机译:基于演化欠采样的装袋集成方法用于不平衡数据分类
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016,China,National Key Lab of ATFM, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016,China,National Key Lab of ATFM, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016,China;
National Key Lab of ATFM, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;
class imbalanced problem; under-sampling; bagging; evolutionary under-sampling; ensemble learning; machine learning; data mining;
机译:基于信息颗粒的下采样方法设计在不平衡数据分类中
机译:KA-Ensemble:迈向非衡度的图像分类合并,在抽样和过度采样
机译:基于聚类的下抽样的组合,并在智能系统中解决不平衡类问题的组合方法
机译:使用欠采样技术通过投票,装袋和Adaboost集成方法改进不平衡学生数据集的分类
机译:具有集合决策树方法的分数随机加权自动启动,用于分类数据
机译:一种用于不平衡数据学习的新型集成方法:外推-SMOTE SVM套袋
机译:基于集合和欠采样的不平衡数据分类研究