Expert-Guided Class-Conditional Goodness-of-Fit Scores for Interpretable Classification with Informative Missingness: An Application to Seismic Monitoring

arXiv stat.ML / 4/17/2026

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Key Points

The paper addresses interpretable classification under three difficulties: pervasive informative missingness, incorporating partial prior expert knowledge, and producing decision rules that humans can understand.
It introduces an expert-guided class-conditional model that turns expert knowledge into a set of small, interpretable goodness-of-fit features reflecting agreement between observed data (including missing parts) and the expert model.
These goodness-of-fit features are fed into a simple, transparent discriminative classifier to yield an inspection-friendly decision rule with explicit, justifiable components.
The framework is applied to seismic monitoring for compliance assessment with the Comprehensive Nuclear-Test-Ban Treaty, showing strong potential as a screening tool that can reduce analysts’ workload.
A simulation study suggests the proposed interpretable, expert-guided approach can outperform strong standard machine-learning classifiers, especially when labeled training data are scarce.

Abstract

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.