Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11889/5618
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dc.contributor.authorAljarah, Ibrahim
dc.contributor.authorMafarja, Majdi
dc.contributor.authorHeidari, Ali Asghar
dc.contributor.authorFaris, Hossam
dc.contributor.authorZhang, Yong
dc.contributor.authorMirjalili, Seyedali
dc.date.accessioned2018-08-15T07:06:42Z
dc.date.available2018-08-15T07:06:42Z
dc.date.issued2018-07-25
dc.identifier.citationIbrahim Aljarah, Majdi Mafarja, Ali Asghar Heidari, Hossam Faris, Yong Zhang, Seyedali Mirjalili, Asynchronous Accelerating Multi-leader Salp Chains for Feature Selection, Applied Soft Computing, 2018, , ISSN 1568-4946, https://doi.org/10.1016/j.asoc.2018.07.040.en_US
dc.identifier.issn1568-4946
dc.identifier.urihttp://hdl.handle.net/20.500.11889/5618
dc.description.abstractFeature selection is an imperative preprocessing step that can positively affect the performance of data mining techniques. Searching for the optimal feature subset amongst an unabridged dataset is a challenging problem, especially for large-scale datasets. In this research, a binary Salp Swarm Algorithm (SSA) with asynchronous updating rules and a new leadership structure is proposed. To set the best leadership structure, several extensive experiments are performed to determine the most effective number of leaders in the social organization of the artificial salp chain. Inspired from the behaviour of a termite colony (TC) in dividing the termites into four types, the salp chain is then divided into several sub-chains, where the salps in each sub-chain can follow a different strategy to adaptively update their locations. Three different updating strategies are employed in this paper. The proposed algorithm is tested and validated on 20 well-known datasets from the UCI repository. The results and comparisons verify that utilizing half of the salps as leaders of the chain can significantly improve the performance of SSA in terms of accuracy metric. Furthermore, dynamically tuning the single parameter of algorithm enable it to more effectively explore the search space in dealing with different feature selection datasets.en_US
dc.language.isoen_USen_US
dc.publisherElsevieren_US
dc.relation.ispartofserieshttps://doi.org/10.1016/j.asoc.2018.07.040
dc.subjectSwarm intelligenceen_US
dc.subjectMachine learningen_US
dc.subjectClassificationen_US
dc.subjectAlgorithmsen_US
dc.subjectMachine learningen_US
dc.subjectMathematical optimizationen_US
dc.titleAsynchronous accelerating multi-leader salp chains for feature selectionen_US
dc.typeArticleen_US
newfileds.departmentEngineering and Technologyen_US
newfileds.corporate-authorMajdi Mafarjaen_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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item.languageiso639-1other-
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