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dc.contributor.authorMafarja, M
dc.contributor.authorHeidari, AA
dc.contributor.authorFaris, H
dc.contributor.authorMirjalili, S
dc.contributor.authorAljarah, I
dc.contributor.editorMirjalili, Seyedali
dc.contributor.editorDong, Jin Song
dc.contributor.editorLewis, Andrew
dc.date.accessioned2020-11-05T22:20:04Z
dc.date.available2020-11-05T22:20:04Z
dc.date.issued2020
dc.identifier.isbn9783030121273
dc.identifier.doi10.1007/978-3-030-12127-3_4
dc.identifier.urihttp://hdl.handle.net/10072/399011
dc.description.abstractIn this chapter, a wrapper-based feature selection algorithm is designed and substantiated based on the binary variant of Dragonfly Algorithm (BDA). DA is a successful, well-established metaheuristic that revealed superior efficacy in dealing with various optimization problems including feature selection. In this chapter we are going first present the inspirations and methamatical modeds of DA in details. Then, the performance of this algorithm is tested on a special type of datasets that contain a huge number of features with low number of samples. This type of datasets makes the optimization process harder, because of the large search space, and the lack of adequate samples to train the model. The experimental results showed the ability of DA to deal with this type of datasets better than other optimizers in the literature. Moreover, an extensive literature review for the DA is provided in this chapter.
dc.description.peerreviewedYes
dc.publisherSpringer
dc.publisher.placeSwitzerland
dc.relation.ispartofbooktitleNature-Inspired Optimizers: Theories, Literature Reviews and Applications
dc.relation.ispartofchapter4
dc.relation.ispartofchapternumbers13
dc.relation.ispartofpagefrom47
dc.relation.ispartofpageto67
dc.relation.ispartofseriesStudies in Computational Intelligence
dc.subject.fieldofresearchInformation and Computing Sciences
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchcode08
dc.subject.fieldofresearchcode0801
dc.titleDragonfly algorithm: Theory, literature review, and application in feature selection
dc.typeBook chapter
dc.type.descriptionB1 - Chapters
dcterms.bibliographicCitationMafarja, M; Heidari, AA; Faris, H; Mirjalili, S; Aljarah, I, Dragonfly algorithm: Theory, literature review, and application in feature selection, Nature-Inspired Optimizers, 2020, pp. 47-67
dc.date.updated2020-11-05T22:14:41Z
gro.hasfulltextNo Full Text
gro.griffith.authorMirjalili, Seyedali


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