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dc.contributor.authorMafarja, Majdi
dc.contributor.authorMirjalili, Seyedali
dc.identifier.citationMajdi M. Mafarja, Seyedali Mirjalili, Hybrid Whale Optimization Algorithm with simulated annealing for feature selection, In Neurocomputing, Volume 260, 2017, Pages 302-312, ISSN 0925-2312,
dc.description• Four hybrid feature selection methods for classification task are proposed. • Our hybrid method combines Whale Optimization Algorithm with simulated annealing. • Eighteen UCI datasets were used in the experiments. • Our approaches result a higher accuracy by using less number of features.en_US
dc.description.abstractHybrid metaheuristics are of the most interesting recent trends in optimization and memetic algorithms. In this paper, two hybridization models are used to design different feature selection techniques based on Whale Optimization Algorithm (WOA). In the first model, Simulated Annealing (SA) algorithm is embedded in WOA algorithm, while it is used to improve the best solution found after each iteration of WOA algorithm in the second model. The goal of using SA here is to enhance the exploitation by searching the most promising regions located by WOA algorithm. The performance of the proposed approaches is evaluated on 18 standard benchmark datasets from UCI repository and compared with three well-known wrapper feature selection methods in the literature. The experimental results confirm the efficiency of the proposed approaches in improving the classification accuracy compared to other wrapper-based algorithms, which insures the ability of WOA algorithm in searching the feature space and selecting the most informative attributes for classification tasks.en_US
dc.subjectMathematical optimizationen_US
dc.subjectOperations researchen_US
dc.subjectData miningen_US
dc.subjectSimulated annealing (Mathematics)en_US
dc.subjectArtificial intelligenceen_US
dc.subjectDatabase managementen_US
dc.subjectSeparation of variables
dc.subjectWhale optimization algorithm
dc.titleHybrid whale optimization algorithm with simulated annealing for feature selectionen_US
newfileds.departmentEngineering and Technologyen_US
newfileds.general-subjectComputers and Information Technology | الحاسوب وتكنولوجيا المعلوماتen_US
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