Clustering boundary over-sampling classification method for imbalanced data sets

被引：2
作者：
Lou, Xiao-Jun [1 ]
Sun, Yu-Xuan [1 ]
Liu, Hai-Tao [1 ,2 ]
机构：
[1] Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences
[2] Wuxi SensingNet Industrialization Research Institute
来源：
Liu, H.-T. (liuhaitao@wsn.cn) | 1600年 / Zhejiang University卷 / 47期
关键词：
Clustering boundary; Imbalanced data sets; K-nearest density; Over-sampling; Synthetic samples;
D O I：
10.3785/j.issn.1008-973X.2013.06.003
中图分类号：
学科分类号：
摘要：
The synthetic minority over-sampling technique (SMOTE) is a widely used method for imbalanced data classification. However, SMOTE synthesizes new samples without any guidance, which may lead to noise-sensitive and over-fitting. To resolve this problem, a novel over-sampling classification method for imbalanced data sets, called cluster boundary-synthetic minority over-sampling technique (CB-SMOTE), was proposed. Clustering consistency index was introduced to find the boundary minority samples. Then, k-nearest density was defined to calculate the number of synthetic new samples and to reject the noise samples, and it modified the rule of new samples synthesis. It is an over-sampling method with guidance, and the new samples generated by this method are much more beneficial for classifier learning. Six classification methods were compared using University of California Irvine (UCI) data sets. Experimental results show that the proposed method outperforms other methods in both minority samples and majority samples, and it is more stable in different over-sampling rates.
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收藏
页码：944 / 950
页数：6
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