Application of Boosting Regression Trees to Preliminary Cost Estimation in Building Construction Projects.
Among the recent data mining techniques available, the boosting approach has attracted a great deal of attention because of its effective learning algorithm and strong boundaries in terms of its generalization performance. However, the boosting approach has yet to be used in regression problems within the construction domain, including cost estimations, but has been actively utilized in other domains. Therefore, a boosting regression tree (BRT) is applied to cost estimations at the early stage of a construction project to examine the applicability of the boosting approach to a regression problem within the construction domain. To evaluate the performance of the BRT model, its performance was compared with that of a neural network (NN) model, which has been proven to have a high performance in cost estimation domains. The BRT model has shown results similar to those of NN model using 234 actual cost datasets of a building construction project. In addition, the BRT model can provide additional information such as the importance plot and structure model, which can support estimators in comprehending the decision making process. Consequently, the boosting approach has potential applicability in preliminary cost estimations in a building construction project.
Department of Plant and Architectural Engineering, Kyonggi University, Gwanggyosan-ro 154-42, Yeongtong-gu, Suwon, Gyeonggi-do 443-760, Republic of Korea.
Yoonseok
Y
Shin
D000465:Algorithms; D001185:Artificial Intelligence / Q000191:economics; D003198:Computer Simulation / Q000191:economics*; D003663:Decision Trees; D015233:Models, Statistical; D012044:Regression Analysis