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
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;
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
DOI :
10.1109/CISE.2009.5364639
