An Improved Support Vector Machine based on Rough Set for Construction Cost Prediction

Evaluation of construction projects is an important task for management of construction projects. An accurate forecast is required to enable supporting the investment decision and to ensure the project's feasible at the minimal cost. So controlling and rationally determining the construction cost plays the most important roles in the budget management of the construction project. Ways and means have been explored to satisfy the requirements for prediction of construction projects recently a novel regression technique, called Support Vector Machines (SVM), based on the statistical learning theory is exploded in this paper for the prediction of construction cost. Nevertheless, the standard SVM still has some difficult in attribute reduction and precision of prediction. This paper introduced the theory of the Rough Set (RS) for good performance in attribute reduction, considered and extracted substances components of construction project as parameters, and set up the Model of the Construction Cost Prediction based on the SVM-RS. The research results show that the prediction accuracy.