TY  - CHAP
AB  - Abstract We propose an oversampling technique to increase the true positive rate (sensitivity) in classifying imbalanced datasets (i.e., those with a value for the target variable that occurs with a small frequency) and hence boost the overall performance measurements such as balanced accuracy, G-mean and area under the receiver operating characteristic (ROC) curve, AUC. This oversampling method is based on the idea of applying the Synthetic Minority Oversampling Technique (SMOTE) on only a selective portion of the dataset instead of the entire dataset. We demonstrate the effectiveness of our oversampling method with four real and simulated datasets generated from three models.
VL  - 12
SN  - 978-1-78743-069-3, 978-1-78743-070-9/1477-4070
DO  - 10.1108/S1477-407020170000012004
UR  - https://doi.org/10.1108/S1477-407020170000012004
AU  - Nguyen Son
AU  - Quinn John
AU  - Olinsky Alan
PY  - 2017
Y1  - 2017/01/01
TI  - An Oversampling Technique for Classifying Imbalanced Datasets
T2  - Advances in Business and Management Forecasting
T3  - Advances in Business and Management Forecasting
PB  - Emerald Publishing Limited
SP  - 63
EP  - 80
Y2  - 2024/04/25
ER  -