Anti-Dumping Early-Warning Model Based on Rough Sets and Neuro-FDT
Zhao Jianna, Xu Zhao · 2007
In this paper, a new anti-dumping early-warning system for the export of China's textile products is presented. The early-warning system based on Rough sets and neuro-fuzzy decision tree modelling method, which is different from traditional modelling methods. We can get reduced information table by rough set, which implies that the number of index and qualitative variables is reduced with no information loss through rough set approach. And then, neural networks-fuzzy decision tree (a fuzzy decision tree structure with neural like parameter adaptation strategy) improves FDT's classification accuracy and carry through anti- dumping early-warning more accuracy. The other new attempt is the setting of early-warning intervals. The result of the positive research indicated that this system is very valid for anti-dumping prediction and it will have a good application prospect in this area.