Majority-to-minority resampling for boosting-based classification under imbalanced data.
Gaoshan WangJian WangKejing HePublished in: Appl. Intell. (2023)
Keyphrases
- imbalanced data
- majority class
- minority class
- ensemble methods
- class distribution
- feature selection
- highly imbalanced
- ensemble classifier
- class imbalance
- classification models
- imbalanced datasets
- support vector machine
- cost sensitive
- decision boundary
- image classification
- benchmark datasets
- classification accuracy
- decision trees
- machine learning
- imbalanced data sets
- linear regression
- pattern classification
- ensemble learning
- feature vectors
- imbalanced class distribution
- prediction accuracy
- class imbalanced
- high dimensionality
- svm classifier
- nearest neighbour
- text classification
- cost sensitive learning
- training set
- random forest
- base learners
- base classifiers
- classification algorithm
- class labels
- training samples
- least squares