提出R2VF方法，高效合并广义线性模型中的类别变量，提升模型简洁性与可解释性。
R2VF: A Two-Step Regularization Algorithm to Cluster Categories in GLMs
- 两步法：先用正则化回归将名义变量转为序数框架，再进行变量融合
- 在不增加计算开销前提下有效缓解过拟合，识别出关键协变量集
- 适合需要简化模型、提升可解释性的统计建模场景
近年来，大量研究致力于突破广义线性模型（GLMs）对严格假设的依赖，以实现更准确、有意义的预测。这些工作包括系数正则化、特征选择以及有序类别聚类等。尽管如此，在保持低计算成本的前提下，高效聚类名义类别仍具挑战。本文提出排名到变量融合（R2VF）方法，一种两步策略，用于在GLMs中高效融合名义与序数类别。首先通过正则化回归将名义特征转换为序数框架，然后实施变量融合，从而在模型复杂度与可解释性之间取得平衡。我们通过与多种方法对比验证了R2VF的有效性，结果表明其在缓解过拟合及识别恰当协变量集合方面表现优异。
原文摘要 · Abstract (English)
Over recent decades, extensive research has aimed to overcome the restrictive underlying assumptions required for a Generalized Linear Model to generate accurate and meaningful predictions. These efforts include regularizing coefficients, selecting features, and clustering ordinal categories, among other approaches. Despite these advances, efficiently clustering nominal categories in GLMs without incurring high computational costs remains a challenge. This paper introduces Ranking to Variable Fusion (R2VF), a two-step method designed to efficiently fuse nominal and ordinal categories in GLMs. By first transforming nominal features into an ordinal framework via regularized regression and then applying variable fusion, R2VF strikes a balance between model complexity and interpretability. We demonstrate the effectiveness of R2VF through comparisons with other methods, highlighting its performance in addressing overfitting and identifying an appropriate set of covariates.
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