AdaCC: cumulative cost-sensitive boosting for imbalanced classification.
Vasileios IosifidisSymeon PapadopoulosBodo RosenhahnEirini NtoutsiPublished in: Knowl. Inf. Syst. (2023)
Keyphrases
- class imbalance
- cost sensitive boosting
- cost sensitive
- support vector machine
- decision trees
- classification accuracy
- image classification
- imbalanced data
- class labels
- machine learning algorithms
- imbalanced data sets
- training set
- classification algorithm
- model selection
- binary classification problems
- high dimensionality
- text classification
- feature space
- machine learning
- benchmark datasets
- data sets
- active learning
- feature vectors
- binary classification
- support vector
- minority class
- feature extraction
- multi class problems
- feature selection