE algorithm