{"id":{"repo_id":"cape-town","oai_identifier":"oai:open.uct.ac.za:11427/27076"},"canonical_url":"https://search.dev.ndltd.org/etd/cape-town/oai:open.uct.ac.za:11427/27076","repository":{"repo_id":"cape-town","name":"University of Cape Town","base_url":"https://open.uct.ac.za/oai/request"},"display":{"title":"Decision tree classifiers for incident call data sets","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Igboamalu, Frank Nonso"],"institution":"Department of Computer Science","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Berman, Sonia"],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017","date_published":"2017","updated_at":"2026-07-22T22:23:47Z","subjects":[],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11427/27076","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Berman, Sonia"]},{"key":"dc:creator","label":"Author","values":["Igboamalu, Frank Nonso"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2018-01-29T07:29:51Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2018-01-29T07:29:51Z"]},{"key":"dc:date.issued","label":"Date","values":["2017"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Department of Computer Science"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Cape Town"]},{"key":"dc:type","label":"Dc Type","values":["Master Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Masters"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["MSc"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/11427/27076"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:title","label":"Title","values":["Decision tree classifiers for incident call data sets"]}]}],"canonical_facts":{"dc:contributor.advisor":["Berman, Sonia"],"dc:creator":["Igboamalu, Frank Nonso"],"dc:date.accessioned":["2018-01-29T07:29:51Z"],"dc:date.available":["2018-01-29T07:29:51Z"],"dc:date.issued":["2017"],"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."],"dc:identifier.uri":["http://hdl.handle.net/11427/27076"],"dc:language.iso":["eng"],"dc:publisher.department":["Department of Computer Science"],"dc:publisher.institution":["University of Cape Town"],"dc:title":["Decision tree classifiers for incident call data sets"],"dc:type":["Master Thesis"],"dc:type.qualificationlevel":["Masters"],"dc:type.qualificationname":["MSc"]},"updated_at":"2026-07-22T22:23:47Z"}