1 Introduction
2 Data streams
2.1 Data stream characteristics
2.2 Learning model
2.3 Concept drift
2.4 Access to labels
3 Imbalanced data
4 Imbalanced data streams
4.1 Data-level approaches
4.2 Algorithm-level approaches
4.3 Similar domains
5 Ensembles for imbalanced data streams
6 Experimental setup
6.1 Algorithms
6.2 Generators
6.3 Performance evaluation
7 Results
7.1 Binary class experiments
7.2 Multi-class experiments
7.3 Overall comparison
8 Lessons learned
9 Recommendations
10 Open challenges and future directions
11 Conclusions