Handling imbalanced datasets for machine learning with feature extraction and sampling techniques
Ryan (Alireza) Mardani, Daniel O. Trad
In classification problems, if the dataset is skewed, most machine learning algorithms produce poor prediction results for minor classes. In this study, we used different over-sampling techniques to balance the dataset that includes well logs and rock facies. XGBoost model is employed for this multi-class classification problem. We found that oversampling can improve prediction results in minor classes. Overall, Synthetic Minority Oversampling Technique (SMOTE) is a better candidate for oversampling, though for some classes Adaptive Synthetic Sampling (ADASYN) could compete with the SMOTE performance.