A feature selection application using particle swarm optimization for learning concept detection

dc.contributor.author	Gunel, K.
dc.contributor.author	Erdogdu, K.
dc.contributor.author	Polat, R.
dc.contributor.author	Ozarslan, Y.
dc.contributor.editor	Adeli H.Correia A.M.Costanzo S.Reis L.P.Rocha A.
dc.date.accessioned	2021-01-25T20:49:03Z
dc.date.available	2021-01-25T20:49:03Z
dc.date.issued	2017
dc.identifier	10.1007/978-3-319-56538-5_94
dc.identifier.issn	21945357
dc.identifier.uri	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018597356&doi=10.1007%2f978-3-319-56538-5_94&partnerID=40&md5=ed041dca097d5982ae1dfd9cf13aa92a
dc.identifier.uri	https://dspace.yasar.edu.tr/xmlui/handle/20.500.12742/10132
dc.description.abstract	Recent developments of computational intelligence on educational technology yield concept map mining as a new research area. Concept map mining covers the extraction of learning concepts, specifying relations among them, and generating a concept map from
dc.language.iso	English
dc.publisher	Advances in Intelligent Systems and Computing
dc.title	A feature selection application using particle swarm optimization for learning concept detection
dc.type	Conference Paper
dc.relation.firstpage	952
dc.relation.lastpage	962
dc.relation.volume	570
dc.description.affiliations	Department of Mathematics, Adnan Menderes University, Aydın, Turkey; Department of Computer Engineering, Yaşar University, İzmir, Turkey; Department of Mathematics, Yaşar University, İzmir, Turkey; The Centre for Open and Distance Learning, Yaşar Universi

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