Treatment of skewed multi-dimensional training data to facilitate the task of engineering neural models

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dc.authorid0000-0002-2126-8757
dc.authorid0000-0001-5252-6301
dc.contributor.authorAltun, H.
dc.contributor.authorBilgil, A.
dc.contributor.authorFidan, B. C.
dc.date.accessioned2019-08-01T13:38:39Z
dc.date.available2019-08-01T13:38:39Z
dc.date.issued2007
dc.departmentNiğde ÖHÜ
dc.description.abstractSuccessful application of neural network models relies heavily on problem-dependent internal parameters. As the theory does not facilitate the choice of the optimal parameters of neural models, these can solely be obtained through a tedious trial-and-error process. The process requires performing multiple training simulations with various network parameters, until satisfactory performance criteria of a neural model are met. In literature, it has been shown that neural models are not consistently good in prediction under highly skewed data. Consequently, the cost of engineering neural models rises in such circumstance to seek for appropriate internal parameters. In this paper the aim is to show that a recently proposed treatment of highly skewed data eases the task of practitioners in engineering neural network models to meet satisfactory performance criteria. As the applications of neural models grows dramatically in diverse engineering domains, the understanding of the treatment show indispensable practical values. (c) 2006 Elsevier Ltd. All rights reserved.
dc.identifier.doi10.1016/j.eswa.2006.07.010
dc.identifier.endpage983
dc.identifier.issn0957-4174
dc.identifier.issue4
dc.identifier.scopus2-s2.0-33947626066
dc.identifier.scopusqualityQ1
dc.identifier.startpage978
dc.identifier.urihttps://dx.doi.org/10.1016/j.eswa.2006.07.010
dc.identifier.urihttps://hdl.handle.net/11480/5338
dc.identifier.volume33
dc.identifier.wosWOS:000246315200015
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthor[0-Belirlenecek]
dc.language.isoen
dc.publisherPERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartofEXPERT SYSTEMS WITH APPLICATIONS
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectmulti-dimensional data treatment
dc.subjectskewness
dc.subjectartificial neural networks
dc.subjectmultilayered perceptron
dc.subjectback propagation
dc.subjectsuspended sediment prediction
dc.titleTreatment of skewed multi-dimensional training data to facilitate the task of engineering neural models
dc.typeArticle

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