eprintid: 13804
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/38/04
datestamp: 2023-11-10 03:28:22
lastmod: 2023-11-10 03:28:22
status_changed: 2023-11-10 01:52:02
type: article
metadata_visibility: show
creators_name: Al-Tashi, Q.
creators_name: Abdulkadir, S.J.
creators_name: Rais, H.M.
creators_name: Mirjalili, S.
creators_name: Alhussian, H.
title: Approaches to Multi-Objective Feature Selection: A Systematic Literature Review
ispublished: pub
keywords: Data mining; Machine learning, Feature selection problem; Multi objective; Research domains; Systematic literature review, Feature extraction
note: cited By 87
abstract: Feature selection has gained much consideration from scholars working in the domain of machine learning and data mining in recent years. Feature selection is a popular problem in Machine learning with the goal of finding optimal features with increase accuracy. As a result, several studies have been conducted on multi-objective feature selection through numerous multi-objective techniques and algorithms. The objective of this paper is to present a systematic literature review of the challenges and issues of the multi-objective feature selection problem and critically analyses the proposed techniques used to tackle this problem. The conducted review covered all related studies published since 2012 up to 2019. The outcomes of the reviewed of these studies clearly showed that no perfect solution to the multi-objective feature selection problem yet. The authors believed that the conducted review would serve as the main source of the techniques and methods used to resolve the problem of multi-objective feature selection. Furthermore, current challenges and issues are deliberated to find promising research domains for further study. © 2013 IEEE.
date: 2020
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088709353&doi=10.1109%2fACCESS.2020.3007291&partnerID=40&md5=98b32a22bb18ababab42888edc9ebd71
id_number: 10.1109/ACCESS.2020.3007291
full_text_status: none
publication: IEEE Access
volume: 8
pagerange: 125076-125096
refereed: TRUE
issn: 21693536
citation:   Al-Tashi, Q. and Abdulkadir, S.J. and Rais, H.M. and Mirjalili, S. and Alhussian, H.  (2020) Approaches to Multi-Objective Feature Selection: A Systematic Literature Review.  IEEE Access, 8.  pp. 125076-125096.  ISSN 21693536