## 3 Methodology
### CBR Training  
The next step of the research process is the CBR training process, which includes a case input and model training. The case input involves collecting old related cases and defining the properties of each case. This collection of cases becomes the repository for the model training section. The CBR repository for the window installation provides the types of building components to be installed, when/where/by whom it was installed, the material types, and its accessibility. The related six attributes are the story, position, size, material, duration, and accessibility code. Unlike the others, the accessibility code is not generally acquired from BIM; users should input a degree of difficulty for construction workers to install into the CBR repository. These multidimensional attributes of the CBR facilitate automatic data retrieval from existing installation data. Descriptions of each attribute of the proposed CBR repository are summarized in Table 1.  
[PERSON] [27] and [PERSON] et al. [28] stated that selecting important attributes in a CBR process results in a satisfactory degree of accuracy, speed, ease of use, ease in updating, clarity of explanation in construction cost estimations, and consistency in variables. Some systems may rely on the user's experience for determining the weights; however, an AI algorithm, factor selection, was used in this research. A key process in CBR is called case retrieval, wherein the algorithm determines the factors that carry the most weight among the attributes of the cases in the case base. Some researchers set the weights based on experience and using an analytic hierarchy process [29], which may involve both uncertainty and imprecision owing to the requirement for human intervention. Other researchers have conducted a genetic algorithm to determine the weight factors [23], which requires extensive  
Figure 3: Window 3D representation and properties reproduced using Industry Foundation Class (IFC) data exchange. computational power in the entire process. In this research, the use of a machine learning algorithm, named factor selection, is proposed for selecting important factors [25; 30]. Factor selection is the process that finds that appropriate factors from a large set of original attributes can be determined with the objective of describing the original dataset. The factor selection and weight assignment were accomplished with the use of the software Weka 3, which is a data mining software that uses machine learning algorithms to define the factor weights. The process included defining the factors and the options for those factors. After the factors were defined, the cases used in the CBR were input in the Weka 3 program. Based on these cases, the program determines which factors influence the target factor, which is the cost. The results of the analysis indicating the ranking of the factors from the most influential on cost to the least influential are the story, accessibility, position, material, and size. Figure 4 shows the weights assigned to each factor. The story factor for the window cost estimation has a much higher value owing to its significant influence on the cost of installing a window.