Dynamically Adjusting Diversity in Ensembles for the Classification of Data Streams with Concept Drift.
Juan Isidro González HidalgoSilas G. T. C. SantosRoberto S. M. BarrosPublished in: ACM Trans. Knowl. Discov. Data (2022)
Keyphrases
- concept drift
- data streams
- data stream classification
- ensemble classifier
- classification algorithm
- ensemble learning
- classifier ensemble
- class imbalance
- streaming data
- concept drifting data streams
- drifting concepts
- drift detection
- decision trees
- sliding window
- change detection
- data stream mining
- mining data streams
- stream data
- evolving data streams
- non stationary
- classification accuracy
- batch learning
- data sets
- incremental learning
- data distribution
- feature extraction
- knn
- feature vectors
- k nearest neighbor
- stream mining
- machine learning methods
- feature space
- machine learning algorithms
- high speed data streams
- pattern recognition
- pairwise
- incoming data
- benchmark datasets