[1] Kotsiantis S, Kanellopoulos D, Pintelas P. Handling Imbalanced Datasets: A Review[J]. GESTS International Trans. on Computer Science and Engineering, 2006, 30(1): 25-36. [2] 杨 永, 王莉利. 基于K-means聚类和遗传算法的少数类样本采样方法研究[J]. 科学技术与工程, 2010, 10(10): 2334-2338. [3] Gao Jing, Fan Wei, Han Jiawei, et al. A General Framework for Mining Concept-drifting Data Streams with Skewed Distri- butions[C]//Proc. of SDM’07. Minneapolis, USA: [s. n.], 2007. [4] Gao Jing, Ding Bolin, Han Jiawei, et al. Classifying Data Streams with Skewed Class Distributions and Concept Drifts[J]. IEEE Internet Computing, 2008, 12(6): 37-49. [5] Chawla N, Bowyer K, Hall L, et al. SMOTE: Synthetic Minority Over-sampling Technique[J]. Journal of Arti?cial Intelligence Research, 2002, 16: 321-357. [6] Han Hui, Wang Wenyuan, Mao Binghuan. Borderline-SMOTE: A New Over-sampling Method in Imbalanced Data Sets Learn- ing[C]//Proc. of IEEE International Conference on Intelligent Computing. Hefei, China: [S. l.], 2005. [7] 王和勇, 樊泓坤, 姚正安. SMOTE和Biased-SVM 相结合的不平衡数据分类方法[J]. 计算机科学, 2008, 35(5): 174-176. [8] 韩 慧, 王 路, 温 明, 等. 不均衡数据集学习中基于初分类的过抽样算法[J]. 计算机应用, 2006, 26(8): 1894-1897. [9] 杜 娟, 衣治安, 周 颖. 基于聚类和遗传交叉的少数类样本生成方法[J]. 计算机工程, 2009, 35(22): 182-184. [10] Kang P, Cho S. EUS SVMs: Ensemble of Under-sampled SVMs for Data Imbalance Problems[C]//Proc. of ICONIP’06. Hong Kong, China: [s. n.], 2006.
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