DocumentCode
2839043
Title
Credit Scoring Model Based on Simple Naive Bayesian Classifier and a Rough Set
Author
Jiang, Yi ; Wu, Li Hua
Author_Institution
Dept. of Comput. Sci., Xiamen Univ., Xiamen, China
fYear
2009
fDate
11-13 Dec. 2009
Firstpage
1
Lastpage
4
Abstract
This paper presents a new approach to credit scoring by synthesizing simple nai¿ve Bayesian classifier (SNBC) and the rough set theory. We adopted the combination of SNBC and rough set theory to build credit scoring model. The experiment was done on German Credit Database and showed that the model has a good prediction performance and has real world value upon application.
Keywords
Bayes methods; finance; pattern classification; rough set theory; German Credit Database; credit scoring; rough set theory; simple naive Bayesian classifier; Bayesian methods; Computer science; Data mining; Databases; Finance; Linear discriminant analysis; Logistics; Predictive models; Set theory; Statistical analysis;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on
Conference_Location
Wuhan
Print_ISBN
978-1-4244-4507-3
Electronic_ISBN
978-1-4244-4507-3
Type
conf
DOI
10.1109/CISE.2009.5364639
Filename
5364639
Link To Document