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.