计算机与数字工程2024，Vol.52Issue(3)：768-774,820,8.DOI:10.3969/j.issn.1672-9722.2024.03.023
基于图卷积网络的个人信用评估研究
Research on Personal Credit Evaluation Based on Graph Convolutional Network
唐灵慧 1李林 1李丹2
作者信息
- 1. 上海理工大学管理学院 上海 200093
- 2. 上海颖幡技术有限公司 上海 200093
- 折叠
摘要
Abstract
In order to address the problem that traditional machine learning models cannot represent the high-dimensional neighbor relationships among lenders in the personal credit assessment problem,this paper proposes a graph convolutional net-work-based personal credit assessment model from a network science perspective,taking into account the multidimensional interre-lationships among lenders.To avoid the impact of feature data redundancy on the accuracy of the model,firstly,recursive feature elimination is used to filter out the feature set that contributes most to the personal credit assessment.Secondly,the importance weights of the filtered features are calculated using random forest,and the features are classified into category features and numeri-cal features.The distance between lenders is calculated based on the feature types and feature weights to obtain the adjacency matrix of the lender network.Finally,the constructed adjacency matrix with lender feature data is input into graph convolutional network for training and predicting the results.Based on the publicly available German personal credit dataset,the model is compared with the results of four recent studies through two evaluation metrics,as well as with three benchmark models through four evaluation met-rics.The experimental results show that the prediction results of this method are all better than other models and can perform person-al credit assessment more accurately.关键词
个人信用评估/图卷积网络/特征选择/特征重要性/随机森林Key words
personal credit assessment/graph convolutional network/feature selection/feature importance/random forest分类
信息技术与安全科学引用本文复制引用
唐灵慧,李林,李丹..基于图卷积网络的个人信用评估研究[J].计算机与数字工程,2024,52(3):768-774,820,8.
