基于安全样本筛选的不平衡数据抽样方法

doi:10.16451/j.cnki.issn1003-6059.201906007

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Abstract

The loss of valuable information may be caused by undersampling, and the class overlapping between the majority class and the minority class may be aggravated by the synthetic minority oversampling technique(SMOTE). A sampling method, Screening_SMOTE, is proposed in this paper, combining safe sample screening based undersampling with SMOTE. Parts of non-informative instances and noise instances in the majority class are identified and discarded by the undersampling method using safe screening rules. Then, the minority class instances generated by SMOTE are added into the screened dataset. The loss of informative information is avoided and the noise instances in the majority class are discarded using safe sample screening based undersampling, relieving the class overlapping. The experimental results show that Screening_SMOTE is an effective method of rebalancing imbalanced datasets, especially for high dimensional imbalanced datasets.

Key words： Imbalanced Data Safe Sample Screening Undersampling Imbalance Ratio Synthetic Minority Oversampling Technique(SMOTE)

Received: 29 January 2019

ZTFLH:

TP 391

About author:: (SHI Hongbo(Corresponding author), Ph.D., professor. Her research interests include machine learning and artificial intelligence.)(LIU Yanxin, master student. Her research interests include machine learning.)(JI Suqin, master, associate professor. Her research interests include machine learning and data mining.)

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	SHI Hongbo
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	JI Suqin

Cite this article:

SHI Hongbo,LIU Yanxin,JI Suqin. Safe Sample Screening Based Sampling Method for Imbalanced Data[J]. , 2019, 32(6): 545-556.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201906007 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2019/V32/I6/545

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