关键词: 不平衡数据集, 分类, 结合少数过采样技术（SMOTE）, 混合核函数, 支持向量机

Abstract: 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.

Key words: imbalanced dataset, classification, Synthetic Minority Over-sampling Technique（SMOTE）, mixed kernel function, Support Vector Machine（SVM）