DocumentCode
2861826
Title
Multi-Classifier Combination for Banks Credit Risk Assessment
Author
Zhou, Qifeng ; Lin, Chengde ; Yang, Wei
Author_Institution
Dept. of Autom., Xiamen Univ.
fYear
2006
fDate
24-26 May 2006
Firstpage
1
Lastpage
4
Abstract
Credit risk assessment problem belongs essentially to a classification problem. In this paper, a multi-classifier combination algorithm has been developed for banks credit risk assessment. We adopt back-propagation (BP) algorithm as the meta-learning algorithm and compared the methods of bagging and boosting to construct the multi-classifier system (MCS). Experimental results on real client´s data illustrate the effectiveness of the proposed method
Keywords
backpropagation; bank data processing; credit transactions; risk management; backpropagation algorithm; bagging-boosting methods; banks credit risk assessment problem; classification problem; metalearning algorithm; multiclassifier combination algorithm; Automation; Bagging; Boosting; Educational institutions; Neural networks; Probability; Risk management; Statistical analysis; Statistics; Voting;
fLanguage
English
Publisher
ieee
Conference_Titel
Industrial Electronics and Applications, 2006 1ST IEEE Conference on
Conference_Location
Singapore
Print_ISBN
0-7803-9513-1
Electronic_ISBN
0-7803-9514-X
Type
conf
DOI
10.1109/ICIEA.2006.257319
Filename
4025920
Link To Document