Decision tree classifiers for incident call data sets

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dc.contributor.advisorBerman, Soniaen_ZA
dc.contributor.authorIgboamalu, Frank Nonsoen_ZA
dc.date.accessioned2018-01-29T07:29:51Z
dc.date.available2018-01-29T07:29:51Z
dc.date.issued2017en_ZA
dc.description.abstractInformation 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.identifier.apacitationIgboamalu, F. N. (2017). <i>Decision tree classifiers for incident call data sets</i>. (Thesis). University of Cape Town ,Faculty of Science ,Department of Computer Science. Retrieved from http://hdl.handle.net/11427/27076en_ZA
dc.identifier.chicagocitationIgboamalu, Frank Nonso. <i>"Decision tree classifiers for incident call data sets."</i> Thesis., University of Cape Town ,Faculty of Science ,Department of Computer Science, 2017. http://hdl.handle.net/11427/27076en_ZA
dc.identifier.citationIgboamalu, F. 2017. Decision tree classifiers for incident call data sets. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Igboamalu, Frank Nonso AB - 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. DA - 2017 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2017 T1 - Decision tree classifiers for incident call data sets TI - Decision tree classifiers for incident call data sets UR - http://hdl.handle.net/11427/27076 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/27076
dc.identifier.vancouvercitationIgboamalu FN. Decision tree classifiers for incident call data sets. [Thesis]. University of Cape Town ,Faculty of Science ,Department of Computer Science, 2017 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/27076en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Computer Scienceen_ZA
dc.publisher.facultyFaculty of Scienceen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherInformation Technologyen_ZA
dc.titleDecision tree classifiers for incident call data setsen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMScen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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