A comparison of feature selection algorithms for cancer classification through gene expression data: Leukemia case

dc.contributor.author	Tasci, A.
dc.contributor.author	Ince, T.
dc.contributor.author	Guzelis, C.
dc.date.accessioned	2021-01-25T20:48:38Z
dc.date.available	2021-01-25T20:48:38Z
dc.date.issued	2018
dc.identifier.uri	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046295885&partnerID=40&md5=4cb69bde42719a0b42513e28bd5c6aaa
dc.identifier.uri	https://dspace.yasar.edu.tr/xmlui/handle/20.500.12742/9999
dc.description.abstract	In this study, three different feature selection algorithms are compared using Support Vector Machines as classifier for cancer classification through gene expression data. The ability of feature selection algorithms to select an optimal gene subset for a
dc.language.iso	English
dc.publisher	2017 10th International Conference on Electrical and Electronics Engineering, ELECO 2017
dc.title	A comparison of feature selection algorithms for cancer classification through gene expression data: Leukemia case
dc.type	Conference Paper
dc.relation.firstpage	1352
dc.relation.lastpage	1354
dc.relation.volume	2018-January
dc.description.affiliations	Department of Electrical and Electronics Engineering, Izmir Institute of Technology, Turkey; Department of Electrical and Electronics Engineering, Izmir University of Economics, Turkey; Department of Electrical and Electronics Engineering, Yaşar Universit

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