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/).

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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  -