Decision tree classifiers for incident call data sets

 

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dc.contributor.advisor Berman, Sonia en_ZA
dc.contributor.author Igboamalu, Frank Nonso en_ZA
dc.date.accessioned 2018-01-29T07:29:51Z
dc.date.available 2018-01-29T07:29:51Z
dc.date.issued 2017 en_ZA
dc.identifier.citation Igboamalu, F. 2017. Decision tree classifiers for incident call data sets. University of Cape Town. en_ZA
dc.identifier.uri http://hdl.handle.net/11427/27076
dc.description.abstract Information technology (IT) has become one of the key technologies for economic and social development in any organization. Therefore the management of Information technology incidents, and particularly in the area of resolving the problem very fast, is of concern to Information technology managers. Delays can result when incorrect subjects are assigned to Information technology incident calls: because the person sent to remedy the problem has the wrong expertise or has not brought with them the software or hardware they need to help that user. In the case study used for this work, there are no management checks in place to verify the assigning of incident description subjects. This research aims to develop a method that will tackle the problem of wrongly assigned subjects for incident descriptions. In particular, this study explores the Information technology incident calls database of an oil and gas company as a case study. The approach was to explore the Information technology incident descriptions and their assigned subjects; thereafter the correctly-assigned records were used for training decision tree classification algorithms using Waikato Environment for Knowledge Analysis (WEKA) software. Finally, the records incorrectly assigned a subject by human operators were used for testing. The J48 algorithm gave the best performance and accuracy, and was able to correctly assign subjects to 81% of the records wrongly classified by human operators. en_ZA
dc.language.iso eng en_ZA
dc.subject.other Information Technology en_ZA
dc.title Decision tree classifiers for incident call data sets en_ZA
dc.type Thesis / Dissertation en_ZA
uct.type.publication Research en_ZA
uct.type.resource Thesis en_ZA
dc.publisher.institution University of Cape Town
dc.publisher.faculty Faculty of Science en_ZA
dc.publisher.department Department of Computer Science en_ZA
dc.type.qualificationlevel Masters en_ZA
dc.type.qualificationname MSc en_ZA
uct.type.filetype Text
uct.type.filetype Image


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