Approaches to Multi-Objective Feature Selection: A Systematic Literature Review

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...

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Autores principales:	Qasem Al-Tashi, Said Jadid Abdulkadir, Helmi Md Rais, Seyedali Mirjalili, Hitham Alhussian
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2020
Materias:	Feature selection multi-objective optimization classification systematic literature review optimization benchmark Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/6908d1d322b44f4e977a47f73c5ee214
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id	oai:doaj.org-article:6908d1d322b44f4e977a47f73c5ee214
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spelling	oai:doaj.org-article:6908d1d322b44f4e977a47f73c5ee2142021-11-19T00:03:42ZApproaches to Multi-Objective Feature Selection: A Systematic Literature Review2169-353610.1109/ACCESS.2020.3007291https://doaj.org/article/6908d1d322b44f4e977a47f73c5ee2142020-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9133433/https://doaj.org/toc/2169-3536Feature 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.Qasem Al-TashiSaid Jadid AbdulkadirHelmi Md RaisSeyedali MirjaliliHitham AlhussianIEEEarticleFeature selectionmulti-objective optimizationclassificationsystematic literature reviewoptimizationbenchmarkElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 8, Pp 125076-125096 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Feature selection multi-objective optimization classification systematic literature review optimization benchmark Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Feature selection multi-objective optimization classification systematic literature review optimization benchmark Electrical engineering. Electronics. Nuclear engineering TK1-9971 Qasem Al-Tashi Said Jadid Abdulkadir Helmi Md Rais Seyedali Mirjalili Hitham Alhussian Approaches to Multi-Objective Feature Selection: A Systematic Literature Review
description	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.
format	article
author	Qasem Al-Tashi Said Jadid Abdulkadir Helmi Md Rais Seyedali Mirjalili Hitham Alhussian
author_facet	Qasem Al-Tashi Said Jadid Abdulkadir Helmi Md Rais Seyedali Mirjalili Hitham Alhussian
author_sort	Qasem Al-Tashi
title	Approaches to Multi-Objective Feature Selection: A Systematic Literature Review
title_short	Approaches to Multi-Objective Feature Selection: A Systematic Literature Review
title_full	Approaches to Multi-Objective Feature Selection: A Systematic Literature Review
title_fullStr	Approaches to Multi-Objective Feature Selection: A Systematic Literature Review
title_full_unstemmed	Approaches to Multi-Objective Feature Selection: A Systematic Literature Review
title_sort	approaches to multi-objective feature selection: a systematic literature review
publisher	IEEE
publishDate	2020
url	https://doaj.org/article/6908d1d322b44f4e977a47f73c5ee214
work_keys_str_mv	AT qasemaltashi approachestomultiobjectivefeatureselectionasystematicliteraturereview AT saidjadidabdulkadir approachestomultiobjectivefeatureselectionasystematicliteraturereview AT helmimdrais approachestomultiobjectivefeatureselectionasystematicliteraturereview AT seyedalimirjalili approachestomultiobjectivefeatureselectionasystematicliteraturereview AT hithamalhussian approachestomultiobjectivefeatureselectionasystematicliteraturereview
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Approaches to Multi-Objective Feature Selection: A Systematic Literature Review

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