Bingamawa, M. T. (2017). Enhancing Accuracy Of Credit Scoring Classification With Imbalance Data Using Synthetic Minority Oversampling Technique-Support Vector Machine (SMOTE-SVM) Model.
Chicago Style (17th ed.) CitationBingamawa, Muhammad Tosan. Enhancing Accuracy Of Credit Scoring Classification With Imbalance Data Using Synthetic Minority Oversampling Technique-Support Vector Machine (SMOTE-SVM) Model. 2017.
MLA (8th ed.) CitationBingamawa, Muhammad Tosan. Enhancing Accuracy Of Credit Scoring Classification With Imbalance Data Using Synthetic Minority Oversampling Technique-Support Vector Machine (SMOTE-SVM) Model. 2017.
Warning: These citations may not always be 100% accurate.