DocumentCode :
2467393
Title :
Credit risk evaluation with extreme learning machine
Author :
Zhou, Hongming ; Lan, Yuan ; Soh, Yeng Chai ; Huang, Guang-Bin ; Zhang, Rui
Author_Institution :
Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore
fYear :
2012
fDate :
14-17 Oct. 2012
Firstpage :
1064
Lastpage :
1069
Abstract :
Credit risk evaluation has become an increasingly important field in financial risk management for financial institutions, especially for banks and credit card companies. Many data mining and statistical methods have been applied to this field. Extreme learning machine (ELM) classifier as a type of generalized single hidden layer feed-forward networks has been used in many applications and achieve good classification accuracy. Thus, we use ELM (kernel based) as a classification tool to perform the credit risk evaluation in this paper. The simulations are done on two credit risk evaluation datasets with three different kernel functions. Simulation results show that the kernel based ELM is more suitable for credit risk evaluation than the popular used Support Vector Machines (SVMs) with consideration of overall, good and bad accuracies.
Keywords :
data mining; feedforward neural nets; financial data processing; generalisation (artificial intelligence); learning (artificial intelligence); pattern classification; risk management; statistical analysis; bank; classification accuracy; credit card company; credit risk evaluation; data mining; extreme learning machine classifier; financial institution; financial risk management; generalized single hidden layer feed-forward network; statistical method; Accuracy; Kernel; Machine learning; Polynomials; Support vector machines; Training; Training data; Confusion Matrix; Credit Risk Evaluation; Extreme learning machine (ELM); Support Vector Machine (SVM);
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man, and Cybernetics (SMC), 2012 IEEE International Conference on
Conference_Location :
Seoul
Print_ISBN :
978-1-4673-1713-9
Electronic_ISBN :
978-1-4673-1712-2
Type :
conf
DOI :
10.1109/ICSMC.2012.6377871
Filename :
6377871
Link To Document :
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