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Decision tree classifiers for incident call data sets
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....
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| Format: | Thesis |
| Language: | English |
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Department of Computer Science
2018
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| _version_ | 1867614460269559808 |
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| access_status_str | Open Access |
| author | Igboamalu, Frank Nonso |
| author2 | Berman, Sonia |
| author_browse | Berman, Sonia Igboamalu, Frank Nonso |
| author_facet | Berman, Sonia Igboamalu, Frank Nonso |
| author_sort | Igboamalu, Frank Nonso |
| collection | Thesis |
| description | 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. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/27076 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:52:23.707Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2018 |
| publishDateRange | 2018 |
| publishDateSort | 2018 |
| publisher | Department of Computer Science |
| publisherStr | Department of Computer Science |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/27076 Decision tree classifiers for incident call data sets Igboamalu, Frank Nonso Berman, Sonia Information Technology 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. 2018-01-29T07:29:51Z 2018-01-29T07:29:51Z 2017 Master Thesis Masters MSc http://hdl.handle.net/11427/27076 eng application/pdf Department of Computer Science Faculty of Science University of Cape Town |
| spellingShingle | Information Technology Igboamalu, Frank Nonso Decision tree classifiers for incident call data sets |
| thesis_degree_str | Master's |
| title | Decision tree classifiers for incident call data sets |
| title_full | Decision tree classifiers for incident call data sets |
| title_fullStr | Decision tree classifiers for incident call data sets |
| title_full_unstemmed | Decision tree classifiers for incident call data sets |
| title_short | Decision tree classifiers for incident call data sets |
| title_sort | decision tree classifiers for incident call data sets |
| topic | Information Technology |
| url | http://hdl.handle.net/11427/27076 |
| work_keys_str_mv | AT igboamalufranknonso decisiontreeclassifiersforincidentcalldatasets |