Combining uniform manifold approximation with localized affine shadowsampling improves classification of imbalanced datasets.
Saptarshi BejPrashant SrivastavaMarkus WolfienOlaf WolkenhauerPublished in: IJCNN (2021)
Keyphrases
- imbalanced datasets
- class imbalance
- cost sensitive learning
- decision trees
- machine learning
- imbalanced data
- feature selection algorithms
- class distribution
- cost sensitive
- classification algorithm
- text classification
- image classification
- support vector machine
- feature selection
- feature extraction
- feature vectors
- sampling methods
- training dataset
- feature space
- pattern classification
- classification rules
- active learning
- training samples
- machine learning methods
- supervised learning
- ensemble methods
- classification models
- training set
- class labels
- support vector
- machine learning algorithms
- support vector machine svm
- semi supervised learning
- data sets