基于样本重采样的电路非平衡数据预处理方法

TP206.1; 针对机载设备电子电路故障状态测试数据少、整体测试数据不均衡的问题,提出了一种基于样本重采样的数据预处理方法.首先,采用超限学习机对原始数据集进行训练以挑选出分类准确的样本.然后,对其中的少数类和多数类分别采用合成少数类过采样技术(synthetic minority oversampling technique,SMOTE)进行过采样和局部密度欠采样处理;并将错误分类的多数类样本作为干扰因素进行删除.通过以上两种手段可以均衡数据集,并控制数据规模防止过拟合,提高对故障样本的检测率.实测数据处理结果表明,相比于其他重采样算法,所提算法整体效果优良且稳定,对电子电路故障诊断具有一...

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Published in系统工程与电子技术 Vol. 42; no. 11; pp. 2654 - 2660
Main Authors 李睿峰, 许爱强, 孙伟超, 吴阳勇
Format Journal Article
LanguageChinese
Published 海军航空大学,山东烟台264001 01.11.2020
Subjects
电子电路
重采样
局部密度
非平衡数据
分类
Online AccessGet full text
ISSN1001-506X
DOI10.3969/j.issn.1001-506X.2020.11.30

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Abstract TP206.1; 针对机载设备电子电路故障状态测试数据少、整体测试数据不均衡的问题,提出了一种基于样本重采样的数据预处理方法.首先,采用超限学习机对原始数据集进行训练以挑选出分类准确的样本.然后,对其中的少数类和多数类分别采用合成少数类过采样技术(synthetic minority oversampling technique,SMOTE)进行过采样和局部密度欠采样处理;并将错误分类的多数类样本作为干扰因素进行删除.通过以上两种手段可以均衡数据集,并控制数据规模防止过拟合,提高对故障样本的检测率.实测数据处理结果表明,相比于其他重采样算法,所提算法整体效果优良且稳定,对电子电路故障诊断具有一定的应用价值.
AbstractList TP206.1; 针对机载设备电子电路故障状态测试数据少、整体测试数据不均衡的问题,提出了一种基于样本重采样的数据预处理方法.首先,采用超限学习机对原始数据集进行训练以挑选出分类准确的样本.然后,对其中的少数类和多数类分别采用合成少数类过采样技术(synthetic minority oversampling technique,SMOTE)进行过采样和局部密度欠采样处理;并将错误分类的多数类样本作为干扰因素进行删除.通过以上两种手段可以均衡数据集,并控制数据规模防止过拟合,提高对故障样本的检测率.实测数据处理结果表明,相比于其他重采样算法,所提算法整体效果优良且稳定,对电子电路故障诊断具有一定的应用价值.
Author 许爱强
李睿峰
吴阳勇
孙伟超
AuthorAffiliation 海军航空大学,山东烟台264001
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LI Ruifeng
XU Aiqiang
SUN Weichao
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Keywords 电子电路
重采样
局部密度
非平衡数据
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