[1]韩志艳,王健. 基于加权合成少数类过采样技术的故障诊断[J].计算机技术与发展,2016,26(09):43-46.
　HAN Zhi-yan,WANG Jian. Fault Diagnosis Method Based on Weighted Synthetic Minority Oversampling Technique[J].,2016,26(09):43-46.
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 基于加权合成少数类过采样技术的故障诊断()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
26
期数:
2016年09期
页码:
43-46
栏目:
应用开发研究
出版日期:
2016-09-10

文章信息/Info

Title:
 Fault Diagnosis Method Based on Weighted Synthetic Minority Oversampling Technique
文章编号:
1673-629X（2016)09-0043-04
作者:
 韩志艳; 王健
 渤海大学 工学院
Author(s):
 HAN Zhi-yan; WANG Jian
关键词:
 ; 故障诊断; 类别不平衡; SMOTE ; 过采样技术
Keywords:
 ; fault diagnosis; class imbalance; SMOTE; oversampling technique
分类号:
TP391.4
文献标志码:
A
摘要:
 合成少数类过采样技术( Synthetic Minority Oversampling Technique,SMOTE)是一种著名的过采样方法,但是它没有考虑样本的分布和潜在的噪声数据。为了改善SMOTE的性能,提出了加权合成少数类过采样技术( Weighted Synthetic Mi-nority Oversampling Technique,WSMOTE)。 WSMOTE通过引入邻域并将样本按照分布的不同划分为不同的组群,不同的群组拥有不同的采样价值,然后根据采样价值的不同加权合成样本。 WSMOTE在处理类别不平衡数据时具有优异的性能,并在半导体制造过程的监控数据仿真中得到了验证。
Abstract:
 The Synthetic Minority Oversampling Technique ( SMOTE) is a famous oversampling method,whereas it doesn’ t consider the distribution of samples and latent noises in the data. In order to improve the performance of SMOTE,a modified method,the Weighted Synthetic Minority Oversampling Technique ( WSMOTE) ,is proposed. WSMOTE introduces the neighborhood union to classify the sam-ples into several groups,and different groups have different importance. Then,WSMOTE generates synthetic sample according to the dif-ferent importance. The proposed method has a better performance when dealing with class imbalance data and it is demonstrated through its application to the semiconductor wafer fabrication process.

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更新日期/Last Update: 2016-10-25