C-SMOTE: Continuous Synthetic Minority Oversampling for Evolving Data Streams.
Alessio BernardoHeitor Murilo GomesJacob MontielBernhard PfahringerAlbert BifetEmanuele Della VallePublished in: IEEE BigData (2020)
Keyphrases
- evolving data streams
- minority class
- class imbalance
- concept drift
- class distribution
- class imbalanced
- majority class
- data streams
- classification error
- imbalanced data
- stream mining
- streaming data
- sampling methods
- support vector machine
- nearest neighbour
- random forests
- stream clustering
- decision boundary
- change detection
- training set
- cost sensitive learning
- data stream mining
- training data
- active learning
- training dataset
- cost sensitive
- stream processing
- classification algorithm
- training examples
- data sets
- real time