Aiming to solve the poor performance of imbalanced data classification, an novel imbalanced data classification algorithm based boundary mixed sampling(BMS) is proposed. This method firstly introduces coefficient of variation is to find out the boundary and non-boundary samples and then deal with them in different ways. The minority samples in boundary are over sampled while the non-boundary majority ones are under sampled to achieve a basic balance of samples. Experimental results show that the proposed method achieves the better classification performance by 5% than other three popular methods in seven UCI datasets, thus this method can be widely used in imbalanced data processing and classification.