Minority Oversampling in Kernel Adaptive Subspaces for Class Imbalanced Datasets.
Chin-Teng LinTsung-Yu HsiehYu-Ting LiuYang-Yin LinChieh-Ning FangYu-Kai WangGary G. YenNikhil R. PalChun-Hsiang ChuangPublished in: IEEE Trans. Knowl. Data Eng. (2018)
Keyphrases
- minority class
- imbalanced datasets
- majority class
- class imbalance
- class distribution
- imbalanced data
- classification error
- original data
- learning from imbalanced data
- cost sensitive learning
- decision boundary
- support vector machine
- kernel function
- sampling methods
- nearest neighbour
- training dataset
- imbalanced class distribution
- training set
- feature space
- ensemble learning
- active learning
- cost sensitive
- kernel methods
- test set
- nearest neighbor
- high dimensional
- high dimensional data
- training samples
- decision trees
- high dimensionality
- low dimensional
- training data
- support vector
- data points
- knn
- principal component analysis
- multi class classification
- binary classification
- class labels
- test data
- data sets