Feature Selection for Document Classification : Case Study of Meta-heuristic Intelligence and Traditional Approaches
Doctor of Philosophy (Computer Engineering), 2020
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Prince of Songkla University
2023
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th-psu.2016-191182023-12-04T02:24:46Z Feature Selection for Document Classification : Case Study of Meta-heuristic Intelligence and Traditional Approaches Khin Sandar Kyaw Somchai Limsiroratana Faculty of Engineering Computer Engineering คณะวิศวกรรมศาสตร์ ภาควิชาวิศวกรรมคอมพิวเตอร์ Electronic publications Metaheuristics Heuristic algorithms Doctor of Philosophy (Computer Engineering), 2020 Nowadays, the culture for accessing news around the world is changed from paper to electronic format and the rate of publication for newspapers and magazines on website are increased dramatically. Meanwhile, text feature selection for the automatic document classification (ADC) is becoming a big challenge because of the unstructured nature of text feature, which is called “multi-dimension feature problem”. On the other hand, various powerful schemes dealing with text feature selection are being developed continuously nowadays, but there still exists a research gap for “optimization of feature selection problem (OFSP)”, which can be looked for the global optimal features. Meanwhile, the capacity of meta-heuristic intelligence for knowledge discovery process (KDP) is also become the critical role to overcome NP-hard problem of OFSP by providing effective performance and efficient computation time. Therefore, the idea of meta-heuristic based approach for optimization of feature selection is proposed in this research to search the global optimal features for ADC. In this thesis, case study of meta-heuristic intelligence and traditional approaches for feature selection optimization process in document classification is observed. It includes eleven meta-heuristic algorithms such as Ant Colony search, Artificial Bee Colony search, Bat search, Cuckoo search, Evolutionary search, Elephant search, Firefly search, Flower search, Genetic search, Rhinoceros search, and Wolf search, for searching the optimal feature subset for document classification. Then, the results of proposed model are compared with three traditional search algorithms like Best First search (BFS), Greedy Stepwise (GS), and Ranker search (RS). In addition, the framework of data mining is applied. It involves data preprocessing, feature engineering, building learning model and evaluating the performance of proposed meta-heuristic intelligence-based feature selection using various performance and computation complexity evaluation schemes. In data processing, tokenization, stop-words handling, stemming and lemmatizing, and normalization are applied. In feature engineering process, n-gram TF-IDF feature extraction is used for implementing feature vector and both filter and wrapper approach are applied for observing different cases. In addition, three different classifiers like J48, Naïve Bayes, and Support Vector Machine, are used for building the document classification model. According to the results, the proposed system can reduce the number of selected features dramatically that can deteriorate learning model performance. In addition, the selected global subset features can yield better performance than traditional search according to single objective function of proposed model. 2023-12-04T02:24:46Z 2023-12-04T02:24:46Z 2020 Thesis http://kb.psu.ac.th/psukb/handle/2016/19118 en Attribution-NonCommercial-NoDerivs 3.0 Thailand http://creativecommons.org/licenses/by-nc-nd/3.0/th/ application/pdf Prince of Songkla University |
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Prince of Songkhla University |
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Khunying Long Athakravi Sunthorn Learning Resources Center |
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Asia |
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Thailand Thailand |
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Khunying Long Athakravi Sunthorn Learning Resources Center |
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English |
| topic |
Electronic publications Metaheuristics Heuristic algorithms |
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Electronic publications Metaheuristics Heuristic algorithms Khin Sandar Kyaw Feature Selection for Document Classification : Case Study of Meta-heuristic Intelligence and Traditional Approaches |
| description |
Doctor of Philosophy (Computer Engineering), 2020 |
| author2 |
Somchai Limsiroratana |
| author_facet |
Somchai Limsiroratana Khin Sandar Kyaw |
| format |
Theses and Dissertations |
| author |
Khin Sandar Kyaw |
| author_sort |
Khin Sandar Kyaw |
| title |
Feature Selection for Document Classification : Case Study of Meta-heuristic Intelligence and Traditional Approaches |
| title_short |
Feature Selection for Document Classification : Case Study of Meta-heuristic Intelligence and Traditional Approaches |
| title_full |
Feature Selection for Document Classification : Case Study of Meta-heuristic Intelligence and Traditional Approaches |
| title_fullStr |
Feature Selection for Document Classification : Case Study of Meta-heuristic Intelligence and Traditional Approaches |
| title_full_unstemmed |
Feature Selection for Document Classification : Case Study of Meta-heuristic Intelligence and Traditional Approaches |
| title_sort |
feature selection for document classification : case study of meta-heuristic intelligence and traditional approaches |
| publisher |
Prince of Songkla University |
| publishDate |
2023 |
| url |
http://kb.psu.ac.th/psukb/handle/2016/19118 |
| _version_ |
1784859627422220288 |