Using Decision Tree Classification to Design the Credit Rating Model
| المصدر: | مجلة الجامعة الوطنية |
|---|---|
| الناشر: | الجامعة الوطنية |
| المؤلف الرئيسي: | Al Baraq, Mowaffak O. A. (Author) |
| مؤلفين آخرين: | Alqobaty, Abdulftah Salem (Co-Author) |
| المجلد/العدد: | ع13 |
| محكمة: | نعم |
| الدولة: |
اليمن |
| التاريخ الميلادي: |
2020
|
| الشهر: | اغسطس |
| الصفحات: | 1 - 23 |
| DOI: |
10.46514/1971-000-013-007 |
| ISSN: |
2519-6022 |
| رقم MD: | 1106641 |
| نوع المحتوى: | بحوث ومقالات |
| اللغة: | الإنجليزية |
| قواعد المعلومات: | EduSearch, HumanIndex |
| مواضيع: | |
| كلمات المؤلف المفتاحية: |
Prediction System | Decision Tree Classification | Credit Rating | Credit Grade | Credit Score | Creditworthiness
|
| رابط المحتوى: |
عدد مرات التحميل
29
| المستخلص: |
The paper describes a credit scoring model based on bagging decision trees, a powerful learning technique that aggregates several decision trees to form a classifier given by a weighted majority vote of classifications predicted by individual decision trees. Credit rating is one application of data mining in banking industry to speed up the decision-making and improving customer credit quality related to many vectors. One of the fundamental tasks in credit risk management is to assign a credit grade to a borrower. Grades are used to rank customers according to their perceived creditworthiness. Grades come in two categories credit ratings and credit scores. Credit ratings are a small discrete classes, usually labelled with letters, whereas credit scores are numeric grades. The results shows that the developed model ranks banking customers with high accuracy by using decision tree making classification algorithms. The proposed classification model can also be used to classify of new applications of banking customers. The accuracy reported was %92.2 as recognition rat gained by this model. |
|---|---|
| ISSN: |
2519-6022 |
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