Abstract: The fixed similarity measurement makes learner difficult to reveal the inherent statistical rules of the data itself with the priori information,and it is difficult to get good effect for the data set with a staggered classification.In order to improve the classification accuracy of the data set with a staggered classification,this paper combined the boundary and sample diffusion method.The method applies the statistical sample label information and position information to obtain boundary point,which is treated as the center.Then we selected appropriate control function to spread neighbo-ring sample points to make the classification more clear,so as to enhance the learning accuracy.Different classifiers are used to validate the method,and the accuracy of the proposed method is improved in different degrees.Compared with three classical supervised distance metric learning method,the experimental results show that this method is suitable for processing high degree of interleaving data sets,and can effectively improve the performance of SVM.

Key words: Distance metric learning,Sample point dispersion,Data preprocessing