Next Article in Journal
The Effect of Monitoring Committees on the Relationship between Board Structure and Firm Performance
Previous Article in Journal / Special Issue
The Design and Risk Management of Structured Finance Vehicles
Article Menu

Export Article

Open AccessArticle
J. Risk Financial Manag. 2016, 9(4), 13; doi:10.3390/jrfm9040013

Credit Scoring by Fuzzy Support Vector Machines with a Novel Membership Function

School of Electrical & Automatic Engineering, Changshu Institute of Technology, Changshu 215500, China
Author to whom correspondence should be addressed.
Academic Editor: Jingzhi Huang
Received: 28 June 2016 / Revised: 15 September 2016 / Accepted: 26 October 2016 / Published: 7 November 2016
(This article belongs to the Special Issue Credit Risk)
View Full-Text   |   Download PDF [870 KB, uploaded 7 November 2016]   |  


Due to the recent financial crisis and European debt crisis, credit risk evaluation has become an increasingly important issue for financial institutions. Reliable credit scoring models are crucial for commercial banks to evaluate the financial performance of clients and have been widely studied in the fields of statistics and machine learning. In this paper a novel fuzzy support vector machine (SVM) credit scoring model is proposed for credit risk analysis, in which fuzzy membership is adopted to indicate different contribution of each input point to the learning of SVM classification hyperplane. Considering the methodological consistency, support vector data description (SVDD) is introduced to construct the fuzzy membership function and to reduce the effect of outliers and noises. The SVDD-based fuzzy SVM model is tested against the traditional fuzzy SVM on two real-world datasets and the research results confirm the effectiveness of the presented method. View Full-Text
Keywords: fuzzy support vector machine; support vector data description; credit scoring fuzzy support vector machine; support vector data description; credit scoring

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Shi, J.; Xu, B. Credit Scoring by Fuzzy Support Vector Machines with a Novel Membership Function. J. Risk Financial Manag. 2016, 9, 13.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
J. Risk Financial Manag. EISSN 1911-8074 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top