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dc.contributor.authorOjuka, Nelson
dc.date.accessioned2012-10-03T11:30:26Z
dc.date.available2012-10-03T11:30:26Z
dc.date.issued2009-10
dc.identifier.citationOjuka, N. (2009). Detection of subscription fraud in telecommunications using decision tree learning. Unpublished master's thesis, Makerere University, Kampala, Uganda.en_US
dc.identifier.urihttp://hdl.handle.net/10570/765
dc.descriptionA Project report submitted to the School of Graduate Studies in partial fulfillment of the requirements for the award of a Master of Science in Computer Science Degree of Makerere University.en_US
dc.description.abstractSubscription fraud and bad debts are the major causes of loss of revenue in the telecommunication industry. This research project therefore, focused on designing a subscription fraud detection system with minimum false positive alerts using decision tree learning. The system has been trained to learn from training data and used decision tree algorithms to make classifications/predictions on future telecommunication data. Weka software was used to induce the resulting decision tree from the training data. The resulting decision tree was pruned and the pruned tree converted to rules which was implemented using Hypertext preprocessed (PHP) programming language, and the result was high detection rate with false positive rate kept very low (about 0.5005%).en_US
dc.language.isoenen_US
dc.subjectSubscription frauden_US
dc.subjectDecision support toolen_US
dc.subjectTelecommunication industryen_US
dc.subjectRevenue lossen_US
dc.titleDetection of subscription fraud in telecommunications using decision tree learningen_US
dc.typeThesis, mastersen_US


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