Optimizing Document Classification: Unleashing the Power of Genetic Algorithms
<p dir="ltr">Many individuals, including researchers, professors, and students, encounter difficulties when searching for scholarly documents, papers, and journals within a specific domain. Consequently, scholars have begun to focus on document classification problem, offering variou...
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| مؤلفون آخرون: | , , , , , |
| منشور في: |
2023
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| _version_ | 1864513527493951488 |
|---|---|
| author | Ghulam Mustafa (458105) |
| author2 | Abid Rauf (17541708) Ahmad Sami Al-Shamayleh (17541495) Muhammad Sulaiman (9106025) Wagdi Alrawagfeh (17271664) Muhammad Tanvir Afzal (4162504) Adnan Akhunzada (3134064) |
| author2_role | author author author author author author |
| author_facet | Ghulam Mustafa (458105) Abid Rauf (17541708) Ahmad Sami Al-Shamayleh (17541495) Muhammad Sulaiman (9106025) Wagdi Alrawagfeh (17271664) Muhammad Tanvir Afzal (4162504) Adnan Akhunzada (3134064) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ghulam Mustafa (458105) Abid Rauf (17541708) Ahmad Sami Al-Shamayleh (17541495) Muhammad Sulaiman (9106025) Wagdi Alrawagfeh (17271664) Muhammad Tanvir Afzal (4162504) Adnan Akhunzada (3134064) |
| dc.date.none.fl_str_mv | 2023-07-04T06:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2023.3292248 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Optimizing_Document_Classification_Unleashing_the_Power_of_Genetic_Algorithms/25205225 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Metadata Feature extraction Bit error rate Support vector machines Genetic algorithms Classification algorithms Semantics Document classification (DC) Word2Vector (W2V) bag of word (BOW) term frequency (TF) association for computing machinery (ACM) machine learning (ML) |
| dc.title.none.fl_str_mv | Optimizing Document Classification: Unleashing the Power of Genetic Algorithms |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Many individuals, including researchers, professors, and students, encounter difficulties when searching for scholarly documents, papers, and journals within a specific domain. Consequently, scholars have begun to focus on document classification problem, offering various methods to address this issue. Researchers have utilized diverse data sources, such as citations, metadata, content, and hybrids, in their approaches.In these sources, the meta-data-based approach stands out for research paper classification due to its availability at no cost. Various scholars have employed different metadata parameters of research articles, including the title, abstract, keywords, and general terms, for research paper classification. In this study, we chose four meta-data-based features such as, title, keyword, abstract, and general terms from the SANTOS dataset, which was prepared by ACM. To represent these features numerically, we employed a semantic-based model called BERT instead of the commonly used count-based models. BERT generates a 768-dimensional vector for each record, which introduces significant time complexity during computation. Additionally, our proposed model optimizes the features using a genetic algorithm. Optimal feature selection performances a crucial role in this domain, enhancing the overall accuracy of the document classification system while reducing the time complexity associated with selecting the most relevant features from this large-dimensional space. For classification purposes, we employed GNB and SVM classifiers. The outcomes of our study exposed that the combination of title and keywords outperformed other combinations.<br></p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="http://creativecommons.org/licenses/by/4.0" target="_blank">http://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2023.3292248" target="_blank">https://dx.doi.org/10.1109/access.2023.3292248</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_4e0eba5cac140cf91b5c6affa2e4e3ab |
| identifier_str_mv | 10.1109/access.2023.3292248 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25205225 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Optimizing Document Classification: Unleashing the Power of Genetic AlgorithmsGhulam Mustafa (458105)Abid Rauf (17541708)Ahmad Sami Al-Shamayleh (17541495)Muhammad Sulaiman (9106025)Wagdi Alrawagfeh (17271664)Muhammad Tanvir Afzal (4162504)Adnan Akhunzada (3134064)EngineeringElectrical engineeringElectronics, sensors and digital hardwareMaterials engineeringMetadataFeature extractionBit error rateSupport vector machinesGenetic algorithmsClassification algorithmsSemanticsDocument classification (DC)Word2Vector (W2V)bag of word (BOW)term frequency (TF)association for computing machinery (ACM)machine learning (ML)<p dir="ltr">Many individuals, including researchers, professors, and students, encounter difficulties when searching for scholarly documents, papers, and journals within a specific domain. Consequently, scholars have begun to focus on document classification problem, offering various methods to address this issue. Researchers have utilized diverse data sources, such as citations, metadata, content, and hybrids, in their approaches.In these sources, the meta-data-based approach stands out for research paper classification due to its availability at no cost. Various scholars have employed different metadata parameters of research articles, including the title, abstract, keywords, and general terms, for research paper classification. In this study, we chose four meta-data-based features such as, title, keyword, abstract, and general terms from the SANTOS dataset, which was prepared by ACM. To represent these features numerically, we employed a semantic-based model called BERT instead of the commonly used count-based models. BERT generates a 768-dimensional vector for each record, which introduces significant time complexity during computation. Additionally, our proposed model optimizes the features using a genetic algorithm. Optimal feature selection performances a crucial role in this domain, enhancing the overall accuracy of the document classification system while reducing the time complexity associated with selecting the most relevant features from this large-dimensional space. For classification purposes, we employed GNB and SVM classifiers. The outcomes of our study exposed that the combination of title and keywords outperformed other combinations.<br></p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="http://creativecommons.org/licenses/by/4.0" target="_blank">http://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2023.3292248" target="_blank">https://dx.doi.org/10.1109/access.2023.3292248</a></p>2023-07-04T06:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2023.3292248https://figshare.com/articles/journal_contribution/Optimizing_Document_Classification_Unleashing_the_Power_of_Genetic_Algorithms/25205225CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/252052252023-07-04T06:00:00Z |
| spellingShingle | Optimizing Document Classification: Unleashing the Power of Genetic Algorithms Ghulam Mustafa (458105) Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Metadata Feature extraction Bit error rate Support vector machines Genetic algorithms Classification algorithms Semantics Document classification (DC) Word2Vector (W2V) bag of word (BOW) term frequency (TF) association for computing machinery (ACM) machine learning (ML) |
| status_str | publishedVersion |
| title | Optimizing Document Classification: Unleashing the Power of Genetic Algorithms |
| title_full | Optimizing Document Classification: Unleashing the Power of Genetic Algorithms |
| title_fullStr | Optimizing Document Classification: Unleashing the Power of Genetic Algorithms |
| title_full_unstemmed | Optimizing Document Classification: Unleashing the Power of Genetic Algorithms |
| title_short | Optimizing Document Classification: Unleashing the Power of Genetic Algorithms |
| title_sort | Optimizing Document Classification: Unleashing the Power of Genetic Algorithms |
| topic | Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Metadata Feature extraction Bit error rate Support vector machines Genetic algorithms Classification algorithms Semantics Document classification (DC) Word2Vector (W2V) bag of word (BOW) term frequency (TF) association for computing machinery (ACM) machine learning (ML) |