Communication Dans Un Congrès Année : 2025

XStacking: Explanation-Guided Stacked Ensemble Learning

Résumé

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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hal-05250779 , version 1 (11-09-2025)

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Moncef Garouani, Ayah Barhrhouj, Olivier Teste. XStacking: Explanation-Guided Stacked Ensemble Learning. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - ECMLPKDD 2025, Sep 2025, Porto (Portugal), Portugal. ⟨hal-05250779⟩
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