发表机构
University of Southampton（南安普顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
ProToMEx是一种利用PTMs的模型无关可解释性范式，可提供全局与局部解释，其解释保真度与SHAP、LIME相当，且局部解释生成速度快约30-40倍，适用于实时场景。
AI 中文摘要
现有的机器学习分类器事后解释器主要聚焦于特征归因，为单个特征分配重要性分数。尽管这类方法具有一定价值，但难以清晰阐明往往驱动模型决策过程的复杂组合模式。为克服这一局限，我们提出ProToMEx，这是一种利用概率主题模型（Probabilistic Topic Models，PTMs）的新型可解释性范式。该模型无关框架学习潜在的“主题”，这些主题代表分类的不同高级原因，超越简单的特征重要性，揭示潜在的语义结构。ProToMEx自然提供模型整体行为的全局解释，以及能分解特定预测的多个共存原因的局部解释。我们通过实证证明，ProToMEx不仅能产生与SHAP、LIME等流行方法保真度相当的解释，还大幅降低了生成局部解释的摊销计算成本，使其非常适合实时应用。具体而言，在标准化表格数据集和合成数据集上，ProToMEx比SHAP和LIME快约30-40倍。
英文摘要
Existing post-hoc explainers for machine learning classifiers primarily focus on feature attribution, assigning importance scores to individual features. While valuable, this approach struggles to articulate the complex, combinatorial patterns that often drive a model's decision-making process. To overcome this limitation, we introduce ProToMEx, a new paradigm for explainability that leverages Probabilistic Topic Models (PTMs). Our model-agnostic framework learns latent ''topics'' that represent distinct, high-level reasons for a classification, moving beyond simple feature importance to reveal underlying semantic structures. ProToMEx naturally provides both global explanations of a model's overall behaviour and local explanations that can disentangle multiple co-existing reasons for a specific prediction. We demonstrate empirically that ProToMEx not only produces explanations of comparable fidelity to popular methods like SHAP and LIME but also drastically reduces the amortised computational cost of generating local explanations, making it highly suitable for real-time applications. Specifically, we show that ProToMEx is ~30-40x faster than SHAP and LIME over standardised tabular datasets and synthetic datasets.
