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Title: Using decision trees for determining attribute weights in a case-based model of early cost prediction
Authors: Doğan, Sevgi Zeynep
Arditi, David
Günaydın, Hüsnü Murat
Keywords: Decision trees
Computer software
Decision making
Optimization models
Publisher: American Society of Civil Engineers (ASCE)
Source: Doğan, 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)
Abstract: This 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.
ISSN: 0733-9364
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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