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参考文献
[1]He Haibo,Garcia E A.Learning from Imbalanced Data[J].IEEE Transactions on Knowledge and Data Engineering,2009,21(9):1263-1284.
[2]叶志飞,文益民,吕宝粮.不平衡分类问题研究综述[J].智能系统学报,2009,4(2):148-156.
[3]张银峰,郭华平,职为梅.一种面向不平衡数据分类的组合剪枝方法[J].计算机工程,2014,40(6):157-161.
[4]曹鹏,栗伟,赵大哲.面向不均衡数据集的ARSGOS算法[J].小型微型计算机系统,2014,35(4):818-823.
[5]Chawla N V,Bowyer K W,Hall L O,et al.SMOTE:Synthetic Minority Over-sampling Technique[J].Journal of Artificial Intelligence Research,2002,16:321-357.
[6]Chen Shen.He Haibo,Garcia E A.RAMO Boost:Ranked Minority Oversampling in Boosting[J].IEEE Transac-tions on Neural Networks,2010,21(10):1624-1642.
[7]Cao Peng,Zhao Dazhe,Zaiane O.An Optimized Cost-sensitive SVM for Imbalanced Data Learning[C]//Proceedings of the 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining.Gold Coast,Australia:[s.n.],2013:280-292.
[8]Zhou Zhihua,Liu Xuying.Training Cost-sensitive Neural
Networks with Methods Addressing the Class Imbalance Problem[J].IEEE Transactions on Knowledge and Data Engineering,2006,18(1):63-77.
[9]Masnadi H,Vasconcelos N,Iranmehr A.Cost-sensitive Support Vector Machines[J].Journal of Machine Learning Research,2015,1(1):1-26.
[10]Wang B X,Japkowicz N.Boosting Support Vector Machines for Imbalanced Data Sets[J].Knowledge and Information Systems,2010,25(1):1-20.
[11]Cao Peng,Zhao Dazhe,Zaiane O.Hybrid Probabilistic Sampling with Random Subspace for Imbalanced Data Learning[J].Intelligent Data Analysis,2014,18(6):1089-1108.
[12]Thongkam J,Xu Guandong,Zhang Yanchun,et al.Support Vector Machine for Outlier Detection in Breast Cancer Survivability Prediction[C]//Proceedings of Asia-Pacific Web Conference.Berlin,Germany:Springer,2008:99-109.
[13]Debruyne M.An Outlier Map for Support Vector Machine Classification[J].Annals of Applied Statistics,2009,3(4):1566-1580.
[14]Batuwita R,Palade V.FSVM-CIL:Fuzzy Support Vector Machines for Class Imbalance Learning[J].IEEE Transactions on Fuzzy Systems,2010,18(3):558-571.
[15]刘三阳,杜喆.一种改进的模糊支持向量机算法[J].智能系统学报,2007,2(3):30-33.
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