Show simple item record

contributor authorKa-Chi Lam
contributor authorMike Chun-Kit Lam
contributor authorDan Wang
date accessioned2017-05-08T21:40:16Z
date available2017-05-08T21:40:16Z
date copyrightMay 2010
date issued2010
identifier other%28asce%29cp%2E1943-5487%2E0000037.pdf
identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/58994
description abstractContractor prequalification is basically a nonlinear two-group classification problem. A robust contractor prequalification decision model should include the ability of handling both quantitative and qualitative data. Support vector machine (SVM) is a set of related supervised learning methods which can handle data in a high dimensional feature space for nonlinear separable problems. A new contractor prequalification decision model using SVM is proposed to assist clients to identify qualified contractors for tendering in this study. A case study was used to validate the proposed decision model and the classification ability was compared with neural networks (NNs) and principal component analysis (PCA). The results show that the proposed SVM model outperforms NN and PCA and the merits of using SVM to mitigate the limitations of using NN are elaborated. The proposed decision model is an ideal alternative for supporting clients to perform contractor prequalification decision making.
publisherAmerican Society of Civil Engineers
titleEfficacy of Using Support Vector Machine in a Contractor Prequalification Decision Model
typeJournal Paper
journal volume24
journal issue3
journal titleJournal of Computing in Civil Engineering
identifier doi10.1061/(ASCE)CP.1943-5487.0000030
treeJournal of Computing in Civil Engineering:;2010:;Volume ( 024 ):;issue: 003
contenttypeFulltext


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record