Application of Rough Set and Neural Network in Engineering Cost Estimation

Abstract

Uncontrolled project investment attracts more and more public attention. The inaccuracy of cost estimation is one of main reasons that make the government investment out of control. Cost estimation is affected by many uncertain factors, and the relationship between these factors are nonlinear, and the traditional model is hard to solve. This paper brings forward a model based on rough set and neural network for estimating engineering construction cost. Firstly based on the original sample, build up the decision table by rough set theory and extract the knowledge of classification, which includes attribute discretization, attribute importance ranking, attribute reduction and classification rule. Then input the extracted key components into neural network as the input training sample. This method reduced the structure of neural network, and improved the training speed and the accuracy of classification. Case study shows that the method has a good reference value for practical application.