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This paper introduces a framework for classification problems with informative missingness that leverages expert knowledge to construct interpretable goodness-of-fit features. Prior knowledge is encoded through an expert-guided class-conditional model, which is then used to generate features quantifying the agreement between observed data and the expert model, accounting for both observed and missing components. Applied to seismic monitoring for nuclear test ban treaty compliance, the method demonstrates potential as a transparent screening tool and outperforms standard machine learning classifiers, especially with small training datasets.
Expert knowledge, distilled into interpretable goodness-of-fit features, can not only improve classification accuracy in the face of informative missingness but also yield more transparent and justifiable decision rules, even outperforming standard ML methods with limited data.
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.
