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 delegation mechanism, weight centralization in the classifier population is avoided, leading to higher accuracy than some boosting methods. Furthermore, this work serves as an exemplar of how frameworks from computational social choice literature can be applied to problems in nontraditional domains.

翻译：我们认为，集成学习与一种委托式投票范式——流动式民主之间存在着密切联系，这种关联可被用于降低集成训练成本。我们提出了一种增量式训练流程，通过受流动式民主启发的委托机制，识别并从集成中移除冗余分类器。分析与大量实验均表明，与传统完整集成训练相比，该方法显著降低了计算开销。通过谨慎选择底层委托机制，避免了分类器群体中的权重集中现象，从而在某些情况下获得了比提升方法更高的准确率。此外，本研究还展示了如何将计算社会选择领域的理论框架应用于非传统领域的问题。