为光谱机器学习模型设计可解释框架，让化学家看得懂预测依据。
Spectral Model eXplainer: a chemically-grounded explainability framework for spectral-based machine learning models
- 基于化学意义的光谱区间，用主成分分析归纳特征
- 通过随机子样本扰动量化区间重要性，结果可直接与实测光谱对比
- 适合需要解释光谱分类决策的化学、材料领域研究者
光谱机器学习模型在化学计量学和光谱分析中应用日益广泛，预测准确性与可解释性同样重要。现有可解释人工智能（XAI）方法多源自表格或通用多元数据领域，对孤立光谱变量赋权，而非化学上合理的光谱区间。广泛使用的SHAP、PFI和VIP等工具未考虑光谱数据的物理连续性和高度共线性，其变量级输出需后处理才能还原区间信息。本研究提出光谱模型解释器（SMX），一种后验、全局、模型无关的可解释框架，通过专家定义的光谱区间解释分类模型。SMX利用主成分分析总结每个区间，定义基于分位数的逻辑谓词，通过随机子样本扰动估计谓词重要性，并以有向加权图聚合袋内排序，最终用局部可达中心性汇总。关键组件是阈值谱重建，将谓词边界反投影至原始光谱域，以自然测量单位实现与实测光谱的直接可视化对比。该方法在八个真实光谱数据集（六组基于X射线荧光-XRF，两组基于伽马射线能谱）和一个具有已知结构的合成基准上进行评估，结果表明其在解释一致性与化学合理性方面优于现有方法。
原文摘要 · Abstract (English)
Spectral-based machine learning models have been increasingly deployed in chemometrics and spectroscopy, where predictive accuracy is as important as explainability. Current employed eXplainable Artificial Intelligence (XAI) methods are largely adapted from tabular or generic multivariate domains, assigning relevance to isolated spectral variables rather than to the chemically meaningful spectral zones. Widely adopted tools such as SHapley Additive exPlanations (SHAP), Permutation Feature Importance (PFI), and Variable Importance in Projection scores (VIP) were not designed for the physical continuity and high collinearity of spectral data, and their variable-level outputs require post-hoc aggregation to recover zone-level information. This study introduces the Spectral Model eXplainer (SMX), a post-hoc, global, model-agnostic XAI framework that explains spectral classifiers through expert-informed spectral zones. SMX summarizes each zone via PCA, defines quantile-based logical predicates, estimates predicate relevance with perturbation in stochastic subsamples, and aggregates bag-wise rankings in a directed weighted graph summarized by Local Reaching Centrality. A key component is threshold spectrum reconstruction, which back-projects predicate boundaries to the original spectral domain in natural measurement units, enabling direct visual comparison with measured spectra. The method was evaluated on eight real spectral datasets (six based on X-ray Fluorescence--XRF and two based on Gamma-ray Spectrometry) and one synthetic benchmark with known gr
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