NDLI: Credit Risk Assessment Using Rough Set Theory and GA-Based SVM

Content Provider	IEEE Xplore Digital Library
Author	Jianguo Zhou Tao Bai
Copyright Year	2008
Description	Author affiliation: Sch. of Bus. Adm., North China Electr. Power Univ., Beijing (Jianguo Zhou; Tao Bai)
Abstract	This paper applies a. classifier, hybridizing rough set approach and improved support vector machine(SVM) using genetic optimization algorithm (GA), to the study of credit risk assessment in commercial banks. We can get reduced information table, which implies that the number of evaluation criteria, such as financial ratios and qualitative variables is reduced with no information loss through rough set approach. And then, this reduced information table is used to develop classification rules and train SVM. Especially, in order to improve the assessment accuracy, GA is applied to optimize the parameters of SVM classifier. The rationale of our hybrid system is using rules developed by rough sets for an object that matches any of the rules and SVM for one that dose not match any of them. The effectiveness of our methodology was verified by experiments comparing traditional discriminant analysis (DA) model, BP neural networks (BPN) and standard SVM with our approach.
Starting Page	320
Ending Page	325
File Size	168628
Page Count	6
File Format	PDF
ISBN	9780769531779
DOI	10.1109/GPC.WORKSHOPS.2008.56
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-05-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Neural networks Support vector machine classification Rough sets Optimization methods Predictive models Set theory Genetics Risk management Principal component analysis
Content Type	Text
Resource Type	Article

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