计算机工程与应用2019，Vol.55Issue(17)：55-62,220,9.DOI:10.3778/j.issn.1002-8331.1808-0410
L-SMOTE与SVM结合的不平衡数据集分类研究
Research on Imbalanced Data Classification Based on L-SMOTE and SVM
摘要
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 prefer-able classifying quality, and improved algorithm reduces the operating time compared with L-SMOTE.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Key words
imbalanced dataset/classification/Synthetic Minority Over-sampling Technique(SMOTE)/mixed kernel function/Support Vector Machine(SVM)分类
信息技术与安全科学引用本文复制引用
罗康洋,王国强..L-SMOTE与SVM结合的不平衡数据集分类研究[J].计算机工程与应用,2019,55(17):55-62,220,9.基金项目
国家自然科学基金面上项目(No.11471211) （No.11471211）
上海市自然科学基金(No.14ZR1418900) （No.14ZR1418900）
全国统计科学研究项目(No.2018LY16) （No.2018LY16）
上海工程技术大学研究生科研创新项目(No.18KY0325). （No.18KY0325）
