Radius-SMOTE: A New Oversampling Technique of Minority Samples Based on Radius Distance for Learning From Imbalanced Data.
Gede Angga PradiptaRetantyo WardoyoAina MusdholifahI Nyoman Hariyasa SanjayaPublished in: IEEE Access (2021)
Keyphrases
- minority class
- learning from imbalanced data
- imbalanced datasets
- imbalanced data
- class imbalance
- majority class
- class distribution
- sampling methods
- classification error
- class imbalanced
- cost sensitive learning
- decision boundary
- support vector machine
- nearest neighbour
- training set
- ensemble learning
- training data
- euclidean distance
- training dataset
- cost sensitive
- training samples
- text classification