Consumer Credit Evaluation Model in Tmall Using SVM Method

被引：0

作者：

Chen, Shuang ^{[1
]}

Li, Dan ^{[1
]}

Gao, HongYun ^{[1
]}

Meng, FanYun ^{[2
]}

机构：

[1] Dalian Univ, Informat & Engn Coll, Dalian 116622, Peoples R China

[2] Qingdao Technol Univ, Sch Comp, Qingdao 266033, Peoples R China

来源：

2017 INTERNATIONAL CONFERENCE ON FINANCIAL MANAGEMENT, EDUCATION AND SOCIAL SCIENCE (FMESS 2017) | 2017年

关键词：

E-commerce; consumer credit evaluation; B2C; B2C E-COMMERCE; TRUST;

D O I：

暂无

中图分类号：

F8 [财政、金融];

学科分类号：

0202 ;

摘要：

Reducing mistrust between businesses and consumers is a central motivating force of e-commerce. This research identifies a number of key factors related to consumer credit in the B2C context and investigates a method which named support vector machines(SVM) to hedge consumer credit evaluation. Our experimental results of consumer credit evaluation based on data sets from Tmall show that SVM can enhance the separation of different consumers.

引用

页码：27 / 30

页数：4

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