A new over-sampling approach: Random-SMOTE for learning from imbalanced data sets

Authors:
Yanjie Dong;Xuehua Wang
Affiliations:
Institution of Information and Decision-making Technology, Dalian University of Technology, Dalian, China;Institution of Information and Decision-making Technology, Dalian University of Technology, Dalian, China
Venue:
KSEM'11 Proceedings of the 5th international conference on Knowledge Science, Engineering and Management
Year:
2011

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Abstract

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.