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http://dx.doi.org/10.5345/JKIBC.2022.22.3.317

Analysis of the Construction Cost Prediction Performance according to Feature Scaling and Log Conversion of Target Variable  

Kang, Yoon-Ho (Graduate School, GyeongSang National University)
Yun, Seok-Heon (Department of Architectural Engineering, GyeongSang National University)
Publication Information
Journal of the Korea Institute of Building Construction / v.22, no.3, 2022 , pp. 317-326 More about this Journal
Abstract
With the development of various technologies in the area of artificial intelligence, a number of studies to application of artificial intelligence technology in the construction field are underway. Diverse technologies have been applied to the task of predicting construction costs, and construction cost prediction technologies applying artificial intelligence technologies have recently been developed. However, it is difficult to secure the vast amount of construction cost data required for machine learning, which has not yet been practically used. In this study, to predict the construction cost, the latest artificial neural network(ANN) method is used to propose a method to improve the construction cost prediction performance. In particular, to improve predictive performance, a log conversion method of target variables and a feature scaling method to eliminate the difference in the relative influence of each column data are applied, and their performance in predicting construction cost is compared and analyzed.
Keywords
machine learning; log conversion; construction cost; target; feature scaling;
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Times Cited By KSCI : 1  (Citation Analysis)
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