A systematic literature review on meta-heuristic based feature selection techniques for text classification

Al-shalif, Sarah Abdulkarem and Senan, Norhalina and Saeed, Faisal and Ghaban, Wad and Ibrahim, Noraini and Aamir, Muhammad and Sharif, Wareesa (2024) A systematic literature review on meta-heuristic based feature selection techniques for text classification. PeerJ Computer Science, 10. e2084. ISSN 2376-5992

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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.

Item Type: Article
Identification Number: https://doi.org/10.7717/peerj-cs.2084
3 May 2024Accepted
12 June 2024Published Online
Uncontrolled Keywords: Artificial Intelligence, Data Mining and Machine Learning, Text Mining
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions: Faculty of Computing, Engineering and the Built Environment > College of Computing
Depositing User: Gemma Tonks
Date Deposited: 26 Jun 2024 12:00
Last Modified: 26 Jun 2024 12:00
URI: https://www.open-access.bcu.ac.uk/id/eprint/15598

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