Please use this identifier to cite or link to this item:
Title: Credit risk evaluation with extreme learning machine
Authors: Zhou, Hongming
Lan, Yuan
Soh, Yeng Chai
Huang, Guang-Bin
Zhang, Rui
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2012
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.
DOI: 10.1109/ICSMC.2012.6377871
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Conference Papers

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.