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dc.contributor.authorMafarja, Majdi
dc.contributor.authorMirjalili, Seyedali
dc.date.accessioned2019-07-04T12:38:33Z
dc.date.available2019-07-04T12:38:33Z
dc.date.issued2018
dc.identifier.issn1568-4946
dc.identifier.doi10.1016/j.asoc.2017.11.006
dc.identifier.urihttp://hdl.handle.net/10072/379693
dc.description.abstractClassification accuracy highly dependents on the nature of the features in a dataset which may contain irrelevant or redundant data. The main aim of feature selection is to eliminate these types of features to enhance the classification accuracy. The wrapper feature selection model works on the feature set to reduce the number of features and improve the classification accuracy simultaneously. In this work, a new wrapper feature selection approach is proposed based on Whale Optimization Algorithm (WOA). WOA is a newly proposed algorithm that has not been systematically applied to feature selection problems yet. Two binary variants of the WOA algorithm are proposed to search the optimal feature subsets for classification purposes. In the first one, we aim to study the influence of using the Tournament and Roulette Wheel selection mechanisms instead of using a random operator in the searching process. In the second approach, crossover and mutation operators are used to enhance the exploitation of the WOA algorithm. The proposed methods are tested on standard benchmark datasets and then compared to three algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), the Ant Lion Optimizer (ALO), and five standard filter feature selection methods. The paper also considers an extensive study of the parameter setting for the proposed technique. The results show the efficiency of the proposed approaches in searching for the optimal feature subsets.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.publisher.placeNetherlands
dc.relation.ispartofpagefrom441
dc.relation.ispartofpageto453
dc.relation.ispartofjournalApplied Soft Computing
dc.relation.ispartofvolume62
dc.subject.fieldofresearchArtificial intelligence
dc.subject.fieldofresearchApplied mathematics
dc.subject.fieldofresearchNumerical and computational mathematics
dc.subject.fieldofresearchcode4602
dc.subject.fieldofresearchcode4901
dc.subject.fieldofresearchcode4903
dc.titleWhale optimization approaches for wrapper feature selection
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.hasfulltextNo Full Text
gro.griffith.authorMirjalili, Seyedali


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