Credit Evaluation of SMEs Based on GBDT-CNN-LR Hybrid Integrated Model

Under the background of the increasing demand for credit evaluation and risk prediction, the establishment of an effective credit evaluation model for small- and medium-sized enterprises has become a research hotspot. Based on previous studies, this paper proposes a two-layer feature extraction meth...

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Bibliographic Details
Published in:Wireless communications and mobile computing 2022-02, Vol.2022, p.1-8
Main Authors: Zhang, Lei, Song, Qiankun
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Artificial neural networks
Capital losses
Classification
Credit risk
Datasets
Decision trees
Engineering
Feature extraction
Machine learning
Neural networks
Regression models
Small & medium sized enterprises-SME
Statistical methods
Support vector machines
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Summary:Under the background of the increasing demand for credit evaluation and risk prediction, the establishment of an effective credit evaluation model for small- and medium-sized enterprises has become a research hotspot. Based on previous studies, this paper proposes a two-layer feature extraction method based on Gradient Boosting Decision Tree (GBDT) and Convolutional Neural Network (CNN). First, based on the original features, GBDT is used to combine and automatically screen them, the missing values in the feature are processed, and the transformed high-dimensional sparse features are obtained. Then, CNN is used to extract features further, and finally, the logistic regression (LR) model is used to predict. In the simulation experiment, this paper takes a dataset of 14,366 small- and medium-sized enterprise credit evaluations as the analysis samples to verify the results. The results show that the GBDT-CNN-LR model has the best performance. The model also shows good generalization ability and stability in the reliability test.
ISSN:1530-8669
1530-8677
DOI:10.1155/2022/5251228