Vilnius Gediminas Technical University;Taylor & Francis Group
摘要:
摘要： This study compares several well-known machine learning techniques for public-private partnership (PPP) project dispute problems. Single and hybrid classification techniques are applied to construct models for PPP project dispute prediction. The single classification techniques utilized are multilayer perceptron (MLP) neural networks, decision trees (DTs), support vector machines, the naïve Bayes classifier, and k-nearest neighbor. Two types of hybrid learning models are developed. One combines clustering and classification techniques and the other combines multiple classification techniques. Experimental results indicate that hybrid models outperform single models in prediction accuracy, Type I and II errors, and the receiver operating characteristic curve. Additionally, the hybrid model combining multiple classification techniques perform better than that combining clustering and classification techniques. Particularly, the MLP+MLP and DT+DT models perform best and second best, achieving prediction accuracies of 97.08% and 95.77%, respectively. This study demonstrates the efficiency and effectiveness of hybrid machine learning techniques for early prediction of dispute occurrence using conceptual project information as model input. The models provide a proactive warning and decision-support information needed to select the appropriate resolution strategy before a dispute occurs. 其他題名： JCEM 出版者： Taylor & Francis Group 出版日期： 2013-08-01 出處： Journal of civil engineering and management, 2013-08, Vol.19 (4), p.505-517 資源來源： DOAJ Directory of Open Access Journals 版權： Copyright Vilnius Gediminas Technical University (VGTU) Press 2013 版權： Copyright (c) 2013 The Author(s). Published by Vilnius Gediminas Technical University. 版權： COPYRIGHT 2013 Vilnius Gediminas Technical University 識別號： ISSN: 1392-3730 識別號： ISSN: 1822-3605 識別號： EISSN: 1822-3605 識別號： DOI: 10.3846/13923730.2013.768544
