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dc.contributor.authorYousef, Malik
dc.contributor.authorJabeer, Amhar
dc.contributor.authorBakir-Gungor, Burcu
dc.date.accessioned2022-02-15T09:17:31Z
dc.date.available2022-02-15T09:17:31Z
dc.date.issued2021en_US
dc.identifier.issn1865-0929
dc.identifier.issn1865-0937
dc.identifier.urihttps //doi.org/10.1007/978-3-030-87101-7_21
dc.identifier.urihttps://hdl.handle.net/20.500.12573/1134
dc.description.abstractGene expression data classification provides a challenge in classification due to it having high dimensionality and a relatively small sample size. Different feature selection approaches have been used to overcome this issue and SVM-RCE being one of the more successful approach. This study is a continuation of two previous research studies SVM-RCE and SVM-RCE-R. SVM-RCE-R suggests a new approach in the scoring function for the clusters, showing that for some different combination of weights the performance was improved. The aim of this study is to find the optimal weights for the scoring function suggested in the study of SVM-RCE-R using optimization approaches. We have discovered that finding the optimal weights for the scoring function would improve the performance of the SVM-RCE-in most cases. We have shown that in some cases the performance is increased dramatically by 10% in terms of accuracy and AUC. By increasing the performance of the algorithm, it is more likely that we can extract subset genes relating to the class association of a microarray sample.en_US
dc.description.sponsorshipSoftware Competence Ctr Hagenberg; JKU Inst Telecooperat; iiwasen_US
dc.language.isoengen_US
dc.publisherSPRINGER INTERNATIONAL PUBLISHING AGGEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLANDen_US
dc.relation.isversionof10.1007/978-3-030-87101-7_21en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectOptimizationen_US
dc.subjectGene expression classificationen_US
dc.subjectMachine learningen_US
dc.titleSVM-RCE-R-OPT: Optimization of Scoring Function for SVM-RCE-Ren_US
dc.title.alternativeCommunications in Computer and Information Scienceen_US
dc.typebookParten_US
dc.contributor.departmentAGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.contributor.institutionauthorJabeer, Amhar
dc.contributor.institutionauthorBakir-Gungor, Burcu
dc.identifier.volumeVolume 1479 Page 215-224en_US
dc.relation.journalDATABASE AND EXPERT SYSTEMS APPLICATIONS - DEXA 2021 WORKSHOPSen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US


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