  Ensemble pruning, selecting a subset of individual learners from an original
ensemble, alleviates the deficiencies of ensemble learning on the cost of time
and space. Accuracy and diversity serve as two crucial factors while they
usually conflict with each other. To balance both of them, we formalize the
ensemble pruning problem as an objection maximization problem based on
information entropy. Then we propose an ensemble pruning method including a
centralized version and a distributed version, in which the latter is to speed
up the former. At last, we extract a general distributed framework for ensemble
pruning, which can be widely suitable for most of the existing ensemble pruning
methods and achieve less time consuming without much accuracy degradation.
Experimental results validate the efficiency of our framework and methods,
particularly concerning a remarkable improvement of the execution speed,
accompanied by gratifying accuracy performance.
