The Application of Weighted Kernel Fisher Discriminant Analysis in Student Loans Default

被引：0
|
作者：
Tang Qin [1 ]
Zeng Jianyou [2 ]
Li Xing [1 ]
Zhang Hongyang [1 ]
机构：
[1] China Univ Geosci, Sch Math & Phys, Wuhan 430074, Peoples R China
[2] China Univ Geosci, Sch Arts & Commun, Wuhan 430074, Peoples R China
来源：
PROCEEDINGS OF THE 8TH INTERNATIONAL CONFERENCE ON INNOVATION AND MANAGEMENT | 2011年
关键词：
Factor analysis; Weighted kernel fisher discriminated analysis; Loan defaults;
D O I：
暂无
中图分类号：
C93 [管理学];
学科分类号：
12 ; 1201 ; 1202 ; 120202 ;
摘要：
This paper took 2782 data of non-defaulted and defaulted state-subsidized student loan in a university as samples. Firstly, by using Factor Analysis, 7 factors were picked up from original 12 attributes of every sample. Then 70% data were served as training samples and 30% data were served as test samples. Furthermore, Fisher Discriminated, Bayesian Discriminate and Weighted Kernel Fisher Discriminated were respectively used to classify these data. The result indicated that the accuracy rate of Fisher Discriminated was 54.08%, while the accuracy rate of Bayesian was 67.99% and Weighted Kernel Fisher Discriminated reached 74.0%. To decision and management, this research has guiding significance for banks, and the principle and the method can also be applied into other similar problems.
引用
收藏
页码：1274 / +
页数：2
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