Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/2425
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dc.contributor.authorDoğan, Sevgi Zeynep-
dc.contributor.authorArditi, David-
dc.contributor.authorGünaydın, Hüsnü Murat-
dc.date.accessioned2016-11-11T08:45:05Z
dc.date.available2016-11-11T08:45:05Z
dc.date.issued2008
dc.identifier.citationDoğan, S. Z., Arditi, D., and Günaydın, H. M. (2008). Using decision trees for determining attribute weights in a case-based model of early cost prediction. Journal of Construction Engineering and Management, 134(2), 146-152. doi:10.1061/(ASCE)0733-9364(2008)134:2(146)en_US
dc.identifier.issn0733-9364
dc.identifier.issn0733-9364-
dc.identifier.urihttp://doi.org/10.1061/(ASCE)0733-9364(2008)134:2(146)
dc.identifier.urihttp://hdl.handle.net/11147/2425
dc.description.abstractThis paper compares the performance of three different decision-tree-based methods of assigning attribute weights to be used in a case-based reasoning (CBR) prediction model. The generation of the attribute weights is performed by considering the presence, absence, and the positions of the attributes in the decision tree. This process and the development of the CBR simulation model are described in the paper. The model was tested by using data pertaining to the early design parameters and unit cost of the structural system of residential building projects. The CBR results indicate that the attribute weights generated by taking into account the information gain of all the attributes performed better than the attribute weights generated by considering only the appearance of attributes in the tree. The study is of benefit primarily to researchers, as it compares the impact of attribute weights generated by three different methods and, hence, highlights the fact that the prediction rate of models such as CBR largely depends on the data associated with the parameters used in the model.en_US
dc.language.isoenen_US
dc.publisherAmerican Society of Civil Engineers (ASCE)en_US
dc.relation.ispartofJournal of Construction Engineering and Management - ASCEen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDecision treesen_US
dc.subjectComputer softwareen_US
dc.subjectDecision makingen_US
dc.subjectOptimization modelsen_US
dc.subjectPredictionsen_US
dc.titleUsing decision trees for determining attribute weights in a case-based model of early cost predictionen_US
dc.typeArticleen_US
dc.authoridTR114949en_US
dc.authoridTR7988en_US
dc.institutionauthorDoğan, Sevgi Zeynep-
dc.institutionauthorGünaydın, Hüsnü Murat-
dc.departmentİzmir Institute of Technology. Architectureen_US
dc.identifier.volume134en_US
dc.identifier.issue2en_US
dc.identifier.startpage146en_US
dc.identifier.endpage152en_US
dc.identifier.wosWOS:000252485900008en_US
dc.identifier.scopus2-s2.0-38149069676en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1061/(ASCE)0733-9364(2008)134:2(146)-
dc.relation.doi10.1061/(ASCE)0733-9364(2008)134:2(146)en_US
dc.coverage.doi10.1061/(ASCE)0733-9364(2008)134:2(146)en_US
local.message.claim2022-06-04T18:59:46.691+0300|||rp02902|||submit_approve|||dc_contributor_author|||None*
dc.identifier.scopusqualityQ1-
item.grantfulltextopen-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.openairetypeArticle-
item.languageiso639-1en-
item.fulltextWith Fulltext-
crisitem.author.dept02.02. Department of Architecture-
crisitem.author.dept02.02. Department of Architecture-
Appears in Collections:Architecture / Mimarlık
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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