GMOTE: Gaussian based minority oversampling technique for imbalanced classification adapting tail probability of outliers.
Seung Jee YangKyung Joon ChaPublished in: CoRR (2021)
Keyphrases
- class imbalance
- minority class
- class distribution
- majority class
- class imbalanced
- cost sensitive learning
- decision boundary
- classification accuracy
- cost sensitive
- support vector machine svm
- pattern classification
- imbalanced datasets
- high dimensionality
- support vector machine
- support vector
- pattern recognition
- feature selection
- classification method
- single class
- class conditional
- training set
- imbalanced data sets
- class membership
- feature extraction
- imbalanced data
- training samples
- classification error
- classification models
- probability distribution
- machine learning methods
- text classification
- nearest neighbour
- feature vectors
- active learning
- training data
- machine learning
- svm classifier
- highly imbalanced