🤖 AI Summary
This study addresses classification problems commonly characterized by missing data, the need to incorporate expert prior knowledge, and demands for interpretable decisions. The authors propose an expert-guided class-conditional modeling approach that constructs interpretable goodness-of-fit features to quantify the consistency between incomplete observations and expert-derived models. By integrating these features with a small set of transparent summary statistics, they design a lightweight yet effective discriminative classifier. This method embeds domain knowledge directly into the class-conditional generative process, achieving substantially improved classification performance under limited sample sizes. Evaluated on seismic monitoring tasks, the system functions as a transparent screening tool that effectively reduces expert workload and outperforms mainstream machine learning methods.
📝 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.