Liquid Democracy for Low-Cost Ensemble Pruning

Autor:	Armstrong, Ben, Larson, Kate
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Computer Science - Multiagent Systems
Druh dokumentu:	Working Paper
Popis:	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. Comment: 30 pages, 20 figures. Extended abstract to appear at AAMAS 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2401.17443 Zobrazit plný text záznamu View this record from Arxiv