A Study on RB-XGBoost Algorithm-Based e-Commerce Credit Risk Assessment Model. (25th October 2021)

• Record Type: Journal Article
• Authors: Yang, Weimin; Gao, Lili
• Other Names: Zhou Mu Academic Editor.
• Is Part Of: Journal of sensors. Volume 2021(2021)
• Publication Date: 2021-10-25
• Subjects: Detectors -- Periodicals; 681.205
• DOI: 10.1155/2021/7066304
• Languages: English
• ISSNs: 1687-725X

Abstract

Abstract : The current method's e-commerce credit risk assessment is prone to poor data balance and low evaluation accuracy. An RB-XGBoost algorithm-based e-commerce credit risk assessment model is proposed in this study. The adaptive random balance (RB) method is used to sample and process the obtained data to improve the balance degree of the data. An assessment index system is constructed based on the processed data. Based on the risk evaluation index system and the XGBoost algorithm, this paper constructed an e-commerce risk assessment model and assessed the e-commerce credit risk using this model. The experimental results show that the proposed method has good data balance, a high kappa coefficient, and a large receiver operating characteristic (ROC) curve area, which can effectively improve e-commerce credit risk assessment accuracy.