We present a novel approach for assessing the reliability of error-prone systems and networks using an interpretable prototype-based classification framework. Specifically, reliability levels of consecutive k-out-of-n success systems, failure networks, and domination networks are classified using Generalized Matrix Learning Vector Quantization (GMLVQ). Beyond achieving accurate classification, the proposed method provides informative insights into the influence of input probabilities on the resulting reliability levels. To further enhance the interpretability of the learned relevance matrix in GMLVQ, we introduce two graph-based visualization strategies that reveal structural patterns and relevant feature interactions that are critical for reliability-level classification. The proposed approach is generally applicable to arbitrary coherent systems and can be further adapted to estimate the probability of a union of any finite family of events based on their individual and pairwise intersection probabilities.
Dohmen et al. (Thu,) studied this question.