  Ensemble pruning is the process of selecting a subset of componentclassifiers
from an ensemble which performs at least as well as theoriginal ensemble while
reducing storage and computational costs.Ensemble pruning in data streams is a
largely unexplored area ofresearch. It requires analysis of ensemble components
as they arerunning on the stream, and differentiation of useful classifiers
fromredundant ones. We present CCRP, an on-the-fly ensemble prun-ing method for
multi-class data stream classification empoweredby an imbalance-aware fusion of
class-wise component rankings.CCRP aims that the resulting pruned ensemble
contains the bestperforming classifier for each target class and hence, reduces
the ef-fects of class imbalance. The conducted experiments on real-worldand
synthetic data streams demonstrate that different types of en-sembles that
integrate CCRP as their pruning scheme consistentlyyield on par or superior
performance with 20% to 90% less averagememory consumption. Lastly, we validate
the proposed pruningscheme by comparing our approach against pruning schemes
basedon ensemble weights and basic rank fusion methods.
