Instance-Based Ensemble Pruning via Multi-Label Classification

Ensemble pruning is concerned with the reduction of the size of an ensemble prior to its combination. Its purpose is to reduce the space and time complexity of the ensemble and/or to increase the ensemble's accuracy. This paper focuses on instance-based approaches to ensemble pruning, where a different subset of the ensemble may be used for each different unclassified instance. We propose modeling this task as a multi-label learning problem, in order to take advantage of the recent advances in this area for the construction of effective ensemble pruning approaches. Results comparing the proposed framework against a variety of other instance-based ensemble pruning approaches in a variety of datasets using a heterogeneous ensemble of 200 classifiers, show that it leads to improved accuracy.