Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11889/5516
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dc.contributor.authorAhmad, Subhi
dc.contributor.authorMafarja, Majdi
dc.contributor.authorFaris, Hossam
dc.contributor.authorAljarah, Ibrahim
dc.date.accessioned2018-04-03T09:15:30Z
dc.date.available2018-04-03T09:15:30Z
dc.date.issued2018-03-24
dc.identifier.urihttp://hdl.handle.net/20.500.11889/5516
dc.descriptionThe paper was presented in the 2nd International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence (ISMSI 2018) which was held in Phuket, Thailand during March 24-25, 2018
dc.description.abstractThe performance of classification algorithms is highly sensitive to the data dimensionality. High dimensionality may cause many problems to a classifier like overfitting and high computational time. Feature selection (FS) is a key solution to both problems. It aims to reduce the number of features by removing the irrelevant, redundant and noisy data, while trying to keep an acceptable classification accuracy. FS can be formulated as an optimization problem. Metaheuristic algorithms have shown superior performance in solving this type of problems. In this work, a chaotic version of Salp Swarm Algorithm (SSA) is proposed, which is considered one of the recent metaheuristic algorithms. The proposed approach is applied for the first time on feature selection problems. Four different chaotic maps are used to control the balance between the exploration and exploitation in the proposed approach. The proposed approaches are evaluated using twelve real datasets. The comparative results shows that the chaotic maps significantly enhances the performance of the SSA algorithm and outperforms other similar approaches in the literature.en_US
dc.language.isoen_USen_US
dc.publisherACMen_US
dc.subjectData structures (Computer science)en_US
dc.subjectMathematical optimizationen_US
dc.subjectArtificial intelligenceen_US
dc.subjectHeuristic algorithmsen_US
dc.subjectAlgorithmsen_US
dc.subjectMachine learningen_US
dc.subjectDatabase managementen_US
dc.subjectComputer network architecturesen_US
dc.subject.lcshMappings (Mathematics)
dc.subject.lcshSeparation of variables
dc.titleFeature selection using salp swarm algorithm with chaosen_US
dc.typeConference Proceedingsen_US
newfileds.departmentEngineering and Technologyen_US
newfileds.conferenceIntelligent Systems, Metaheuristics & Swarm Intelligence (2nd : 2018 : Phuket, Thailand)en_US
newfileds.item-access-typeopen_accessen_US
newfileds.thesis-prognoneen_US
newfileds.general-subjectComputers and Information Technology | الحاسوب وتكنولوجيا المعلوماتen_US
item.grantfulltextopen-
item.fulltextWith Fulltext-
item.languageiso639-1other-
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