Project dispute prediction by hybrid machine learning techniques

Jui Sheng Chou, Chih Fong Tsai, Yu Hsin Lu

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)505-517
Number of pages13
JournalJournal of Civil Engineering and Management
Volume19
Issue number4
DOIs
StatePublished - Sep 2013

Keywords

  • Clustering and classification
  • Dispute prediction
  • Hybrid intelligence
  • Machine learning
  • Project management
  • Public-private partnership

Fingerprint

Dive into the research topics of 'Project dispute prediction by hybrid machine learning techniques'. Together they form a unique fingerprint.

Cite this