An Early-Warning Model Based on Rough Sets and Svm for the Risk of Textile's Exports

In this paper, a hybrid intelligent system is applied to recognize the clusters of the risk of textile's export, combining rough set approach and support vector machine (SVM). We can get reduced information table with no information loss by rough set approach. And then, this reduced information is used to develop classification rules and train SVM to infer appropriate parameters. The rationale of our hybrid system is using rules developed by rough sets for an object that matches any of the rules and SVM for one that dose not match any of them. The effectiveness of our methodology was verified by experiments comparing BP neural networks with our approach.