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政大典藏 > College of Commerce > Department of Risk Management and Insurance > Proceedings > Item 140.119/75489

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/75489

Title:	Classification methods of credit rating - A comparative analysis on SVM, MDA and RST
Authors:	Hsu, C.F.;Hung, Hsufeng 洪敘峰
Contributors:	企管系
Keywords:	Bank credit;Classification accuracy;Classification methods;Classification models;Comparative analysis;Credit ratings;Customer relationship management;Data sets;Decision making support system;Discriminate analysis;Empirical analysis;Feature selection;Risk measurement;SVM model;Two classification;Artificial intelligence;Classification (of information);Computer software;Public relations;Rating;Risk assessment;Software architecture;Support vector machines;Decision making
Date:	2009-12
Issue Date:	2015-06-02 10:18:55 (UTC+8)
Abstract:	The execution and the result of bank credit rating are closely linked with the bank`s investment and loan policies which form the initial risk measurement. It is an important and a shouldn`t ignored issue for bankers to set up a scientific, objective and accurate credit rating model in the field of customer relationship management. In this study, two classification methods, multiple discriminate analysis (MDA), CANDISC, and support vector machine (SVM) are applied to conduct a comparative empirical analysis using real world commercial loan data set. The result comes out that SVM model has reliable high classification accuracy under feature selection and therefore is suitable for bank credit rating. This study suggests the decision-making personnel to establish a decision-making support system to assist their judgment by using the classification model. ©2009 IEEE.
Relation:	Proceedings - 2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009,-
Data Type:	conference
DOI link:	http://dx.doi.org/10.1109/CISE.2009.5366068
DOI:	10.1109/CISE.2009.5366068
Appears in Collections:	[Department of Risk Management and Insurance] Proceedings

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