Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11889/5313
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dc.contributor.authorMafarja, Majdi
dc.contributor.authorMirjalili, Seyedali
dc.date.accessioned2018-01-09T06:12:39Z
dc.date.available2018-01-09T06:12:39Z
dc.date.issued2017-11-01
dc.identifier.citationMajdi Mafarja, Seyedali Mirjalili, Whale optimization approaches for wrapper feature selection, In Applied Soft Computing, Volume 62, 2018, Pages 441-453, ISSN 1568-4946, https://doi.org/10.1016/j.asoc.2017.11.006.en_US
dc.identifier.urihttp://hdl.handle.net/20.500.11889/5313
dc.description• Novel Whale Optimization approaches are proposed for feature Selection. • Crossover and mutation are used to enhance the exploitation property in WOA algorithm. • Tournament selection is used to enhance the exploration in WOA algorithm. • A superior performance of the proposed approaches is proved in the experiments.en_US
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.en_US
dc.language.isoen_USen_US
dc.publisherElsevieren_US
dc.subjectAlgorithmsen_US
dc.subjectProblem solvingen_US
dc.subjectComputational intelligenceen_US
dc.subjectMathematical optimizationen_US
dc.subjectSeparation of variables
dc.subject.lcshMachine learning
dc.subject.lcshArtificial intelligence
dc.titleWhale optimization approaches for wrapper feature selectionen_US
dc.typeArticleen_US
newfileds.departmentEngineering and Technologyen_US
newfileds.item-access-typeopen_accessen_US
newfileds.thesis-prognoneen_US
newfileds.general-subjectComputers and Information Technology | الحاسوب وتكنولوجيا المعلوماتen_US
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