Self-training credit evaluation integrated classification  model based on data editing

LIU Wenjie; WANG Guoqiang

doi:10.12299/jsues.23-0054

Volume 38 Issue 1

Feb. 2024

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LIU Wenjie, WANG Guoqiang. Self-training credit evaluation integrated classification model based on data editing[J]. Journal of Shanghai University of Engineering Science, 2024, 38(1): 83-89. doi: 10.12299/jsues.23-0054

Citation:

LIU Wenjie, WANG Guoqiang. Self-training credit evaluation integrated classification model based on data editing[J]. Journal of Shanghai University of Engineering Science, 2024, 38(1): 83-89. doi: 10.12299/jsues.23-0054

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Self-training credit evaluation integrated classification model based on data editing

doi: 10.12299/jsues.23-0054

LIU Wenjie,
WANG Guoqiang^,

School of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai 201620, China

Received Date: 2023-03-06
Publish Date: 2024-03-30

Abstract

Abstract

Aiming at the problems of unbalance of credit data and difficult acquisition of label data, a self-training credit evaluation integrated classification model based on data editing was proposed. Firstly, synthetic minority over-sampling technique (SMOTE) was used to sample labeled samples to alleviate data imbalance. Secondly, a Stacking integration model was constructed on a few labeled sample datasets and unlabeled samples were "falsified" to obtain label-like data. Finally, an improved semi-supervised double-weighted K-nearest neighbor algorithm was proposed, which was used to clip the pseudo-label data and expand the training set until the model converged. Simulation experiments of UCI and Kaggle credit evaluation dataset show that the model has better predictive performance and can identify a few types of samples more effectively.
- credit evaluation,
- semi-superised learning,
- Stacking integration strategy,
- data editing,
- self-training

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References(23)

References

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