  The active growth and dynamic nature of cellular networks makes network
troubleshooting challenging. Identification of network problems leveraging on
machine learning has gained a lot of visibility in the past few years,
resulting in dramatically improved cellular network services. In this paper, we
present a novel methodology to automate the fault identification process in a
cellular network and to classify network anomalies, which combines supervised
and unsupervised machine learning algorithms. Our experiments using real data
from operational commercial mobile networks obtained through drive-test
measurements as well as via the MONROE platform show that our method can
automatically identify and classify networking anomalies, thus enabling timely
and precise troubleshooting actions.
