Using Cost-Sensitive Learning and Feature Selection Algorithms to Improve the Performance of Imbalanced Classification.
Fang FengKuan-Ching LiJun ShenQingguo ZhouXuhui YangPublished in: IEEE Access (2020)
Keyphrases
- cost sensitive learning
- imbalanced datasets
- feature selection algorithms
- class imbalance
- cost sensitive
- feature selection
- class distribution
- minority class
- misclassification costs
- learning models
- active learning
- binary classification
- missing values
- decision trees
- rule extraction
- data sets
- feature subset
- selection algorithm
- high dimensionality
- feature set
- image classification
- support vector machine
- classification accuracy
- training set
- probability estimation
- support vector
- training dataset
- machine learning
- class labels
- text classification
- concept drift
- multi class
- imbalanced data
- data mining