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dc.contributor.authorDag, Osman
dc.contributor.authorKarabulut, Erdem
dc.contributor.authorAlpar, Reha
dc.date.accessioned2021-06-03T06:03:28Z
dc.date.available2021-06-03T06:03:28Z
dc.date.issued2019
dc.identifier.issn1875-6891
dc.identifier.urihttp://dx.doi.org/10.2991/ijcis.d.190618.001
dc.identifier.urihttp://hdl.handle.net/11655/24171
dc.description.abstractGroup method of data handling (GMDH)-type neural network algorithms are the self-organizing algorithms for modeling complex systems. GMDH algorithms are used for different objectives; examples include regression, classification, clustering, forecasting, and so on. In this paper, we present GMDH2 package to perform binary classification via GMDH-type neural network algorithms. The package offers two main algorithms: GMDH algorithm and diverse classifiers ensemble based on GMDH (dce-GMDH) algorithm. GMDH algorithm performs binary classification and returns important variables. dce-GMDH algorithm performs binary classification by assembling classifiers based on GMDH algorithm. The package also provides a well-formatted table of descriptives in different format (R, LaTeX, HTML). Moreover, it produces confusion matrix and related statistics, and scatter plot (2D and 3D) with classification labels of binary classes to assess the prediction performance. Moreover, a user-friendly web-interface of the package is provided especially for non-R users. (c) 2019 The Authors. Published by Atlantis Press SARL.
dc.language.isoen
dc.relation.isversionof10.2991/ijcis.d.190618.001
dc.rightsAttribution 4.0 United States
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectClassification
dc.subjectMachine learning
dc.subjectR package
dc.subjectWeb-tool
dc.titleGmdh2: Binary Classification Via Gmdh-Type Neural Network Algorithms-R Package And Web-Based Tool
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.relation.journalInternational Journal Of Computational In℡Ligence Systems
dc.contributor.departmentBiyoistatistik
dc.identifier.volume12
dc.identifier.issue2
dc.description.indexWoS
dc.description.indexScopus


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Attribution 4.0 United States
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