Ant colony algorithm for text classification in multicore-multithread environment / Ahmad Nazmi Fadzal
In the age of wide digital usage, text classification is one of the significant prominent attribute required in order to automatically arrange emails, articles, and other textual data in an organization. Unclassified data can lead to slower data retrieval thus a reliable method is required to effect...
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| Format: | Thesis |
| Language: | English |
| Published: |
2017
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| Online Access: | https://ir.uitm.edu.my/id/eprint/18846/ |
| _version_ | 1848804625115774976 |
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| author | Fadzal, Ahmad Nazmi |
| author_facet | Fadzal, Ahmad Nazmi |
| author_sort | Fadzal, Ahmad Nazmi |
| building | UiTM Institutional Repository |
| collection | Online Access |
| description | In the age of wide digital usage, text classification is one of the significant prominent attribute required in order to automatically arrange emails, articles, and other textual data in an organization. Unclassified data can lead to slower data retrieval thus a reliable method is required to effectively retrieve data efficiently and in systematic manner. Ant Colony Optimization (ACO) is a bio-inspired technique that was introduced to solve Non-Polynomial hard problem of high text data dimension that is similar to Traveling Salesman Problem (TSP) using probabilistic way. Pheromone concept is the main criterion that distinguish ACO to other algorithms. Based on the concept, pheromone saturation is used to combine stackable solution pattern that is discovered while straying to different term node to build a path. ACO classification accuracy is compared to Genetic Algorithm classifier which also a wrapper method. On integration of the technique, ACO is proposed to work in a multicore-multithread environment to gain additional execution time advantage. In multicore-multithread environment, the adjustment aims to make artificial ants communicate across the physical core of processor. As a trade to the investment for more computing power, the execution time reduction is expected to show an improvement without compromising the original classification accuracy. The unthreaded and multicore-multithreaded version of ACO was experimented and compared in term of accuracy and execution time. It was found that the result show a positive improvement. |
| first_indexed | 2025-11-14T21:58:06Z |
| format | Thesis |
| id | uitm-18846 |
| institution | Universiti Teknologi MARA |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T21:58:06Z |
| publishDate | 2017 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uitm-188462022-11-14T02:46:28Z https://ir.uitm.edu.my/id/eprint/18846/ Ant colony algorithm for text classification in multicore-multithread environment / Ahmad Nazmi Fadzal Fadzal, Ahmad Nazmi Algorithms In the age of wide digital usage, text classification is one of the significant prominent attribute required in order to automatically arrange emails, articles, and other textual data in an organization. Unclassified data can lead to slower data retrieval thus a reliable method is required to effectively retrieve data efficiently and in systematic manner. Ant Colony Optimization (ACO) is a bio-inspired technique that was introduced to solve Non-Polynomial hard problem of high text data dimension that is similar to Traveling Salesman Problem (TSP) using probabilistic way. Pheromone concept is the main criterion that distinguish ACO to other algorithms. Based on the concept, pheromone saturation is used to combine stackable solution pattern that is discovered while straying to different term node to build a path. ACO classification accuracy is compared to Genetic Algorithm classifier which also a wrapper method. On integration of the technique, ACO is proposed to work in a multicore-multithread environment to gain additional execution time advantage. In multicore-multithread environment, the adjustment aims to make artificial ants communicate across the physical core of processor. As a trade to the investment for more computing power, the execution time reduction is expected to show an improvement without compromising the original classification accuracy. The unthreaded and multicore-multithreaded version of ACO was experimented and compared in term of accuracy and execution time. It was found that the result show a positive improvement. 2017 Thesis NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/18846/1/18846.pdf Fadzal, Ahmad Nazmi (2017) Ant colony algorithm for text classification in multicore-multithread environment / Ahmad Nazmi Fadzal. (2017) Masters thesis, thesis, Universiti Teknologi MARA. <http://terminalib.uitm.edu.my/18846.pdf> |
| spellingShingle | Algorithms Fadzal, Ahmad Nazmi Ant colony algorithm for text classification in multicore-multithread environment / Ahmad Nazmi Fadzal |
| title | Ant colony algorithm for text classification in multicore-multithread environment / Ahmad Nazmi Fadzal |
| title_full | Ant colony algorithm for text classification in multicore-multithread environment / Ahmad Nazmi Fadzal |
| title_fullStr | Ant colony algorithm for text classification in multicore-multithread environment / Ahmad Nazmi Fadzal |
| title_full_unstemmed | Ant colony algorithm for text classification in multicore-multithread environment / Ahmad Nazmi Fadzal |
| title_short | Ant colony algorithm for text classification in multicore-multithread environment / Ahmad Nazmi Fadzal |
| title_sort | ant colony algorithm for text classification in multicore-multithread environment / ahmad nazmi fadzal |
| topic | Algorithms |
| url | https://ir.uitm.edu.my/id/eprint/18846/ |