Name
Title | ||
|---|---|---|
Improving classification accuracy of project dispute resolution using hybrid artificial intelligence and support vector machine models |
Abstract | ||
|---|---|---|
Support vector machines (SVMs) have been applied successfully to construction knowledge domains. However, SVMs, as a baseline model, still have a potential improvement space by integrating hybrid intelligence. This work compares the performance of various classification models using the combination of fuzzy logic, a fast and messy genetic algorithm, and SVMs. A set of public-private partnership projects was collected as a real case study in construction management. The data were split into mutually independent folds for cross validation. Experimental results demonstrate that the proposed hybrid artificial intelligence system has the best and most reliable classification accuracy at 77.04%, a 24.76% improvement compared with that of SVMs in predicting project dispute resolution (PDR) outcomes (i.e., mediation, arbitration, litigation, negotiation, and administrative appeals) when the dispute category and phase in which a dispute occurs are known during project execution. This work demonstrates the improvement capability of hybrid intelligence in classifying PDR predictions related to public infrastructure projects. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.eswa.2012.10.036 | Expert Syst. Appl. |
Keywords | Field | DocType |
construction knowledge domain,project dispute resolution,proposed hybrid artificial intelligence,improvement capability,improving classification accuracy,classifying pdr prediction,dispute category,hybrid intelligence,potential improvement space,project execution,support vector machine model,construction management,fuzzy logic,classification,support vector machines,genetic algorithm | Data mining,Dispute resolution,Computer science,Support vector machine,Fuzzy logic,Arbitration,Artificial intelligence,Cross-validation,Artificial Intelligence System,Machine learning,Genetic algorithm,Construction management | Journal |
Volume | Issue | ISSN |
40 | 6 | 0957-4174 |
Citations | PageRank | References |
10 | 0.52 | 14 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jui-Sheng Chou | 1 | 149 | 17.95 |
| Min-Yuan Cheng | 2 | 174 | 19.84 |
| Yu-Wei Wu | 3 | 43 | 5.89 |