cmaRs: A powerful predictive data mining package in R

dc.authorscopusid36015912400
dc.authorscopusid49662456700
dc.authorscopusid6506670261
dc.contributor.authorYerlikaya-oezkurt, Fatma
dc.contributor.authorYazici, Ceyda
dc.contributor.authorBatmaz, Inci
dc.contributor.otherIndustrial Engineering
dc.date.accessioned2024-07-05T15:22:14Z
dc.date.available2024-07-05T15:22:14Z
dc.date.issued2023
dc.departmentAtılım Universityen_US
dc.department-temp[Yerlikaya-oezkurt, Fatma] Atilim Univ, Dept Ind Engn, Ankara, Turkiye; [Yazici, Ceyda] TED Univ, Dept Math, Ankara, Turkiye; [Batmaz, Inci] Middle East Tech Univ, Dept Stat, Ankara, Turkiyeen_US
dc.description.abstractConic Multivariate Adaptive Regression Splines (CMARS) is a very successful method for modeling nonlinear structures in high-dimensional data. It is based on MARS algorithm and utilizes Tikhonov regularization and Conic Quadratic Optimization (CQO). In this paper, the open-source R package, cmaRs, built to construct CMARS models for prediction and binary classification is presented with illustrative applications. Also, the CMARS algorithm is provided in both pseudo and R code. Note here that cmaRs package provides a good example for a challenging implementation of CQO based on MOSEK solver in R environment by linking R MOSEK through the package Rmosek.en_US
dc.identifier.citation1
dc.identifier.doi10.1016/j.softx.2023.101553
dc.identifier.issn2352-7110
dc.identifier.scopus2-s2.0-85174703053
dc.identifier.urihttps://doi.org/10.1016/j.softx.2023.101553
dc.identifier.urihttps://hdl.handle.net/20.500.14411/2152
dc.identifier.volume24en_US
dc.identifier.wosWOS:001101634100001
dc.identifier.wosqualityQ2
dc.institutionauthorYerlikaya Özkurt, Fatma
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectConic multivariate adaptive regression splinesen_US
dc.subjectNonparametric regressionen_US
dc.subjectTikhonov regularizationen_US
dc.subjectConic quadratic programmingen_US
dc.subjectInterior point methoden_US
dc.subjectBinary classificationen_US
dc.titlecmaRs: A powerful predictive data mining package in Ren_US
dc.typeArticleen_US
dspace.entity.typePublication
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relation.isOrgUnitOfPublication.latestForDiscovery12c9377e-b7fe-4600-8326-f3613a05653d

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