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APIC: A method for automated pattern identification and classification
Machine Learning (ML) is a transformative technology at the forefront of many modern research endeavours. The technology is generating a tremendous amount of attention from researchers and practitioners, providing new approaches to solving complex classification and regression tasks. While concepts...
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| Format: | Thesis |
| Language: | English |
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Department of Computer Science
2018
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| _version_ | 1867613180171124736 |
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| access_status_str | Open Access |
| author | Goss, Ryan Gavin |
| author2 | Nitschke, Geoff Stuart |
| author_browse | Goss, Ryan Gavin Nitschke, Geoff Stuart |
| author_facet | Nitschke, Geoff Stuart Goss, Ryan Gavin |
| author_sort | Goss, Ryan Gavin |
| collection | Thesis |
| description | Machine Learning (ML) is a transformative technology at the forefront of many modern research endeavours. The technology is generating a tremendous amount of attention from researchers and practitioners, providing new approaches to solving complex classification and regression tasks. While concepts such as Deep Learning have existed for many years, the computational power for realising the utility of these algorithms in real-world applications has only recently become available. This dissertation investigated the efficacy of a novel, general method for deploying ML in a variety of complex tasks, where best feature selection, data-set labelling, model definition and training processes were determined automatically. Models were developed in an iterative fashion, evaluated using both training and validation data sets. The proposed method was evaluated using three distinct case studies, describing complex classification tasks often requiring significant input from human experts. The results achieved demonstrate that the proposed method compares with, and often outperforms, less general, comparable methods designed specifically for each task. Feature selection, data-set annotation, model design and training processes were optimised by the method, where less complex, comparatively accurate classifiers with lower dependency on computational power and human expert intervention were produced. In chapter 4, the proposed method demonstrated improved efficacy over comparable systems, automatically identifying and classifying complex application protocols traversing IP networks. In chapter 5, the proposed method was able to discriminate between normal and anomalous traffic, maintaining accuracy in excess of 99%, while reducing false alarms to a mere 0.08%. Finally, in chapter 6, the proposed method discovered more optimal classifiers than those implemented by comparable methods, with classification scores rivalling those achieved by state-of-the-art systems. The findings of this research concluded that developing a fully automated, general method, exhibiting efficacy in a wide variety of complex classification tasks with minimal expert intervention, was possible. The method and various artefacts produced in each case study of this dissertation are thus significant contributions to the field of ML. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/27025 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:32:00.945Z |
| 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:11427/27025 APIC: A method for automated pattern identification and classification Goss, Ryan Gavin Nitschke, Geoff Stuart Pattern Recognition Machine Learning Machine Learning (ML) is a transformative technology at the forefront of many modern research endeavours. The technology is generating a tremendous amount of attention from researchers and practitioners, providing new approaches to solving complex classification and regression tasks. While concepts such as Deep Learning have existed for many years, the computational power for realising the utility of these algorithms in real-world applications has only recently become available. This dissertation investigated the efficacy of a novel, general method for deploying ML in a variety of complex tasks, where best feature selection, data-set labelling, model definition and training processes were determined automatically. Models were developed in an iterative fashion, evaluated using both training and validation data sets. The proposed method was evaluated using three distinct case studies, describing complex classification tasks often requiring significant input from human experts. The results achieved demonstrate that the proposed method compares with, and often outperforms, less general, comparable methods designed specifically for each task. Feature selection, data-set annotation, model design and training processes were optimised by the method, where less complex, comparatively accurate classifiers with lower dependency on computational power and human expert intervention were produced. In chapter 4, the proposed method demonstrated improved efficacy over comparable systems, automatically identifying and classifying complex application protocols traversing IP networks. In chapter 5, the proposed method was able to discriminate between normal and anomalous traffic, maintaining accuracy in excess of 99%, while reducing false alarms to a mere 0.08%. Finally, in chapter 6, the proposed method discovered more optimal classifiers than those implemented by comparable methods, with classification scores rivalling those achieved by state-of-the-art systems. The findings of this research concluded that developing a fully automated, general method, exhibiting efficacy in a wide variety of complex classification tasks with minimal expert intervention, was possible. The method and various artefacts produced in each case study of this dissertation are thus significant contributions to the field of ML. 2018-01-25T14:10:52Z 2018-01-25T14:10:52Z 2017 Doctoral Thesis Doctoral PhD http://hdl.handle.net/11427/27025 eng application/pdf Department of Computer Science Faculty of Science University of Cape Town |
| spellingShingle | Pattern Recognition Machine Learning Goss, Ryan Gavin APIC: A method for automated pattern identification and classification |
| thesis_degree_str | Doctoral |
| title | APIC: A method for automated pattern identification and classification |
| title_full | APIC: A method for automated pattern identification and classification |
| title_fullStr | APIC: A method for automated pattern identification and classification |
| title_full_unstemmed | APIC: A method for automated pattern identification and classification |
| title_short | APIC: A method for automated pattern identification and classification |
| title_sort | apic a method for automated pattern identification and classification |
| topic | Pattern Recognition Machine Learning |
| url | http://hdl.handle.net/11427/27025 |
| work_keys_str_mv | AT gossryangavin apicamethodforautomatedpatternidentificationandclassification |