Cluster-Based Minority Over-Sampling for Imbalanced Datasets.
Kamthorn PuntumaponThanawin RakthanmanonKitsana WaiyamaiPublished in: IEICE Trans. Inf. Syst. (2016)
Keyphrases
- imbalanced datasets
- minority class
- majority class
- imbalanced class distribution
- sampling methods
- class imbalance
- class distribution
- imbalanced data
- cost sensitive learning
- learning from imbalanced data
- classification error
- nearest neighbour
- random sampling
- support vector machine
- decision boundary
- rare class
- original data
- training set
- ensemble learning
- training dataset
- highly skewed
- active learning
- decision trees
- cost sensitive
- test set
- sampling algorithm
- ensemble methods
- training data
- misclassification costs
- linear regression
- machine learning