摘要
现有的个人信用评估模型大多数是解决信用二分类问题,而信用等级划分需要进一步研究.文中以SVM为基础,结合k-means聚类方法,假设具有相似特征的客户拥有相同信用情况,提出了k-means和SVM结合的个人信用评估模型,使之不仅能对个人进行二分类划分,且能将客户划分为不同的信用等级.实验结果表明,与其他模型相比,提出的模型二分类精度较高,并且能得到个人的信用等级,具有较高的实用价值.
Most of the existing personal credit rating models are built to solve two-classification problems. And the credit rating division needs further research. This paper is based on the SVM with the clustering method,kmeans. Assuming that the customers who have the similar characteristics have the same credit rating,the personal credit rating model based on k-means and SVM is proposed and it can divide the customers not only into two classifications but also into different credit ratings. The experimental results show that compared with other models,two classification accuracy of the proposed model is higher and it can get a personal credit rating,which has a higher practical value.
出处
《江苏科技大学学报（自然科学版）》
CAS
北大核心
2017年第6期836-842,共7页
Journal of Jiangsu University of Science and Technology:Natural Science Edition
基金
上海市教委科研创新项目(13YS015)
关键词
信用评估
支持向量机
K-MEANS
信用等级
SVM
clustering method,kmeans
customers
similar characteristics
same credit rating
personal credit rating model
