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dc.contributor.authorMafarja, Majdi
dc.contributor.authorJaber, Iyad
dc.contributor.authorEleyan, Derar
dc.contributor.authorHammouri, Abdelaziz
dc.contributor.authorMirjalili, Seyedali
dc.description.abstractWrapper feature selection methods aim to reduce the number of features from the original feature set to and improve the classification accuracy simultaneously. In this paper, a wrapper-feature selection algorithm based on the binary dragonfly algorithm is proposed. Dragonfly algorithm is a recent swarm intelligence algorithm that mimics the behavior of the dragonflies. Eighteen UCI datasets are used to evaluate the performance of the proposed approach. The results of the proposed method are compared with those of Particle Swarm Optimization (PSO), Genetic Algorithms (GAs) in terms of classification accuracy and number of selected attributes. The results show the ability of Binary Dragonfly Algorithm (BDA) in searching the feature space and selecting the most informative features for classification tasks.en_US
dc.publisherInternational Conference on new Trends in Computing Sciences (2017 : Amman, JO)en_US
dc.subjectArtificial intelligenceen_US
dc.subjectData miningen_US
dc.subjectComputational intelligenceen_US
dc.subjectMachine learningen_US
dc.subjectSelection theoremsen_US
dc.subjectRough setsen_US
dc.subjectMathematical optimization
dc.subjectBinary dragonfly algorithm
dc.subject.lcshSeparation of variables
dc.titleBinary dragonfly algorithm for feature selectionen_US
dc.typeConference Proceedingsen_US
newfileds.departmentEngineering and Technologyen_US
newfileds.general-subjectComputers and Information Technology | الحاسوب وتكنولوجيا المعلوماتen_US
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