Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11889/5540
DC FieldValueLanguage
dc.contributor.authorFaris, Hossam
dc.contributor.authorMafarja, Majdi
dc.contributor.authorHeidari, Ali Asghar
dc.contributor.authorAljarah, Ibrahim
dc.contributor.authorAl-Zoubi, Ala’ M.
dc.contributor.authorMirjalili, Seyedali
dc.contributor.authorFujita, Hamido
dc.date.accessioned2018-05-12T05:48:12Z
dc.date.available2018-05-12T05:48:12Z
dc.date.issued2018-05-09
dc.identifier.otherhttps://doi.org/10.1016/j.knosys.2018.05.009
dc.identifier.urihttp://hdl.handle.net/20.500.11889/5540
dc.description.abstractSearching for the (near) optimal subset of features is a challenging problem in the process of Feature Selection (FS). In the literature, Swarm Intelligence (SI) algorithms show superior performance in solving this problem. This motivated our attempts to test the performance of the newly proposed Salp Swarm Algorithm (SSA) in this area. As such, two new wrapper FS approaches that use SSA as the search strategy are proposed. In the first approach, eight transfer functions are employed to convert the continuous version of SSA to binary. In the second approach, the crossover operator is used in addition to the transfer functions to replace the average operator and enhance the exploratory behavior of the algorithm. The proposed approaches are benchmarked on 22 well-known UCI datasets and the results are compared with 5 FS methods: Binary Grey Wolf Optimizer (BGWO), Binary Gravitational Search Algorithms (BGSA), Binary Bat Algorithm (BBA), Binary Particle Swarm Optimization (BPSO), and Genetic Algorithm (GA). The paper also considers an extensive study of the parameter setting for the proposed technique. From the results, it is observed that the proposed approach significantly outperforms others on around 90% of the datasets.en_US
dc.publisherExpert systems (Computer science)en_US
dc.subjectMathematical optimizationen_US
dc.subjectComputer algorithmsen_US
dc.subjectArtificial intelligenceen_US
dc.subjectData miningen_US
dc.subjectMachine learningen_US
dc.subjectClassificationen_US
dc.subjectData structures (Computer science)en_US
dc.titleAn efficient binary salp swarm algorithm with crossover scheme for feature selection problemsen_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
item.fulltextWith Fulltext-
item.grantfulltextopen-
Appears in Collections:Fulltext Publications
Show simple item record

Page view(s)

46
Last Week
0
Last month
2
checked on May 11, 2022

Download(s)

525
checked on May 11, 2022

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.