Predicting Network Faults using Random Forest and C5.0
About this article
DOI:
https://doi.org/10.14419/ijet.v7i2.14.11164Keywords:
Broadband Network, C5.0 Decision Tree, Network Fault Prediction, Random Forest, TelecommunicationAbstract
The Internet is an enabling technology that assists daily and business activities. However, a network fault could prevent the user from accessing the internet thus creating trouble tickets. Ideally, accurate prediction prior to network fault allows the telco to respond before the customer raises a trouble ticket. Current research focuses on forecasting the quantity of trouble ticket using historical trouble ticket. To improve the prediction of network fault, the customer trouble ticket data is augmented to include internet usage data and signal measurement data. Random Forest (RF) and C5.0 Decision Tree algorithms are used to derive predictive models. Experiment results reveal that RF shows higher AUC score as compared to C5.0 Decision Tree. RF is able to identify the important features while C5.0 Decision Tree is able to list decision rules that describe the relation among selected features.
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