Network reliability assessment through empirical models using a machine learning approach

The Machine Learning (ML) paradigm offers an interesting approach for assessing various aspects related to the reliability of any system that can be represented as a network. The main idea is to employ a specific ML technique, trained on a restricted subset of data, to produce an estimate of the Structure Function. In this chapter, three ML techniques (Support Vector Machines, Decision Trees and Shadow Clustering) are presented in detail and their behavior is carefully examined through different applications involving: reliability evaluation, reconstruction of approximate reliability expressions and determination of cut and path sets.