Safe sample screening based sampling method for imbalanced data

被引：0

作者：

Shi H. ^{[1
]}

Liu Y. ^{[1
]}

Ji S. ^{[1
]}

机构：

[1] College of Information, Shanxi University of Finance and Economics, Taiyuan

来源：

Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2019年 / 32卷 / 06期

基金：

中国国家自然科学基金;

关键词：

Imbalanced Data; Safe Sample Screening; Synthetic Minority Oversampling Technique(SMOTE); Undersampling; Imbalance Ratio;

D O I：

10.16451/j.cnki.issn1003-6059.201906007

中图分类号：

学科分类号：

摘要：

The loss of valuable information may be caused by undersampling, and the class overlapping between the majority class and the minority class may be aggravated by the synthetic minority oversampling technique(SMOTE). A sampling method, Screening_SMOTE, is proposed in this paper, combining safe sample screening based undersampling with SMOTE. Parts of non-informative instances and noise instances in the majority class are identified and discarded by the undersampling method using safe screening rules. Then, the minority class instances generated by SMOTE are added into the screened dataset. The loss of informative information is avoided and the noise instances in the majority class are discarded using safe sample screening based undersampling, relieving the class overlapping. The experimental results show that Screening_SMOTE is an effective method of rebalancing imbalanced datasets, especially for high dimensional imbalanced datasets. © 2019, Science Press. All right reserved.

引用

页码：545 / 556

页数：11

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