Applying data mining techniques for predicting telecommunication service faults: the case of Ethio-telecom
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Date
2013-10
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Addis Ababa University
Abstract
Faults are inevitable in telecommunication services, therefore predicting them ahead of
time is crucial to make the systems more robust and the operation more reliable. Faults
in telecommunication services have direct impact on its availability and maintenance
costs, so their quick elimination, prevention and removal of causes that generated them,
is of special interest.
This study is aimed at applying data mining techniques to support prediction of broad
band network service faults at Ethio-telecom. The subject of this study is from Ethio telecom's
Z-Smart Trouble Ticket system, which contains customer's service fault report
information and remarks given by experts about the actual fault reasons after the
problems solved.
In the data mining process, the first step was collecting the target data from the above
mentioned system at Ethio-telecom. Then various types of preprocessing tasks were
performed on the collected data so that to make the data ready for the planned data
mining tasks. On the model building phase. (4.5 variant of decision tree and Naive
byes of Bayesian network algorithms were applied for building the classifiers and
accuracy results obtained using J48 and naïve byes was 74•06% and 69% respectively.
Due to the data set imbalance observed on the class variables, SMOTE minority over
sampling technique with J48 algorithm was applied and it improves the classifier
accuracy to 77•90%.
The results from this study were encouraging, which strengthened the belief that
applying data mining techniques could in fact support network service faults prediction
activity at Ethio telecom. In the future, using a balanced data set, and incorporating
more attributes and also by testing with various classification algorithms better
classifier accuracy could be obtained
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Keywords
Data Mining, Classification, Prediction, Telecommunication, Network, Faults.