Title :
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
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;
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2010 International Conference on
Conference_Location :
Qingdao
Print_ISBN :
978-1-4244-6526-2
DOI :
10.1109/ICMLC.2010.5580511
