Application of Support Vector Machine Based on Rough Sets to Project Risk Assessment (RS-SVM)
Zhengyuan Jia, Lihua Gong, Jia Liang Han · 2008
The risk assessment of project is the important content for project management. This paper combines rough sets theory and support vector machine. The paper selects rough sets (RS) and support vector machine (SVM) algorithms to establish a new mathematical model for risk assessment of project. Using the rough sets to reduce numbers of indicators of risk factors, which reduces the dimensions of the input space. When treating the reduced data as the input space of SVM, we find that both the convergence speed and the assessment accuracy are enhanced. The results of Matlab simulation show the superiority of the model. The model based on rough sets and support vector machine can effectively help project managers for management of project risk.