¹University of Science and Technology Beijing / School of Computer and Communication Engineering Department, Beijing, P.R.China
²Chinese Academy of Governance / E-Government Research Center, Beijing, P.R.China
³Wenzhou University / College of Physics & Electronic Information Engineering, Zhejiang, P.R.China
⁴College of Information Technology, Beijing Union University, Beijing, 100101,China

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Abstract—A new method was proposed for leaning from the imbalanced dataset based the samples distribution density in this paper. In the proposed scheme, a model of samples distribution density was designed, followed by the improved smote progress SDD-SMOTE where we smoted the minority samples according to the samples distribution density. Cross-validation results show that proposed SDD-SMOTE method to some extent improves the minority prediction in both the recall and the precision metrics.

Index Terms—imbalanced dataset; knowledge discovery; over sample; distribution density

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Cite: Kuoyi Shao, Yun Zhai, Haifeng Sui, Changsheng Zhang, Nan Ma, "A New Over-sample Method Based on Distribution Density," Journal of Computers vol. 9, no. 2, pp. 483-490, 2014.