中文摘要：

针对合成少数类过采样（synthetic minority over-sampling technique, SMOTE）方法在提升支持向量机（support vector machine, SVM）的非均衡样本学习能力中出现的过拟合（over fitting），引入自适应合成抽样方法（adaptive synthetic sampling approach, ADASYN）和逐级优化递减欠采样方法（optimization of decreasing reduction, ODR）分别克服SMOTE在生成新样本中的盲目性和在处理对象上的局限性，进而与SVM相结合，构造出改进SVM，即ODR-ADASYN—SVM模型来预测中国极端金融风险；最后运用T检验对各模型预测精度的差异性进行显著性检验以及对各模型的预测稳定性进行评价．实证结果表明，ODR—ADASYN-SVM模型不仅能够显著地提升SVM的非均衡样本学习能力，同时也能够有效地克服SMOTE的过拟合，从而展示出优越的极端金融风险预测性能．

英文摘要：

Synthetic minority over-sampling technique （SMOTE） has the problem of over fitting in improving the imbalanced samples＇ learning ability of support vector machine （SVM）. In this paper, adaptive synthetic sampling approach （ADASYN） and optimization of decreasing reduction approach （ODR） are assembled into an ODR-ADASYN to overcome the blindness in generating new samples and the limitations in processing the object. Combining SVM with ODR-ADASYN, an improved SVM, named ODR-ADASYN-SVM, is put forward to predict extremely financial risks; T-test is also applied to the significance test of the difference of the predic- tion accuracy of all models and to the evaluation of the prediction stability of all models. The result illustrates that the ODR-ADASYN-SVM can not only significantly improve the imbalanced samples＇ learning ability of SVM, but also overcome the problem of over fitting for SMOTE effectively. Hence, the ODR-ADASYN-SVM has a superior ability to predict extremely financial risks.