An applied study on credit scoring model in customer(cosmetics) mining

摘要：

The paper attempts to build models by way of various credit scoring models, based on the database of some domestic cosmetics company. These models include linear discriminant analysis method, logistic regression method, K-nearest neighbor classification method (KNN) and support vector machines (SVMs), whose results have been analyzed comparatively to look forward to gained optimal scoring model which is fit for the customers data. The model can be used to increase advertisments response rate.

关键词： credit scoring discriminant analysis logistic regression K-nearest neighbor classsification support vector machines

作者: Yang-mei Zhang-shuguang

作者单位: Department of Statistic and Finance,University of Science and Technology,Hefei,230026

会议类型: 国际会议

会议名称: International Symposium on Financial Engineering and Risk Management(2008年金融工程与风险管理)

会议地点: 上海

会议语种:英文

页码: 132-136

在线出版日期: 2008-06-01（万方平台首次上网日期，不代表论文的发表时间）