Title :
Multi-Classifier Combination for Banks Credit Risk Assessment
Author :
Zhou, Qifeng ; Lin, Chengde ; Yang, Wei
Author_Institution :
Dept. of Autom., Xiamen Univ.
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;
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
DOI :
10.1109/ICIEA.2006.257319
