A hybrid feature selection method for classifying metagenomic data in relation to inflammatory bowel disease

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dc.contributor.authorAlshawaqfeh, M.
dc.contributor.authorGharaibeh, A.
dc.contributor.authorWajid, B.
dc.date.accessioned2021-01-25T21:48:03Z
dc.date.available2021-01-25T21:48:03Z
dc.date.issued2019
dc.departmentİstanbul Ticaret Üniversitesien_US
dc.descriptionUniversity of Northumbria at Newcastleen_US
dc.description3rd International Conference on Advances in Artificial Intelligence, ICAAI 2019 -- 26 October 2019 through 28 October 2019 -- -- 157135en_US
dc.description.abstractDue to the recent advances in high throughput metagenomic sequencing technologies, Microbial abundance profiles of environmental samples have become publicly available. Increasing number of metagenomic studies has associated the imbalance of bacterial abundance to health and disase state of the host. This suggests utilizing the bacterial profiles as a diagnostic tool to identify the bacterial-related disease state of individuals. However, the high dimensional nature of metagenomic datasets renders this process a challenging task. Therefore, an efficient framework that enables accurate classification of metagenomic samples belonging to different classes is of central important. In this work, a hybrid feature selection technique that combines the advantages of filter and wrapper feature selection algorithms is proposed. The experimental results demonstrate that the proposed algorithm outperforms widely used feature selection techniques in terms of classification accuracy and provide a significant reduction in the computation time. © 2019 ACM.en_US
dc.identifier.doi10.1145/3369114.3371675en_US
dc.identifier.endpage89en_US
dc.identifier.isbn9781450000000
dc.identifier.scopus2-s2.0-85079101012en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage86en_US
dc.identifier.urihttps://doi.org/10.1145/3369114.3371675
dc.identifier.urihttps://hdl.handle.net/11467/4496
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherAssociation for Computing Machineryen_US
dc.relation.ispartofACM International Conference Proceeding Seriesen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectClassificationen_US
dc.subjectHybrid Feature Selectionen_US
dc.subjectMetagenomicsen_US
dc.titleA hybrid feature selection method for classifying metagenomic data in relation to inflammatory bowel diseaseen_US
dc.typeConference Objecten_US

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