Combining integrated sampling with SVM ensembles for learning from imbalanced datasetsYANG LIU; XIAOHUI YU; JIMMY XIANGJI HUANG et al.Information processing & management. 2011, Vol 47, Num 4, pp 617-631, issn 0306-4573, 15 p.Article

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KBA : Kernel boundary alignment considering imbalanced data distributionGANG WU; CHANG, Edward Y.IEEE transactions on knowledge and data engineering. 2005, Vol 17, Num 6, pp 786-795, issn 1041-4347, 10 p.Article

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Learning SVM with weighted maximum margin criterion for classification of imbalanced dataZHUANGYUAN ZHAO; PING ZHONG; YAOHONG ZHAO et al.Mathematical and computer modelling. 2011, Vol 54, Num 3-4, pp 1093-1099, issn 0895-7177, 7 p.Article

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