Document Type : Research Paper

Authors

Abstract

This research study aims at using Data Mining and Fuzzy Logic
approaches to classify the credit scoring of banking system applicants
as to cover uncertainties and ambiguity connected with applicant
classes and also variables that affect their behavior.
The methodology, according to a standard Data Mining process, is to
collect and refine the client data, then those variables which are in
linguistic forms are converted to fuzzy variables under the supervision
of banking experts and final data are modeled using Fuzzy Decision
Tree, subsequently. The unfuzzy data are also modeled using the other
algorithms.
The results of the study suggest that as far as client distinction
accuracy is concerned Fuzzy Decision Tree produces better results
compared to Traditional Trees, Neural Networks, and statistical
procedures such as Logistic Regression and Bayesian Network.
However, it is not as accurate as Support Vector Machine and Genetic
Tree. On the other hand, Fuzzy Decision Tree technique has gained
better prediction than prediction performance of bank credit scoring
experts.

Keywords

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حقوقی بانک )بررسی موردی بانک کشاورزی(" ، کارشناسی ارشد، دانشگاه شهید بهشتی.
2. سبزواری، حسن، ) 3343 ( "برآورد و مقایسه مدل امتیازدهی اعتباری لاجیت و روش
مطالعه موردی: مشتریان حقوقی بانک پارسیان(" ، پایان نامه کارشناسی ارشد، ( AHP
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3. گودرزی، محمد رضا، ) 3343 ( "استفاده از منطق فازی در یادگیری درخت تصمیم فازی و
هرس کردن آن در محیط های مغشوش"، پایان نامه کارشناسی ارشد، دانشگاه علم و
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