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...

詳細記述

書誌詳細

第一著者:	Bingamawa, Muhammad Tosan
フォーマット:	学位論文
言語:	英語 英語
出版事項:	2017
主題:	Q Science (General) QA76 Computer software
オンライン･アクセス:	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