Liquid Democracy for Low-Cost Ensemble Pruning

We argue that there is a strong connection between ensemble learning and a delegative voting paradigm -- liquid democracy -- that can be leveraged to reduce ensemble training costs. We present an incremental training procedure that identifies and removes redundant classifiers from an ensemble via delegation mechanisms inspired by liquid democracy. Through both analysis and extensive experiments we show that this process greatly reduces the computational cost of training compared to training a full ensemble. By carefully selecting the underlying …

analysis classifiers cost costs cs.ai cs.lg cs.ma democracy ensemble incremental low paradigm pruning reduce through training training costs via voting