Research on Personal Credit Evaluation Based on Graph Convolutional Network
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.
personal credit assessmentgraph convolutional networkfeature selectionfeature importancerandom forest
国家科技期刊平台
NSTL
万方数据
