Switching Triple-Weight-SMOTE in Empirical Feature Space for Imbalanced and Incomplete Data.
Jun DouGuoliang WeiYan SongDihao ZhouMing LiPublished in: IEEE Trans Autom. Sci. Eng. (2024)
Keyphrases
- incomplete data
- feature space
- imbalanced data sets
- imbalanced data
- class imbalance
- imbalanced datasets
- class distribution
- missing values
- learning bayesian networks
- class imbalanced
- minority class
- high dimensionality
- missing data
- feature selection
- bayesian networks
- training set
- support vector machine
- cost sensitive learning
- input data
- em algorithm
- dimensionality reduction
- feature vectors
- high dimensional
- principal component analysis
- incomplete data sets
- classification accuracy
- training data
- data sets
- training samples
- kernel function
- low dimensional
- feature extraction
- data points
- multiple imputation
- rare events
- active learning
- data mining
- test set
- machine learning
- typical testors
- computer vision
- feature set
- sampling methods
- decision boundary
- training dataset
- missing attribute values
- text classification
- unlabeled data
- concept drift