Preprocessing method based on sample resampling for imbalanced data of electronic circuits

被引：0
|
作者：
Li R. [1 ]
Xu A. [1 ]
Sun W. [1 ]
Wu Y. [1 ]
机构：
[1] Naval Aviation University, Yantai
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2020年 / 42卷 / 11期
关键词：
Classification; Electronic circui; Imbalanced data; Local density; Resample;
D O I：
10.3969/j.issn.1001-506X.2020.11.30
中图分类号：
学科分类号：
摘要：
In order to solve the deficiency of fault state data and imbalance of whole test data in airborne electronic circuit, a data preprocessing method based on sample resampling is proposed. Firstly, extreme learning machine is used to training the original data set to select the correct classified samples. Secondly, the synthetic minority oversampling technique (SMOTE) is used to oversampling and local density under-sampling respectively for the minority and majority of the correct classified samples. And the misclassified majority samples are deleted as interference factors. In this way, the data set can be equalized, and the data size can be controlled to prevent over-fitting, and the detection rate of fault samples can be improved. Compared with other data resampling methods, the test data processing results show that the proposed method has a good and stable overall effect, which has a certain application value for the fault diagnosis of electronic circuit. © 2020, Editorial Office of Systems Engineering and Electronics. All right reserved.
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页码：2654 / 2660
页数：6
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