An Adaptive Genetic Algorithm with Recursive Feature Elimination Approach for Predicting Malaria Vector Gene Expression Data Classification using Support Vector Machine Kernels

dc.contributor.authorArowolo, Micheal Olaolu
dc.contributor.authorAdebiyi, Marion Olubunmi
dc.contributor.authorNnodim, Chiebuka Timothy
dc.date.accessioned2023-08-31T14:39:37Z
dc.date.available2023-08-31T14:39:37Z
dc.date.issued2021-07-27
dc.description.sponsorshipACE: Applied Informatics and Communicationen_US
dc.identifier.issn1686-3933
dc.identifier.urihttp://hdl.handle.net/123456789/2107
dc.language.isoenen_US
dc.publisherWalailak J Sci & Techen_US
dc.relation.ispartofseriesWalailak J Sci & Tech;2021; 18(17)
dc.subjectAdaptive genetic algorithmen_US
dc.subjectRecursive feature eliminationen_US
dc.subjectMalaria vectoren_US
dc.subjectVector Machine kernelsen_US
dc.subjectNigeriaen_US
dc.subjectACE: Applied Informatics and Communicationen_US
dc.subjectCovenant Universityen_US
dc.subjectDigital Developmenten_US
dc.titleAn Adaptive Genetic Algorithm with Recursive Feature Elimination Approach for Predicting Malaria Vector Gene Expression Data Classification using Support Vector Machine Kernelsen_US
dc.typeArticleen_US
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