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

Sarah Abdulkarem Al-shalif (Corresponding / Lead Author), Norhalina Senan, Faisal Saeed, Wad Ghaban, Noraini Ibrahim, Muhammad Aamir, Wareesa Sharif

    Research output: Contribution to journalArticlepeer-review


    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.
    Original languageEnglish
    Pages (from-to)1-45
    Number of pages45
    JournalPeerj Computer Science
    Issue number2024
    Publication statusPublished (VoR) - 12 Jun 2024


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