传统的分类算法在对不平衡数据进行分类时, 容易导致少数类被错分. 为了提高少数类样本的
分类准确度, 提出了一种基于改进密度峰值聚类的采样算法IDP SMOTE. 首先, 采用Box cox …

For data processing methods, various fields will encounter different problems, and
unbalanced data is a more difficult subject. At present, academia has under-sampling for the …

针对分类器在不均衡数据集上对小类分类准确率较差的问题, 提出了改进K-means
的双向采样算法KMBS (k-means bi-directional sampling), 并将集成学习应用在分类算法上 …

This paper presents a novel adaptive resampling algorithm based on the clustering by fast
search and find of density peaks (CFSFDP) algorithm and the synthetic minority …

For the bottleneck of improving the accuracy of minority class samples within the paradigm
of imbalanced datasets, a novel under-sampling method based on the cooperative co …

针对偏斜数据集的分类问题, 提出一种改进的少数类样本过抽样算法(B-ISMOTE).
在边界少数类实例及其最近邻实例构成的n 维球体空间内进行随机插值, 以此产生虚拟少数类 …

In data mining classification, class imbalance is characterized that different classes have an
obvious difference in the number of samples. Most classifiers typically assume a balanced …