XStacking让集成学习既准又可解释，用解释引导模型融合。
XStacking: Explanation-Guided Stacked Ensemble Learning
- 用解释引导的动态特征变换，融合多个模型
- 29个数据集上同时提升预测效果与可解释性
- 适合需要可信AI决策的医疗、金融等场景
集成机器学习（EML）技术，尤其是堆叠方法，通过组合多个基础模型显著提升了预测性能。然而，这类方法常因缺乏可解释性而受到批评。本文提出XStacking，一种高效且内在可解释的框架，通过结合动态特征变换与模型无关的Shapley加性解释，使堆叠模型在保持高预测准确率的同时具备内在可解释性。我们在29个数据集上验证了该框架的有效性，结果显示其不仅提升了学习空间的预测效能，还增强了模型结果的可解释性。XStacking为负责任的机器学习提供了实用且可扩展的解决方案。
原文摘要 · Abstract (English)
Ensemble Machine Learning (EML) techniques, especially stacking, have been shown to improve predictive performance by combining multiple base models. However, they are often criticized for their lack of interpretability. In this paper, we introduce XStacking, an effective and inherently explainable framework that addresses this limitation by integrating dynamic feature transformation with model-agnostic Shapley additive explanations. This enables stacked models to retain their predictive accuracy while becoming inherently explainable. We demonstrate the effectiveness of the framework on 29 datasets, achieving improvements in both the predictive effectiveness of the learning space and the interpretability of the resulting models. XStacking offers a practical and scalable solution for responsible ML.
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