南京大学学报(自然科学版) ›› 2023, Vol. 59 ›› Issue (6): 1003–1012.doi: 10.13232/j.cnki.jnju.2023.06.010
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改进边界分类的Borderline‑SMOTE过采样方法
马贺1, 宋媚1,2(
), 祝义1
- 1.江苏师范大学计算机科学与技术学院，徐州，221116
2.江苏师范大学管理科学与工程研究中心，徐州，221116
Improved Borderline⁃SMOTE oversampling method for boundary classification
He Ma1, Mei Song1,2(), Yi Zhu1
- 1.School of Computer Science and Technology, Jiangsu Normal University, Xuzhou, 221116, China
2.Management Science and Technology Center, Jiangsu Normal University, Xuzhou, 221116, China
摘要：
针对不平衡数据中类重叠区域易造成分类错误的问题，提出一种引入合成因子改进边界分类的Borderline?SMOTE过采样方法（IBSM）.首先根据少数类样本近邻分布情况找出处于边界的少数类样本，然后计算边界样本对应的合成因子，并根据其取值更新该样本需生成的样本数，最后在近邻中根据合成因子挑选距离最近的top?Z少数类样本进行新样本生成.将提出的方法与八种采样方法在KNN和SVM两种分类器、10个KEEL不平衡数据集上进行对比实验，结果表明，提出的方法在大部分数据集上的F1，G?mean，AUC （Area under Curve）均获得最优值，且F1与AUC的Friedman排名最优，证明所提方法和其余采样方法相比，在处理不平衡数据中的边界样本分类问题时有更好的表现，通过合成因子设定一定的约束条件与分配策略，可以为同类研究提供思路.
中图分类号:
- TP181
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