A Study on RB-XGBoost Algorithm-Based e-Commerce Credit Risk Assessment Model

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 t...

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Autores principales:	Weimin Yang, Lili Gao
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Technology (General) T1-995
Acceso en línea:	https://doaj.org/article/7277a4a0c30b4c5da40396c61d8540d7
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id	oai:doaj.org-article:7277a4a0c30b4c5da40396c61d8540d7
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spelling	oai:doaj.org-article:7277a4a0c30b4c5da40396c61d8540d72021-11-08T02:35:47ZA Study on RB-XGBoost Algorithm-Based e-Commerce Credit Risk Assessment Model1687-726810.1155/2021/7066304https://doaj.org/article/7277a4a0c30b4c5da40396c61d8540d72021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/7066304https://doaj.org/toc/1687-7268The 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.Weimin YangLili GaoHindawi LimitedarticleTechnology (General)T1-995ENJournal of Sensors, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology (General) T1-995
spellingShingle	Technology (General) T1-995 Weimin Yang Lili Gao A Study on RB-XGBoost Algorithm-Based e-Commerce Credit Risk Assessment Model
description	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.
format	article
author	Weimin Yang Lili Gao
author_facet	Weimin Yang Lili Gao
author_sort	Weimin Yang
title	A Study on RB-XGBoost Algorithm-Based e-Commerce Credit Risk Assessment Model
title_short	A Study on RB-XGBoost Algorithm-Based e-Commerce Credit Risk Assessment Model
title_full	A Study on RB-XGBoost Algorithm-Based e-Commerce Credit Risk Assessment Model
title_fullStr	A Study on RB-XGBoost Algorithm-Based e-Commerce Credit Risk Assessment Model
title_full_unstemmed	A Study on RB-XGBoost Algorithm-Based e-Commerce Credit Risk Assessment Model
title_sort	study on rb-xgboost algorithm-based e-commerce credit risk assessment model
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/7277a4a0c30b4c5da40396c61d8540d7
work_keys_str_mv	AT weiminyang astudyonrbxgboostalgorithmbasedecommercecreditriskassessmentmodel AT liligao astudyonrbxgboostalgorithmbasedecommercecreditriskassessmentmodel AT weiminyang studyonrbxgboostalgorithmbasedecommercecreditriskassessmentmodel AT liligao studyonrbxgboostalgorithmbasedecommercecreditriskassessmentmodel
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A Study on RB-XGBoost Algorithm-Based e-Commerce Credit Risk Assessment Model

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