Professor Dr. Jui-Sheng Chou, Professor Dr. Min-Yuan Cheng, Yu-Wei Wu, and Anh-Duc Pham have published Optimizing parameters of support vector machine using fast messy genetic algorithm for dispute classification, Expert Systems with Applications, 41, 3955–3964 (2014).
Here is the 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.
The application context for the article is construction contracts involving public-private partnerships in Taiwan:
[…] the dispute between PPP participants commonly occur unexpectedly and may involve many issues, including surety bond issue, sub-contractor qualifications, licenses, permits, investment scale, resident rights, government guarantees, excessive profits, operating period, taxation, and default loan commitment […]. Numerous studies show that an efficient, effective, and fair dispute resolution process is essential for PPP project success […]. Therefore, development of intelligence models can enable early warming of potential dispute resolutions prior to project initiation to be becoming crucial. […]