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dc.contributor.authorTaradeh, Mohammad
dc.contributor.authorMafarja, Majdi
dc.contributor.authorHeidari, Ali Asghar
dc.contributor.authorFaris, Hossam
dc.contributor.authorAljarah, Ibrahim
dc.contributor.authorMirjalili, Seyedali
dc.contributor.authorFujita, Hamido
dc.date.accessioned2019-06-19T13:10:11Z
dc.date.available2019-06-19T13:10:11Z
dc.date.issued2019
dc.identifier.issn0020-0255
dc.identifier.doi10.1016/j.ins.2019.05.038
dc.identifier.urihttp://hdl.handle.net/10072/385482
dc.description.abstractWith recent advancements in data collection tools and the widespread use of intelligent information systems, a huge amount of data streams with lots of redundant, irrelevant, and noisy features are collected and a large number of features (attributes) should be processed. Therefore, there is a growing demand for developing efficient Feature Selection (FS) techniques. Gravitational Search algorithm (GSA) is a successful population-based metaheuristic inspired by Newton’s law of gravity. In this research, a novel GSA-based algorithm with evolutionary crossover and mutation operators is proposed to deal with feature selection (FS) tasks. As an NP-hard problem, FS finds an optimal subset of features from a given set. For the proposed wrapper FS method, both K-Nearest Neighbors (KNN) and Decision Tree (DT) classifiers are used as evaluators. Eighteen well-known UCI datasets are utilized to assess the performance of the proposed approaches. In order to verify the efficiency of proposed algorithms, the results are compared with some popular nature-inspired algorithms (i.e. Genetic Algorithm (GA), Particle Swarm Optimizer (PSO), and Grey Wolf Optimizer (GWO)). The extensive results and comparisons demonstrate the superiority of the proposed algorithm in solving FS problems.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofpagefrom219
dc.relation.ispartofpageto239
dc.relation.ispartofjournalInformation Sciences
dc.relation.ispartofvolume497
dc.subject.fieldofresearchMathematical sciences
dc.subject.fieldofresearchEngineering
dc.subject.fieldofresearchcode49
dc.subject.fieldofresearchcode40
dc.titleAn evolutionary gravitational search-based feature selection
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dcterms.licensehttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2019 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorMirjalili, Seyedali


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