Enhancing Accuracy Of Credit Scoring Classification With Imbalance Data Using Synthetic Minority Oversampling Technique-Support Vector Machine (SMOTE-SVM) Model

Enhancing Accuracy Of Credit Scoring Classification With Imbalance Data Using Synthetic Minority Oversampling Technique-Support Vector Machine (SMOTE-SVM) Model

Credit is one of the business models that provide a significant growth. With the growth of new credit applicants and financial markets, the possibility of credit problem occurrence also become higher. Thus, it becomes important for a financial institution to conduct a preliminary selection to the cr...

وصف كامل

محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Bingamawa, Muhammad Tosan
التنسيق:	أطروحة
اللغة:	English English
منشور في:	2017
الموضوعات:	Q Science (General) QA76 Computer software
الوصول للمادة أونلاين:	http://eprints.utem.edu.my/id/eprint/20759/1/Enhancing%20Accuracy%20Of%20Credit%20Scoring%20Classification%20With%20Imbalance%20Data%20Using%20Synthetic%20Minority%20Oversampling%20Technique-Support%20Vector%20Machine%20%28SMOTE-SVM%29%20Model%20-%2024%20Pages.pdf http://eprints.utem.edu.my/id/eprint/20759/2/Enhancing%20Accuracy%20Of%20Credit%20Scoring%20Classification%20With%20Imbalance%20Data%20Using%20Synthetic%20Minority%20Oversampling%20Technique-Support%20Vector%20Machine%20%28SMOTE-SVM%29%20Model%20-%20Muhammad%20Tosan%20Bingamawa.pdf
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