Analyzing the oversampling of different classes and types of examples in multi-class imbalanced datasets.
José A. SáezBartosz KrawczykMichal WozniakPublished in: Pattern Recognit. (2016)
Keyphrases
- multi class
- imbalanced datasets
- class imbalance
- minority class
- cost sensitive
- multiple classes
- support vector machine
- cost sensitive learning
- single class
- class distribution
- binary classifiers
- sampling methods
- feature selection
- binary classification
- multi class classification
- base classifiers
- imbalanced data
- pairwise
- misclassification costs
- classification error
- data sets
- active learning
- training data
- missing data
- training set
- decision boundary
- training examples
- nearest neighbour
- fraud detection
- feature vectors
- machine learning