A new over-sampling approach: random-SMOTE for learning from imbalanced data sets
Y Dong, X Wang - … and Management: 5th International Conference, KSEM …, 2011 - Springer
For imbalanced data sets, examples of minority class are sparsely distributed in sample
space compared with the overwhelming amount of majority class. This presents a great …
space compared with the overwhelming amount of majority class. This presents a great …
基于BL-SMOTE 和随机森林的不平衡数据分类
张宸宁， 李国成 - 北京信息科技大学学报: 自然科学版, 2019 - cqvip.com
不平衡数据在信用评估, 财务造假, 医疗诊断等现实应用中广泛存在. 在众多处理不平衡数据的
算法中, SMOTE 算法(synthetic minority over-sampling technique) 应用最为广泛 …
算法中, SMOTE 算法(synthetic minority over-sampling technique) 应用最为广泛 …
[PDF][PDF] Adaptive neighbor synthetic minority oversampling technique under 1NN outcast handling.
W Siriseriwan, K Sinapiromsaran - Songklanakarin Journal of …, 2017 - thaiscience.info
SMOTE is an effective oversampling technique for a class imbalance problem due to its
simplicity and relatively high recall value. One drawback of SMOTE is a requirement of the …
simplicity and relatively high recall value. One drawback of SMOTE is a requirement of the …
Lvq-smote–learning vector quantization based synthetic minority over–sampling technique for biomedical data
M Nakamura, Y Kajiwara, A Otsuka, H Kimura - BioData mining, 2013 - Springer
Background Over-sampling methods based on Synthetic Minority Over-sampling Technique
(SMOTE) have been proposed for classification problems of imbalanced biomedical data …
(SMOTE) have been proposed for classification problems of imbalanced biomedical data …
SPAW-SMOTE: Space Partitioning Adaptive Weighted Synthetic Minority Oversampling Technique For Imbalanced Data Set Learning
Q Zhang, J He, T Li, X Lan, W Fang… - The Computer Journal, 2024 - academic.oup.com
The problem of data imbalance is common in reality, which greatly affects the performance
of classifiers. Most of the solutions are to balance the data set by generating new minority …
of classifiers. Most of the solutions are to balance the data set by generating new minority …
L-SMOTE 与SVM 结合的不平衡数据集分类研究
罗康洋， 王国强 - 计算机工程与应用, 2019 - cqvip.com
针对不平衡数据集的低分类效率, 基于L-SMOTE 算法和混合核SVM 提出了一种改进的SMOTE
算法(FTLSMOTE). 利用混合核SVM 对数据集进行分类. 提出了噪声样本识别三原则对噪声样本 …
算法(FTLSMOTE). 利用混合核SVM 对数据集进行分类. 提出了噪声样本识别三原则对噪声样本 …
基于K 近邻的过抽样算法在不平衡的医学资料中的应用
周舒冬， 张磊， 李丽霞 - 中国卫生统计, 2008 - cqvip.com
目的介绍一种基于K 近邻的过抽样算法在不平衡的医学数据集分类中的应用. 方法首先利用K
近邻法删除在分类中容易与少数类混淆的多数类样本; 再对新生成的训练集利用SMOTE …
近邻法删除在分类中容易与少数类混淆的多数类样本; 再对新生成的训练集利用SMOTE …
SMOTE-ENC: A novel SMOTE-based method to generate synthetic data for nominal and continuous features
M Mukherjee, M Khushi - Applied system innovation, 2021 - mdpi.com
Real-world datasets are heavily skewed where some classes are significantly outnumbered
by the other classes. In these situations, machine learning algorithms fail to achieve …
by the other classes. In these situations, machine learning algorithms fail to achieve …