The integrated methodology of rough set theory and fuzzy SVM for customer classification

In this paper, an intelligent system that hybridized rough set approach (RS) and fuzzy support vector machine (FSVM) is applied to the study of customer classification in commercial banks. We can get reduced information table, which implies that the number of evaluation criteria such as financial ra...

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Bibliographic Details
Published in2008 IEEE Conference on Cybernetics and Intelligent Systems pp. 353 - 357
Main Authors Jianguo Zhou, Tao Bai, Aiguang Zhang, Jiming Tian
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2008
Subjects
Customer Classification
Fuzzy set theory
Fuzzy sets
Fuzzy Support Vector Machine
Fuzzy systems
Hybrid intelligent systems
Machine intelligence
Performance analysis
Rough Set Theory
Rough sets
Set theory
Support vector machine classification
Support vector machines
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Summary:In this paper, an intelligent system that hybridized rough set approach (RS) and fuzzy support vector machine (FSVM) is applied to the study of customer classification 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 FSVM. The rationale of our hybrid system is using rules developed by rough sets for an object that matches any of the rules and FSVM for one that dose not match any of them. By applying the proposed approach to customer classification of China Construction Bank, RS-FSVM not only provides satisfactory approximation and generalization property, but also achieves superior performance to traditional discriminant analysis model (DA), BP neural networks (BPN) and standard SVM.
ISBN:1424416736
9781424416738
ISSN:2326-8123
DOI:10.1109/ICCIS.2008.4670954