Machine Learning for Classifying Cellular Traffic

Typ
Examensarbete för masterexamen
Master Thesis
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Publicerad
2017
Författare
Frölich, Isabelle
Modellbyggare
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Sammanfattning
Today’s cellular network is ever growing, making the need for a mechanism that can identify overloads greater each day. In this report a design science research is conducted showcasing the possibilities to use the classification machine learning algorithm naive Bayes to identify signaling overloads in a cellular network node. The research shows that naive Bayes can be used to successfully identify the greater majority of the possible overloads that could occur in a cellular node.
Beskrivning
Ämne/nyckelord
Data- och informationsvetenskap, Computer and Information Science
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