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...

Full description

Bibliographic Details
Published in:PeerJ Computer Science
Main Authors: Al-shalif, Sarah Abdulkarem, Senan, Norhalina, Saeed, Faisal, Ghaban, Wad, Ibrahim, Noraini, Aamir, Muhammad, Sharif, Wareesa
Other Authors: Research Management Center at Universiti Teknologi Malaysia, Data Analytics and Artificial Intelligence (DAAI) Research Group in Birmingham City University, UK
Format: Article in Journal/Newspaper
Language:English
Published: PeerJ 2024
Subjects:
ringed seal
Online Access:http://dx.doi.org/10.7717/peerj-cs.2084
https://peerj.com/articles/cs-2084.pdf
https://peerj.com/articles/cs-2084.xml
https://peerj.com/articles/cs-2084.html
Description
Summary: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.

Similar Items