用专家知识构建可解释的地震监测分类模型，解决数据缺失问题。
Expert-Guided Class-Conditional Goodness-of-Fit Scores for Interpretable Classification with Informative Missingness: An Application to Seismic Monitoring
- 用专家先验构建类别条件模型，生成可解释拟合度特征。
- 在小样本下表现优于主流机器学习模型，减少专家工作量。
- 适合需要透明决策的高风险领域，如核禁试条约监测。
我们研究了一类存在三重挑战的分类问题：普遍存在的信息性缺失、将部分专家先验知识融入学习过程，以及对可解释决策规则的需求。提出一种框架，通过专家引导的类别条件模型编码先验知识，构建少量可解释的拟合度特征。这些特征量化观测数据与专家模型的一致性，分离出数据中已观测和缺失部分的贡献。将这些特征与少数透明辅助统计量结合，构成简单判别分类器，得到易于检查和辩护的决策规则。该框架应用于核禁试条约合规性评估中的地震监测场景。结果显示，该方法具备作为透明筛查工具的强大潜力，可显著降低专家分析师的工作负担。一项设计用于隔离该框架贡献的模拟表明，在训练样本有限时，这种可解释的专家引导方法甚至能超越强大的标准机器学习分类器。
原文摘要 · Abstract (English)
We study a classification problem with three key challenges: pervasive informative missingness, the integration of partial prior expert knowledge into the learning process, and the need for interpretable decision rules. We propose a framework that encodes prior knowledge through an expert-guided class-conditional model for one or more classes, and use this model to construct a small set of interpretable goodness-of-fit features. The features quantify how well the observed data agree with the expert model, isolating the contributions of different aspects of the data, including both observed and missing components. These features are combined with a few transparent auxiliary summaries in a simple discriminative classifier, resulting in a decision rule that is easy to inspect and justify. We develop and apply the framework in the context of seismic monitoring used to assess compliance with the Comprehensive Nuclear-Test-Ban Treaty. We show that the method has strong potential as a transparent screening tool, reducing workload for expert analysts. A simulation designed to isolate the contribution of the proposed framework shows that this interpretable expert-guided method can even outperform strong standard machine-learning classifiers, particularly when training samples are small.
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