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dc.contributor.authorMafarja, Majdi
dc.contributor.authorAljarah, Ibrahim
dc.contributor.authorHeidari, Ali Asghar
dc.contributor.authorFaris, Hossam
dc.contributor.authorFournier-Viger, Philippe
dc.contributor.authorLi, Xiaodong
dc.contributor.authorMirjalili, Seyedali
dc.date.accessioned2019-07-04T12:41:53Z
dc.date.available2019-07-04T12:41:53Z
dc.date.issued2018
dc.identifier.issn0950-7051
dc.identifier.doi10.1016/j.knosys.2018.08.003
dc.identifier.urihttp://hdl.handle.net/10072/381979
dc.description.abstractThe Dragonfly Algorithm (DA) is a recently proposed heuristic search algorithm that was shown to have excellent performance for numerous optimization problems. In this paper, a wrapper-feature selection algorithm is proposed based on the Binary Dragonfly Algorithm (BDA). The key component of the BDA is the transfer function that maps a continuous search space to a discrete search space. In this study, eight transfer functions, categorized into two families (S-shaped and V-shaped functions) are integrated into the BDA and evaluated using eighteen benchmark datasets obtained from the UCI data repository. The main contribution of this paper is the proposal of time-varying S-shaped and V-shaped transfer functions to leverage the impact of the step vector on balancing exploration and exploitation. During the early stages of the optimization process, the probability of changing the position of an element is high, which facilitates the exploration of new solutions starting from the initial population. On the other hand, the probability of changing the position of an element becomes lower towards the end of the optimization process. This behavior is obtained by considering the current iteration number as a parameter of transfer functions. The performance of the proposed approaches is compared with that of other state-of-art approaches including the DA, binary grey wolf optimizer (bGWO), binary gravitational search algorithm (BGSA), binary bat algorithm (BBA), particle swarm optimization (PSO), and genetic algorithm in terms of classification accuracy, sensitivity, specificity, area under the curve, and number of selected attributes. Results show that the time-varying S-shaped BDA approach outperforms compared approaches.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.publisher.placeNetherlands
dc.relation.ispartofpagefrom185
dc.relation.ispartofpageto204
dc.relation.ispartofjournalKnowledge-Based Systems
dc.relation.ispartofvolume161
dc.subject.fieldofresearchInformation and Computing Sciences not elsewhere classified
dc.subject.fieldofresearchInformation and Computing Sciences
dc.subject.fieldofresearchCommerce, Management, Tourism and Services
dc.subject.fieldofresearchPsychology and Cognitive Sciences
dc.subject.fieldofresearchcode089999
dc.subject.fieldofresearchcode08
dc.subject.fieldofresearchcode15
dc.subject.fieldofresearchcode17
dc.titleBinary dragonfly optimization for feature selection using time-varying transfer functions
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© 2018 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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