School of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, China.
School of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing 400074, China.
Comput Intell Neurosci. 2022 Feb 21;2022:8612759. doi: 10.1155/2022/8612759. eCollection 2022.
Due to the difficulty of credit risk assessment, the current financing and loan difficulties of small- and medium-sized enterprises (SMEs) are particularly prominent, which hinders the operation and development of enterprises. Based on the previous researches, this paper first screens out features by correlation coefficient method and gradient boosting decision tree (GBDT). Then, with the help of SE-Block, the attention mechanism is added to the feature tensor of the subset separated from metadata. On this foundation, two models, XGBoost and LightGBM, are used to train four subsets, respectively, and Bayesian ridge regression is used to fuse the training results of single models under different subsets. In the simulation experiment, the AUC value of the NN-ATT-Bayesian-Stacking model reaches 0.9675 and the distribution of prediction results is ideal. The model shows good robustness, which could make a reliable assessment for the financing and loans of SMEs.
由于信用风险评估的难度，当前中小企业（SMEs）的融资和贷款困难尤为突出，这阻碍了企业的运营和发展。基于以往的研究，本文首先通过相关系数法和梯度提升决策树（GBDT）筛选出特征。然后，借助 SE-Block，为从元数据中分离出的子集的特征张量添加注意力机制。在此基础上，使用 XGBoost 和 LightGBM 两种模型分别对四个子集进行训练，并使用贝叶斯岭回归融合不同子集下的单模型的训练结果。在仿真实验中，NN-ATT-Bayesian-Stacking 模型的 AUC 值达到 0.9675，预测结果的分布较为理想。该模型表现出良好的稳健性，可为中小企业的融资和贷款提供可靠的评估。
