NDLI: Research of evaluating credit-risk in power enterprise based on SVM and VIKOR method

Content Provider	IEEE Xplore Digital Library
Author	Yuansheng Huang Ying Yan
Copyright Year	2008
Description	Author affiliation: Dept. of Economic & Manage., North China Electr. Power Univ., Baoding, China (Yuansheng Huang; Ying Yan)
Abstract	Some clients are in arrears with a great amount of electricity charges in operation of power system, which has seriously hindered the healthy development of electric industry. Evaluation of clients¿ credit is an important problem of management in power supply enterprises.Considering the training time and the accuracy, a new algorithm based on Support Vector Machine (SVM) and VIKOR method is adopted to solve this problem. Support Vector Machine (SVM) was proposed to solve the small sample learning problem. VIKOR method was developed to solve decision problems with conflicting and with different units criteria.Finally, an application example has been given to test the feasibility and effectiveness of the proposed method.
Starting Page	1596
Ending Page	1599
File Size	648322
Page Count	4
File Format	PDF
ISBN	9781424426294
DOI	10.1109/IEEM.2008.4738141
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-08
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	risk management Power supplies Power system management credit-risk SVM classification Management training Support vector machines Industrial training VIKOR method Machine learning Electricity supply industry Load management Power systems Energy management
Content Type	Text
Resource Type	Article

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