Systems Engineering and Electronics ›› 2020, Vol. 42 ›› Issue (11): 2654-2660.doi: 10.3969/j.issn.1001-506X.2020.11.30

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

Ruifeng LI(), Aiqiang XU(), Weichao SUN(), Yangyong WU()   

  1. Naval Aviation University, Yantai 264001, China
  • Received:2020-02-07 Online:2020-11-01 Published:2020-11-05
  • Supported by:
    “泰山学者”攀登计划资助课题

Abstract:

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.

Key words: electronic circui, imbalanced data, resample, local density, classification

CLC Number: 

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