Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning

Authors:
Hui Han;Wen-Yuan Wang;Bing-Huan Mao
Affiliations:
Department of Automation, Tsinghua University, Beijing, P. R. China;Department of Automation, Tsinghua University, Beijing, P. R. China;Department of Statistics, Central University of Finance and Economics, Beijing, P. R. China
Venue:
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Year:
2005

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Abstract

In recent years, mining with imbalanced data sets receives more and more attentions in both theoretical and practical aspects. This paper introduces the importance of imbalanced data sets and their broad application domains in data mining, and then summarizes the evaluation metrics and the existing methods to evaluate and solve the imbalance problem. Synthetic minority over-sampling technique (SMOTE) is one of the over-sampling methods addressing this problem. Based on SMOTE method, this paper presents two new minority over-sampling methods, borderline-SMOTE1 and borderline-SMOTE2, in which only the minority examples near the borderline are over-sampled. For the minority class, experiments show that our approaches achieve better TP rate and F-value than SMOTE and random over-sampling methods.