NDLI: Classifiers based on Developed SVM for Credit Analysis in Electricity Market

Content Provider	IEEE Xplore Digital Library
Author	Ying Xie Wen-jie Huang
Copyright Year	2007
Description	Author affiliation: North China Electr. Power Univ., Beijing (Ying Xie; Wen-jie Huang)
Abstract	The credit analysis on electricity users is of great use for the decision-making and management of market trades in the market. But due to the short history and the complexity of client information, the credit analysis in electricity market has indeed formed a typical nonlinear classification problem with small samples, which is still unsolved by traditional methods. To solve above problem, a novel classifiers based on developed SVM for the credit analysis in electricity market is presented, where two algorithms are integrated: 1)support vector machine (SVM) is the basic algorithm with special adaptability and advantage in nonlinear higher-dimensional pattern identification with small samples; 2) independent component analysis (ICA) is an excellent tool for blind signal separation. In the model, first the attributes of credit analysis for electricity uses are reconstructed by ICA in order to overcome the degradation of the latent noise and redundancy in SVM inputs. Second, the mined attributes with better information are fed to SVM for credit classification. In this way, the accuracy of SVM classifier is fatherly enhanced by combining with ICA. And then the performance of the credit analysis is improved. Simulation result shows that the proposed method may increase the accuracy of credit analysis.
Starting Page	797
Ending Page	801
File Size	491221
Page Count	5
File Format	PDF
ISBN	9781424407361
DOI	10.1109/ICIEA.2007.4318517
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-05-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Support vector machine classification Electricity supply industry Independent component analysis Decision making Energy management History Information analysis Pattern analysis Signal analysis independent component analysis credit analysis classification support vector machine
Content Type	Text
Resource Type	Article

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