A joint generalized exemplar method for classification of massive datasets

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dc.authoridTağluk, M. Emin/0000-0001-7789-6376
dc.authoridERTUGRUL, Ömer Faruk/0000-0003-0710-0867
dc.authorwosidTağluk, M. Emin/ABH-1005-2020
dc.authorwosidERTUGRUL, Ömer Faruk/F-7057-2015
dc.contributor.authorTagluk, Mehmet Emin
dc.contributor.authorErtugrul, Omer Faruk
dc.date.accessioned2024-08-04T20:41:08Z
dc.date.available2024-08-04T20:41:08Z
dc.date.issued2015
dc.departmentİnönü Üniversitesien_US
dc.description.abstractDue to technological improvements, the number and volume of datasets are considerably increasing and bring about the need for additional memory and computational complexity. To work with massive datasets in an efficient way; feature selection, data reduction, rule based and exemplar based methods have been introduced. This study presents a method, which may be called joint generalized exemplar (JGE), for classification of massive data sets. This method aims to enhance the computational performance of NGE by working against nesting and overlapping of hyper-rectangles with reassessing the overlapping parts with the same procedure repeatedly and joining non-overlapped hyper-rectangle sections that falling within the same class. This provides an opportunity to have adaptive decision boundaries, and also employing batch data searching instead of incremental searching. Later, the classification was done in accordance with the distance between each particular query and generalized exemplars. The accuracy and time requirements for classification of synthetic datasets and a benchmark dataset obtained by JGE, NGE and other popular machine learning methods were compared and the achieved results by JGE found acceptable. (C) 2015 Elsevier B.V. All rights reserved.en_US
dc.identifier.doi10.1016/j.asoc.2015.07.044
dc.identifier.endpage498en_US
dc.identifier.issn1568-4946
dc.identifier.issn1872-9681
dc.identifier.scopus2-s2.0-84939813591en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage487en_US
dc.identifier.urihttps://doi.org/10.1016/j.asoc.2015.07.044
dc.identifier.urihttps://hdl.handle.net/11616/96936
dc.identifier.volume36en_US
dc.identifier.wosWOS:000360424700040en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Science Bven_US
dc.relation.ispartofApplied Soft Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectNested generalized exemplaren_US
dc.subjectExemplar-based learningen_US
dc.subjectClassificationen_US
dc.subjectCompressionen_US
dc.subjectArtificial intelligenceen_US
dc.titleA joint generalized exemplar method for classification of massive datasetsen_US
dc.typeArticleen_US

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