By Topic

Credit Risk Assessment Based on Fuzzy SVM and Principal Component Analysis

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Zhao Min ; Sch. of Electron. & Inf. Eng., Sichuan Univ., Chengdu, China

Credit risk assessment has been an important research topic in customer relationship management. It is also an important field for commercial banks because discriminating good creditors from bad ones is becoming more and more crucial for banks. A Fuzzy Support Vector Machine (FSVM) classification model based on principal component analysis (PCA-FSVM) was advanced, which adapted PCA to extract principal components to replace the original indexes, so that the processing speed and classification accuracy can be improved. Then credit risk assessment example that apply this classification model was provided and compared with the method of SVM and BP neural networks, which shows the better performance and better classification accuracy of PCA-FSVM.

Published in:

Web Information Systems and Mining, 2009. WISM 2009. International Conference on

Date of Conference:

7-8 Nov. 2009