DocumentCode :
2240277
Title :
A New Method for Estimating Bank Credit Risk
Author :
Chiu, Jiun-Yao ; Yan, Yan ; Xuedong, Gao ; Chen, Rung-Ching
Author_Institution :
Dept. of Inf. Manage., Chaoyang Univ. of Technol., Taichung, Taiwan
fYear :
2010
fDate :
18-20 Nov. 2010
Firstpage :
503
Lastpage :
507
Abstract :
Stating from the complement between rough sets and decision tree classification algorithm, it proposes a new method of data mining based on rough sets and decision tree classification algorithm, and applies it in the estimating of bank credit risk with the help of a RSES (Rough Set Exploration System) software system for data mining. Experiments have proved that this new method of date mining retains the internal features of the original data, speeds up the process of access to knowledge, improves the classification accuracy rate, enhances the interpretability of the rules, and achieves satisfactory results.
Keywords :
bank data processing; credit transactions; data mining; decision trees; pattern classification; risk analysis; rough set theory; RSES software system; bank credit risk estimation; classification accuracy rate; data mining; decision tree classification; rough set exploration system; RSES; bank credit risk; decision tree classification; rough sets;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Technologies and Applications of Artificial Intelligence (TAAI), 2010 International Conference on
Conference_Location :
Hsinchu City
Print_ISBN :
978-1-4244-8668-7
Electronic_ISBN :
978-0-7695-4253-9
Type :
conf
DOI :
10.1109/TAAI.2010.85
Filename :
5695500
Link To Document :
بازگشت