中文摘要：

准确识别电子商务信用风险,有利于提高企业风险防范能力,减少损失.建立了基于粗糙集（RS）、遗传算法（GA）和支持向量机（SVM）的电子商务信用风险分类模型（RS-GA-SVM）.首先,利用RS对分类指标进行约简,选择出电子商务信用风险关键影响因素.其次,采用GA算法优化SVM模型参数,并应于电子商务信用风险分类.最后,实证表明,RS-GA-SVM模型具有高的分类精度和分类效率。

英文摘要：

Accurate identification of e-commerce credit risk classification is helpful for improving capacity of enterprise risk prevention, so as to reduce the loss. This paper proposes an integrated model （RS-GA-SVM） of rough set （RS）, genetic algorithm （GA） and support vector machine （SVM） for E-commerce credit risk classification. Firstly, RS is used to choose the key factors of E-commerce credit risk classification. Secondly, the parameters of SVM model are optimized by using GA algorithm, which is used for E-commerce credit risk classification. Finally, the empirical results show that the proposed RS-GA-SVM model has higher classification accuracy and classification efficiency.