%0 Journal Article %A LUO Kangyang %A WANG Guoqiang %T Research on Imbalanced Data Classification Based on L-SMOTE and SVM %D 2019 %R 10.3778/j.issn.1002-8331.1808-0410 %J Computer Engineering and Applications %P 55-62 %V 55 %N 17 %X In view of the low classification effectiveness of the imbalanced datasets, this paper gives an improved SMOTE(FTL-SMOTE) based on L-SMOTE and SVM with mixtures kernels. Firstly, the classification is carried on using SVM with mixtures kernel function. Secondly, this paper presents the three principles of noise samples recognition for identifying precisely the noise samples and deleting these samples, and the sampling to the minority class samples is wrongly and correctly classified that using the method of F-SMOTE and T-SMOTE algorithm. Looping the above process until the termination condition is satisfied. The extensive experiments are conducted to compare classic SMOTE and important relevantly algorithms on the UCI dataset, and the experimental results show that the method given in this paper has preferable classifying quality, and improved algorithm reduces the operating time compared with L-SMOTE. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.1808-0410