The nature of statistical learning theory
The nature of statistical learning theory
Data mining: concepts and techniques
Data mining: concepts and techniques
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
RapidAccurate Optimization of Difficult Problems Using Fast Messy Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Fuzzy case-based reasoning for coping with construction disputes
Expert Systems with Applications: An International Journal
Fuzzy Logic for Business, Finance, and Management
Fuzzy Logic for Business, Finance, and Management
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Evolutionary fuzzy decision model for construction management using support vector machine
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Predicting high-tech equipment fabrication cost with a novel evolutionary SVM inference model
Expert Systems with Applications: An International Journal
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Comparison of multilabel classification models to forecast project dispute resolutions
Expert Systems with Applications: An International Journal
Implementing support vector regression with differential evolution to forecast motherboard shipments
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
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.