A Comparison of Undersampling, Oversampling, and SMOTE Methods for Dealing with Imbalanced Classification in Educational Data Mining.
Tarid WongvorachanSurina HeOkan BulutPublished in: Inf. (2023)
Keyphrases
- class imbalance
- imbalanced data sets
- sampling methods
- cost sensitive learning
- class distribution
- machine learning methods
- majority class
- support vector
- active learning
- classification accuracy
- benchmark data sets
- class imbalanced
- databases
- imbalanced datasets
- imbalanced data
- minority class
- high dimensionality
- text classification
- feature selection