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...

全面介紹

Saved in:
書目詳細資料
主要作者: Bingamawa, Muhammad Tosan
格式: Thesis
語言:English
English
出版: 2017
主題:
Q Science (General)
QA76 Computer software
在線閱讀: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
標簽: 添加標簽
沒有標簽, 成為第一個標記此記錄!
成為第一個發表評論!
您必須先登錄

相似書籍