A systematic literature review on meta-heuristic based feature selection techniques for text classification
Feature selection (FS) is a critical step in many data science-based applications, especially in text classification, as it includes selecting relevant and important features from an original feature set. This process can improve learning accuracy, streamline learning duration, and simplify outcomes...
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ftdoajarticles:oai:doaj.org/article:0595860f0afd4547b5bf427fade1f504 2024-09-15T18:32:23+00:00 A systematic literature review on meta-heuristic based feature selection techniques for text classification Sarah Abdulkarem Al-shalif Norhalina Senan Faisal Saeed Wad Ghaban Noraini Ibrahim Muhammad Aamir Wareesa Sharif 2024-06-01T00:00:00Z https://doi.org/10.7717/peerj-cs.2084 https://doaj.org/article/0595860f0afd4547b5bf427fade1f504 EN eng PeerJ Inc. https://peerj.com/articles/cs-2084.pdf https://peerj.com/articles/cs-2084/ https://doaj.org/toc/2376-5992 doi:10.7717/peerj-cs.2084 2376-5992 https://doaj.org/article/0595860f0afd4547b5bf427fade1f504 PeerJ Computer Science, Vol 10, p e2084 (2024) Feature selection Meta-heuristic techniques Text classification Dimensionally reduction Evolutionary algorithms Ringed seal search Electronic computers. Computer science QA75.5-76.95 article 2024 ftdoajarticles https://doi.org/10.7717/peerj-cs.2084 2024-08-05T17:49:12Z Feature selection (FS) is a critical step in many data science-based applications, especially in text classification, as it includes selecting relevant and important features from an original feature set. This process can improve learning accuracy, streamline learning duration, and simplify outcomes. In text classification, there are often many excessive and unrelated features that impact performance of the applied classifiers, and various techniques have been suggested to tackle this problem, categorized as traditional techniques and meta-heuristic (MH) techniques. In order to discover the optimal subset of features, FS processes require a search strategy, and MH techniques use various strategies to strike a balance between exploration and exploitation. The goal of this research article is to systematically analyze the MH techniques used for FS between 2015 and 2022, focusing on 108 primary studies from three different databases such as Scopus, Science Direct, and Google Scholar to identify the techniques used, as well as their strengths and weaknesses. The findings indicate that MH techniques are efficient and outperform traditional techniques, with the potential for further exploration of MH techniques such as Ringed Seal Search (RSS) to improve FS in several applications. Article in Journal/Newspaper ringed seal Directory of Open Access Journals: DOAJ Articles PeerJ Computer Science 10 e2084 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Feature selection Meta-heuristic techniques Text classification Dimensionally reduction Evolutionary algorithms Ringed seal search Electronic computers. Computer science QA75.5-76.95 |
spellingShingle |
Feature selection Meta-heuristic techniques Text classification Dimensionally reduction Evolutionary algorithms Ringed seal search Electronic computers. Computer science QA75.5-76.95 Sarah Abdulkarem Al-shalif Norhalina Senan Faisal Saeed Wad Ghaban Noraini Ibrahim Muhammad Aamir Wareesa Sharif A systematic literature review on meta-heuristic based feature selection techniques for text classification |
topic_facet |
Feature selection Meta-heuristic techniques Text classification Dimensionally reduction Evolutionary algorithms Ringed seal search Electronic computers. Computer science QA75.5-76.95 |
description |
Feature selection (FS) is a critical step in many data science-based applications, especially in text classification, as it includes selecting relevant and important features from an original feature set. This process can improve learning accuracy, streamline learning duration, and simplify outcomes. In text classification, there are often many excessive and unrelated features that impact performance of the applied classifiers, and various techniques have been suggested to tackle this problem, categorized as traditional techniques and meta-heuristic (MH) techniques. In order to discover the optimal subset of features, FS processes require a search strategy, and MH techniques use various strategies to strike a balance between exploration and exploitation. The goal of this research article is to systematically analyze the MH techniques used for FS between 2015 and 2022, focusing on 108 primary studies from three different databases such as Scopus, Science Direct, and Google Scholar to identify the techniques used, as well as their strengths and weaknesses. The findings indicate that MH techniques are efficient and outperform traditional techniques, with the potential for further exploration of MH techniques such as Ringed Seal Search (RSS) to improve FS in several applications. |
format |
Article in Journal/Newspaper |
author |
Sarah Abdulkarem Al-shalif Norhalina Senan Faisal Saeed Wad Ghaban Noraini Ibrahim Muhammad Aamir Wareesa Sharif |
author_facet |
Sarah Abdulkarem Al-shalif Norhalina Senan Faisal Saeed Wad Ghaban Noraini Ibrahim Muhammad Aamir Wareesa Sharif |
author_sort |
Sarah Abdulkarem Al-shalif |
title |
A systematic literature review on meta-heuristic based feature selection techniques for text classification |
title_short |
A systematic literature review on meta-heuristic based feature selection techniques for text classification |
title_full |
A systematic literature review on meta-heuristic based feature selection techniques for text classification |
title_fullStr |
A systematic literature review on meta-heuristic based feature selection techniques for text classification |
title_full_unstemmed |
A systematic literature review on meta-heuristic based feature selection techniques for text classification |
title_sort |
systematic literature review on meta-heuristic based feature selection techniques for text classification |
publisher |
PeerJ Inc. |
publishDate |
2024 |
url |
https://doi.org/10.7717/peerj-cs.2084 https://doaj.org/article/0595860f0afd4547b5bf427fade1f504 |
genre |
ringed seal |
genre_facet |
ringed seal |
op_source |
PeerJ Computer Science, Vol 10, p e2084 (2024) |
op_relation |
https://peerj.com/articles/cs-2084.pdf https://peerj.com/articles/cs-2084/ https://doaj.org/toc/2376-5992 doi:10.7717/peerj-cs.2084 2376-5992 https://doaj.org/article/0595860f0afd4547b5bf427fade1f504 |
op_doi |
https://doi.org/10.7717/peerj-cs.2084 |
container_title |
PeerJ Computer Science |
container_volume |
10 |
container_start_page |
e2084 |
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1810474112497221632 |