Construction Project Cost Prediction Based on Genetic Algorithm and Least Squares Support Vector Machine
- DOI
- 10.2991/iccet-15.2015.190How to use a DOI?
- Keywords
- construction project cost; forecast model; genetic algorithm; least squares support vectormachines; small-sample learning
- Abstract
For the small sample data and complex nonlinear characteristic of construction project cost, a new hybrid prediction model combing genetic algorithm and small sample learning model based on least squares support vector machines is proposed. First, all the candidate features are ranked by correlation with the dependent variable, the front ranking features are used to initialize part of population for the genetic algorithm to get better feature subset, and then the construction cost prediction model of the least square support vector machine is constructed. Experiments on Jiangsu Province housing project data show an improved performance over other models in prediction accuracy, it is an effective method of project cost forecasting.
- Copyright
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Ming Xu AU - Bingfeng Xu AU - Lanjiang Zhou AU - Lin Wu PY - 2015/11 DA - 2015/11 TI - Construction Project Cost Prediction Based on Genetic Algorithm and Least Squares Support Vector Machine BT - Proceedings of the 5th International Conference on Civil Engineering and Transportation 2015 PB - Atlantis Press SP - 1004 EP - 1009 SN - 2352-5401 UR - https://doi.org/10.2991/iccet-15.2015.190 DO - 10.2991/iccet-15.2015.190 ID - Xu2015/11 ER -