Using Decision Trees for Determining Attribute Weights in a Case-Based Model of Early Cost Prediction |
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Authors: | Sevgi Zeynep Do?an David Arditi H Murat Günaydin |
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Affiliation: | 1Research Assistant, Dept. of Architecture, Izmir Institute of Technology, Izmir 35430, Turkey. E-mail: sevgidogan@iyte.edu.tr 2Professor, Dept. of Civil and Architectural Engineering, Illinois Institute of Technology, Chicago, IL 60616-3793. E-mail: arditi@iit.edu 3Associate Professor, Dept. of Architecture, Izmir Institute of Technology, Izmir, 35430, Turkey.
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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. |
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Keywords: | Construction cost Cost estimates Decision making Decision support systems Computer software Optimization models Predictions Weight |
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