SMOTE bagging algorithm for imbalanced dataset in logistic regression analysis (case: credit of bank X)
Fithria Siti Hanifah, Hari Wijayanto, Anang Kurnia · Applied Mathematical Sciences · 2015
Logistic regression analysis is one of classification methods which is both most popular and common used. This classifier works well when the class distribution in response variable is balanced. In many real cases, the imbalanced class dataset frequently was found. This problem can affect of being difficult at obtaining a good predictive model for minority class dataset. The prediction accuracy generated will be good for majority class but not for minority class. SMOTEBagging is a combination of SMOTE and Bagging algorithm which is used to solve this problem. The purpose of this study is to create a powerful model at classifying the imbalanced data and to improve the classification performance of weak classifier. This study used credit scoring data which is imbalanced data consisting of 17 explanatory variables involved. The result from this study showed that the sensitivity and AUC value from SMOTEBagging Logistic Regression