Pro-IDD: Pareto-based ensemble for imbalanced and drifting data streams.
Muhammad UsmanHuanhuan ChenPublished in: Knowl. Based Syst. (2023)
Keyphrases
- data streams
- concept drift
- class imbalance
- ensemble learning
- ensemble classifier
- drifting concepts
- imbalanced data
- concept drifting data streams
- class distribution
- mining concept drifting data streams
- concept drifting
- streaming data
- noisy data streams
- multi objective
- sliding window
- binary classification problems
- ensemble methods
- stream data
- change detection
- genetic algorithm
- imbalanced datasets
- multi objective optimization
- sensor data
- minority class
- data distribution
- data sets
- learning algorithm
- multicriteria optimization
- outlier detection
- continuous data streams
- cost sensitive learning
- rare events
- training set
- random forests
- itemsets
- non stationary
- classification algorithm
- classifier ensemble
- majority voting
- feature selection
- sensor networks
- support vector machine
- neural network
- multi class
- active learning
- data streaming
- pruning algorithm
- differential evolution
- multiobjective optimization
- continuous queries
- random forest
- decision trees
- evolutionary algorithm