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An RB\u2010XGBoost algorithm\u2010based e\u2010commerce 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\u2010commerce risk assessment model and assessed the e\u2010commerce 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\u2010commerce credit risk assessment accuracy.<\/jats:p>","DOI":"10.1155\/2021\/7066304","type":"journal-article","created":{"date-parts":[[2021,10,25]],"date-time":"2021-10-25T19:50:08Z","timestamp":1635191408000},"update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Study on RB\u2010XGBoost Algorithm\u2010Based e\u2010Commerce Credit Risk Assessment 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