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

2026-04-16 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Environmental Science & Earth Systems · Depth: Expert, quick

Summary

A new classification framework addresses challenges in pervasive informative missingness, integration of partial prior expert knowledge, and the need for interpretable decision rules. The framework encodes prior knowledge via an expert-guided class-conditional model for specific classes, generating a small set of interpretable goodness-of-fit features. These features quantify data agreement with the expert model, isolating contributions from observed and missing data components. Combined with transparent auxiliary summaries, these features feed into a simple discriminative classifier, yielding an easily inspectable decision rule. Applied to seismic monitoring for Comprehensive Nuclear-Test-Ban Treaty compliance, the method shows potential as a transparent screening tool, reducing expert analyst workload. Simulations indicate this interpretable, expert-guided approach can outperform standard machine learning classifiers, especially with limited training samples.

Key takeaway

For Machine Learning Engineers developing classification systems with significant missing data or requiring high interpretability, this framework offers a robust alternative. Your team should consider integrating expert-guided class-conditional models to generate interpretable features, potentially outperforming complex models and reducing expert workload, particularly when training data is scarce.

Key insights

Expert-guided, interpretable classification can outperform standard ML, especially with informative missingness and small datasets.

Principles

• Integrate expert knowledge into class-conditional models.
• Quantify data agreement with expert models via goodness-of-fit features.
• Combine interpretable features with simple classifiers for transparency.

Method

Encode prior knowledge into an expert-guided class-conditional model, generate goodness-of-fit features, and combine these with auxiliary summaries for a simple discriminative classifier.

In practice

• Apply to seismic monitoring for treaty compliance.
• Use as a transparent screening tool.
• Consider for small training sample scenarios.

Topics

• Expert-Guided Classification
• Informative Missingness
• Goodness-of-Fit Scores
• Interpretable Classification
• Seismic Monitoring

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.