A Comprehensive Analysis of Synthetic Minority Oversampling Technique (SMOTE) for handling class imbalance.
Dina ElreedyAmir F. AtiyaPublished in: Inf. Sci. (2019)
Keyphrases
- comprehensive analysis
- class imbalance
- minority class
- class imbalanced
- majority class
- class distribution
- imbalanced datasets
- cost sensitive
- imbalanced data sets
- active learning
- imbalanced data
- cost sensitive learning
- sampling methods
- concept drift
- rare events
- high dimensionality
- test set
- imbalanced class distribution
- misclassification costs
- training data
- highly skewed
- training set
- classification error
- classification accuracy
- non stationary
- naive bayes