Handling Problems of Credit Data for Imbalanced Classes using SMOTEXGBoost

作     者：Heru Mardiansyah Rahmat Widia Sembiring Syahril Efendi 

作者机构：Graduate Program of Computer Science Department of Information Technology Department of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medam Indonesia 

出 版 物：《Journal of Physics: Conference Series》 

年 卷 期：2021年第1830卷第1期

学科分类：07[理学] 0702[理学-物理学] 

摘      要：Some researchers find data with imbalanced class conditions, where there are data with a number of minorities and a majority. SMOTE is a data approach for an imbalanced classes and XGBoost is one algorithm for an imbalanced data problems. This research uses SMOTE and XGBoost or abbreviated as SMOTEXGBoost for handling data with an imbalanced classes. The results showed almost the same accuracy value between SMOTE and SMOTEXGBoost at 99%. While the value of AUC SMOTEXBoost has a more stable value than SMOTE that is equal to 99.89% for training and 98.51% for testing.