Research on Credit Risk Early-Warning for Listed Companies in Chengyu Economic Zone Based on Best Fuzzy Support Vector Machine

DOI

10.2991/rac-16.2016.82How to use a DOI?

Keywords

enterprise credit risk; FSVM; kernel function; Chengyu economic zone; early- warning

Abstract

Taking listed companies in Chengyu Economic Zone as an example, this paper introduces the fuzzy algorithm into support vector machine (SVM), constructing the model of fuzzy support vector machine (FSVM) for Credit risk early-warning, which based on four different kernel functions (linear, polynomial, sigmoid and Gauss radial basis) are compared as well as compared with traditional statistical models and other artificial intelligent models. The result of investigation illustrates that FSVM based on Gauss radial basis kernel function is not only superior to that based on other three kernel functions, but also better significantly than traditional statistical models and other artificial intelligent models.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Kai Xu
AU  - Zongfang Zhou
PY  - 2016/11
DA  - 2016/11
TI  - Research on Credit Risk Early-Warning for Listed Companies in Chengyu Economic Zone Based on Best Fuzzy Support Vector Machine
BT  - Proceedings of the 7th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention
PB  - Atlantis Press
SP  - 513
EP  - 518
SN  - 1951-6851
UR  - https://doi.org/10.2991/rac-16.2016.82
DO  - 10.2991/rac-16.2016.82
ID  - Xu2016/11
ER  -