提出iKF方法，揭示变量间的多阶交互关系。
Post-hoc Interpretability Illumination for Scientific Interaction Discovery
- 通过迭代选择关键变量构建森林，识别变量间交互
- 可生成不同阶次的重要变量与交互排序列表
- 区分三类交互模式，适用于科学发现与可解释建模
模型可解释性在决策应用中备受关注，但现有工具因能力或效率不足而表现有限。为此，我们提出一种新型后处理方法——迭代国王森林（iKF），用于揭示变量间的复杂多阶交互。iKF 迭代选择最重要变量作为“国王”，以该变量为根节点构建每棵树的国王森林，识别与其交互的变量，并生成不同阶次的重要变量与交互的排序短列表。此外，iKF 提供推断指标，分析所选交互模式并将其分类为三类：伴随交互、协同交互与层级交互。大量实验表明，iKF 具有强大的解释能力，展现出在跨学科科学发现和可解释建模中的巨大潜力。
原文摘要 · Abstract (English)
Model interpretability and explainability have garnered substantial attention in recent years, particularly in decision-making applications. However, existing interpretability tools often fall short in delivering satisfactory performance due to limited capabilities or efficiency issues. To address these challenges, we propose a novel post-hoc method: Iterative Kings' Forests (iKF), designed to uncover complex multi-order interactions among variables. iKF iteratively selects the next most important variable, the "King", and constructs King's Forests by placing it at the root node of each tree to identify variables that interact with the "King". It then generates ranked short lists of important variables and interactions of varying orders. Additionally, iKF provides inference metrics to analyze the patterns of the selected interactions and classify them into one of three interaction types: Accompanied Interaction, Synergistic Interaction, and Hierarchical Interaction. Extensive experiments demonstrate the strong interpretive power of our proposed iKF, highlighting its great potential for explainable modeling and scientific discovery across diverse scientific fields.
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