Paper Info
Title
Optimizing parameters of support vector machine using fast messy genetic algorithm for dispute classification
Abstract
Hybrid system is a potential tool to deal with construction engineering and management problems. This study proposes an optimized hybrid artificial intelligence model to integrate a fast messy genetic algorithm (fmGA) with a support vector machine (SVM). The fmGA-based SVM (GASVM) is used for early prediction of dispute propensity in the initial phase of public-private partnership projects. Particularly, the SVM mainly provides learning and curve fitting while the fmGA optimizes SVM parameters. Measures in term of accuracy, precision, sensitivity, specificity, and area under the curve and synthesis index are used for performance evaluation of proposed hybrid intelligence classification model. Experimental comparisons indicate that GASVM achieves better cross-fold prediction accuracy compared to other baseline models (i.e., CART, CHAID, QUEST, and C5.0) and previous works. The forecasting results provide the proactive-warning and decision-support information needed to manage potential disputes.
Year
DOI
Venue
2014
10.1016/j.eswa.2013.12.035
Expert Syst. Appl.
Keywords
Field
DocType
early prediction,fmga optimizes svm parameter,support vector machine,hybrid system,curve fitting,cross-fold prediction accuracy,potential dispute,dispute classification,optimizing parameter,messy genetic algorithm,optimized hybrid artificial intelligence,proposed hybrid intelligence classification,baseline model,fmga-based svm,project management,optimization
Data mining,CHAID,Curve fitting,Computer science,Support vector machine,Artificial intelligence,Hybrid system,Machine learning,Genetic algorithm,Project management
Journal
Volume
Issue
ISSN
41
8
0957-4174
Citations 
PageRank 
References 
10
0.49
32
Authors
4
Name
Order
Citations
PageRank
Jui-Sheng Chou114917.95
Min-Yuan Cheng217419.84
Yu-Wei Wu3435.89
Anh-Duc Pham4523.89