用非线性机器学习预测日本国会选举，效果优于传统方法
Forecasting Japanese elections: A nonlinear machine-learning approach
- 采用决策树与集成学习构建非线性预测模型
- 在样本内外均比经典线性模型准确度略高
- 为单一国家选举预测提供可复现的新范式
尽管日本是全球最重要的发达民主国家之一，其全国性选举预测模型的发展仍较有限。本文引入基于决策树与集成学习的非线性机器学习模型，用于预测日本众议院选举结果。为评估方法优势，我们复现了Lewis-Beck和Tien（LBT）关于日本选举预测的经典统计模型的理论框架与数据集。结果显示，我们的模型在样本内与样本外评估中均表现出适度但持续更高的预测准确率，表明非线性算法在捕捉复杂选举行为方面优于传统线性方法。本研究是早期将非线性机器学习应用于单一国家选举预测的尝试之一，提供了可复现的分析框架，结合其他国家的特定选举理论，或可提升更广泛国家情境下的预测性能。
原文摘要 · Abstract (English)
Despite Japan being one of the world's largest advanced democracies, the development of election forecasting models for its national elections remains limited. This study introduces nonlinear machine-learning forecasting models, based on decision tree and ensemble learning methods, for predicting the outcomes of Japanese lower-house elections. To assess the methodological benefits of our approach, we replicated the theoretical framework and dataset of Lewis-Beck and Tien's (LBT) foundational statistical forecasting model for Japanese elections. Our models demonstrated moderately but consistently improved predictive accuracy compared to LBT's model in both in-sample and out-of-sample evaluations, suggesting that nonlinear algorithms offer an alternative approach to classical linear methods in capturing complex electoral dynamics. This study represents one of the earlier applications of nonlinear machine-learning techniques to single-country election forecasting. It offers a replicable framework that, when combined with the country-specific electoral theories of other nations, may enhance the predictive performance of forecasting models in broader national contexts.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。