Treatment of multi-dimensional data to enhance neural network estimators in regression problems

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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.abstractThis paper proposes and explains a data treatment technique to improve the accuracy of a neural network estimator in regression problems, where multi-dimensional input data set is highly skewed and non-normally distributed. The proposed treatment modifies the distribution characteristics of the data set. The prediction of the suspended sediment, which is an important problem in river engineering applications, will be considered as a case study. Conventional approaches lack in providing high accuracy due to the inherently employed simplicity in order to obtain empirical formulae. On the other hand, artificial neural networks are able to model the non-linear characteristics of the mechanism of the sediment transport and have a growing body of applications in diverse applications in civil engineering. It will be shown that a significant enhancement and superior score in accuracy, compared with the classical approaches, are obtainable when the proposed treatment is employed. The proposed technique is an extension to the understanding of the practical aspects of neural computing applications. Therefore the outcome of the present study is important as it is applicable to any scenario where neural network approaches are involved. (C) 2006 Elsevier Ltd. All rights reserved.
dc.identifier.doi10.1016/j.eswa.2006.01.054
dc.identifier.endpage605
dc.identifier.issn0957-4174
dc.identifier.issue2
dc.identifier.scopus2-s2.0-33750474052
dc.identifier.scopusqualityQ1
dc.identifier.startpage599
dc.identifier.urihttps://dx.doi.org/10.1016/j.eswa.2006.01.054
dc.identifier.urihttps://hdl.handle.net/11480/5415
dc.identifier.volume32
dc.identifier.wosWOS:000242979100033
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.subjectartificial neural networks
dc.subjectregression
dc.subjectmulti-layered perceptron
dc.subjectsediment transport
dc.subjectskewness
dc.subjecttraining data treatment
dc.titleTreatment of multi-dimensional data to enhance neural network estimators in regression problems
dc.typeArticle

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