A New Over-Sampling Approach: Random-SMOTE for Learning from Imbalanced Data Sets

被引：0

作者：

Dong, Yanjie ^{[1
]}

Wang, Xuehua ^{[1
]}

机构：

[1] Dalian Univ Technol, Inst Informat & Decision Making Technol, Dalian, Peoples R China

来源：

KNOWLEDGE SCIENCE, ENGINEERING AND MANAGEMENT | 2011年 / 7091卷

关键词：

Imbalanced Data sets; Over-sampling Approach; Random-SMOTE;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

For imbalanced data sets, examples of minority class are sparsely distributed in sample space compared with the overwhelming amount of majority class. This presents a great challenge for learning from the minority class. Enlightened by SMOTE, a new over-sampling method, Random-SMOTE, which generates examples randomly in the sample space of minority class is proposed. According to the experiments on real data sets, Random-SMOTE is more effective compared with other random sampling approaches.

引用

页码：343 / 352

页数：10

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