Title :
Credit Risk Assessment Using BP Neural Network with Dempster-Shafer Theory
Author :
Lin, Lin ; Huang, Nantian
Author_Institution :
Coll. of Inf. & Control Eng., Jilin Inst. of Chem. Technol., Jilin, China
Abstract :
In latest decades credit risk assessment has been a heavy problem in the society especially in the financial system. Credit risk assessment is a decision level decision problem. Information fusion in multi-sensor system is a very complex process, especially in the decision level fusion process. Presently some useful and representative methods, such as neural networks and Dempster-Shafer evidence theory, which can solve some decision level fusion problems. But neural network has some faults, such as bad stability, long-time training time, and bad convergence rate et al. Dempster-Shafer evidence theory needs known evidence of every objective. Aiming at theses faults of neural networks and Dempster-Shafer evidence theory, we propose a novel decision level fusion algorithm based on back-propagation neural network and Dempster-Shafer Theory to overcome the above mentioned problems of present decision level fusion methods. Meanwhile, introduce the unknown degree for all objectives in this paper. At last, some experiments show the validity and feasibility.
Keywords :
backpropagation; decision support systems; financial management; inference mechanisms; neural nets; risk management; sensor fusion; BP neural network; Dempster-Shafer evidence theory; backpropagation neural network; credit risk assessment; decision level decision problem; decision level fusion process; financial system; information fusion; multisensor system; Artificial intelligence; Artificial neural networks; Bayesian methods; Chemical technology; Computational intelligence; Control engineering; Educational institutions; Neural networks; Pattern recognition; Risk management; Dempster-Shafer (D-S) evidence theory; back-propagation neural network (BPNN); credit risk assessment; decision level fusion (DLF); information fusion (IF); multi-sensor system;
Conference_Titel :
Artificial Intelligence and Computational Intelligence, 2009. AICI '09. International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-3835-8
Electronic_ISBN :
978-0-7695-3816-7
DOI :
10.1109/AICI.2009.193