The model of credit risk assessment in power industry base on RS-SVM

Author

Sun, Wei ; Du, Qiu-shi ; Cui, Bo

Author_Institution

Dept. of Econ. Manage., North China Electr. Power Univ., Baoding, China

Volume

4

fYear

2010

fDate

11-14 July 2010

Firstpage

2021

Lastpage

2025

Abstract

In this paper, according to the situation of credit risk assessment in power industry, index system of risk assessment was established. Credit risk assessment models based on rough set and support vector machines (RSSVM) were proposed for the characteristic of more indicator numbers. Through introducing actual data of a power industry to the empirical analysis, this method was testified that it can classify the data in a high accuracy. The research illustrates that the model mentioned above has good results, and the method is practical and feasible.

Keywords

economic indicators; electricity supply industry; finance; pattern classification; power plants; risk management; rough set theory; support vector machines; credit risk assessment; data classification; empirical analysis; power industry; risk assessment index system; rough set; support vector machine; Accuracy; Indexes; Power industry; Risk management; Support vector machine classification; Training; Credit Risk Assessment; Power Industry; Rough Set; Support Vector Machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics (ICMLC), 2010 International Conference on

Conference_Location

Qingdao

Print_ISBN

978-1-4244-6526-2

Type

conf

DOI

10.1109/ICMLC.2010.5580511

Filename

5580511