摘要For oversampling algorithms based on nearest neighbor values, such as synthetic
minority oversampling technology, when dealing with imbalances in data categories, cannot …

针对SMOTE (syntheticminorityover samplingtechnique) 等基于近邻值的传统过采样算法在
处理类不平衡数据时近邻参数不能根据少数类样本的分布及时调整的问题, 提出邻域自适应 …

在不平衡数据集中, 过抽样算法如Smote (Synthetic Minority Oversampling) 算法, R-Smote
算法与SD-ISmote 算法可能会模糊多数类与少数类的边界以及使用噪声数据合成新样本 …

针对SMOTE (synthetic minority over-sampling technique) 在合成少数类新样本时存在的不足,
提出了一种改进的SMOTE 算法GA-SMOTE. 该算法的关键将是遗传算法中的3 …

Synthetic minority oversampling technique (SMOTE) has become one of the mainstream
methods for dealing with unbalanced data due to its ability to effectively deal with minority …

In Synthetic Minority Over-sampling TEchnique (SMOTE), noise samples may participate in
the synthesis of new samples, so it is difficult to guarantee the rationality of the new samples …

One solution to deal with class imbalance is to modify its class distribution. Synthetic over-
sampling is a well-known method to modify class distribution by generating new synthetic …

针对SMOTE (Synthetic Minority Over-sampling Technique) 等传统过采样算法存在的忽略类内
不平衡, 扩展少数类的分类区域以及合成的新样本高度相似等问题, 基于综合考虑类内不平衡和 …

The synthetic minority oversampling technique (SMOTE) has emerged as a significant
approach to address class imbalance challenges in machine learning. However, the …

In order to handle the class imbalance problem, synthetic data generation methods such as
SMOTE, ADASYN, and Borderline-SMOTE have been developed. These methods use a …