Hybrid efficient genetic algorithm for big data feature selection problems

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dc.contributor.authorMohammed, Tareq Abed
dc.contributor.authorBayat, Oğuz
dc.contributor.authorUçan, Osman Nuri
dc.contributor.authorAlhayali, Shaymaa
dc.date.accessioned2021-05-15T11:33:23Z
dc.date.available2021-05-15T11:33:23Z
dc.date.issued2020
dc.departmentMühendislik ve Doğa Bilimleri Fakültesi, Elektrik - Elektronik Mühendisliği Bölümüen_US
dc.description.abstractDue to the huge amount of data being generating from different sources, the analyzing and extracting of useful information from these data becomes a very complex task. The difficulty of dealing with big data optimization problems comes from many factors such as the high number of features, and the existing of lost data. The feature selection process becomes an important step in many data mining and machine learning algorithms to reduce the dimensionality of the optimization problems and increase the performance of the classification or clustering algorithms. In this paper, a set of hybrid and efficient genetic algorithms are proposed to solve feature selection problem, when the handled data has a large feature size. The proposed algorithms use a new gene-weighted mechanism that can adaptively classify the features into strong relative features, weak or redundant features, and unstable features during the evolution of the algorithm. Based on this classification, the proposed algorithm gives the strong features high priority and the weak features less priority when generating new candidate solutions. In the same time, the proposed algorithm tries to more concentrate on unstable features that sometimes appear and sometimes disappear from the best solutions of the population. The performance of proposed algorithms is investigated by using different datasets and feature selection algorithms. The results show that our proposed algorithms can outperform the other feature selection algorithms and effectively enhance the classification performance over the tested datasets.en_US
dc.identifier.doi10.1007/s10699-019-09588-6
dc.identifier.endpage1025en_US
dc.identifier.issn1233-1821
dc.identifier.issn1572-8471
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85062727063
dc.identifier.scopusqualityQ1
dc.identifier.startpage1009en_US
dc.identifier.urihttps://doi.org/10.1007/s10699-019-09588-6
dc.identifier.urihttps://hdl.handle.net/20.500.12939/146
dc.identifier.volume25en_US
dc.identifier.wosWOS:000590044200008
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorUçan, Osman Nuri
dc.institutionauthorBayat, Oğuz
dc.institutionauthorAlhayali, Shaymaa
dc.language.isoen
dc.publisherSpringeren_US
dc.relation.ispartofFoundations of Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFeature Selectionen_US
dc.subjectEvolutionary Algorithmsen_US
dc.subjectBig Data Analyzingen_US
dc.subjectArtificial Neural Networksen_US
dc.titleHybrid efficient genetic algorithm for big data feature selection problems
dc.typeArticle

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WoS İndeksli Yayınlar Koleksiyonu
Elektrik - Elektronik Mühendisliği Bölümü Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu