Liquid Democracy for Low-Cost Ensemble Pruning

We show that there is a strong connection between ensemble learning and a delegative voting paradigm, liquid democracy, which can be leveraged to reduce ensemble training costs. We present an incremental training procedure that removes redundant classifiers from an ensemble via delegation. By carefully selecting the underlying delegation mechanism weight-centralization among classifiers is avoided, leading to higher accuracy than some boosting methods with a significantly lower cost than training a full ensemble. This work serves as an exemplar of how ideas from computational social choice can be applied to problems in nontraditional domains.

Authors

• Ben Armstrong Tulane University
• Kate Larson University of Waterloo

Keywords

• Liquid Democracy
• Machine Learning
• Ensembles
• Pruning