影响因子：6.5
DOI码：10.1016/j.neucom.2024.129233
所属单位：哈尔滨工程大学船舶工程学院
发表刊物：Neurocomputing
刊物所在地：荷兰
项目来源：National Natural Science Foundation of China (Nos. 71503108, 62077029), the Jiangsu Provincial Educa
关键字：Imbalance data; Intra-class imbalance; Cluster; Over-sampling
摘要：An over-sampling method, SD-CSMOTE, is proposed to address the problem of intra-class imbalance in data. First, the minority samples are clustered via the shared nearest neighbour-density peak clustering method. The sample density within each cluster is then determined using the kernel density estimation method. Thereafter total number of samples to be generated in each cluster is calculated. Finally, two samples are randomly selected from the cluster on the basis of the cluster centre, and a new sample is created at the centroid of the three samples. The proposed method is compared with 10 different sampling methods through extensive tests on three classifiers and 10 publicly imbalanced datasets. The results demonstrate that the proposed method achieves optimal F1 value, G-mean, and AUC on most of the datasets, and the Friedman rankings are observed to be optimal across multiple classifiers. It is confirmed that the proposed method performs better than other sampling methods in resolving the intra-class imbalance problem. This method also provides a new way to address issues such as small disjuncts and data imbalance.
备注：National Natural Science Foundation of China (Nos. 71503108, 62077029), the Jiangsu Provincial Education Science Planning Project (B-b/2024/01/47), the CCF-Huawei Innovation Research Program Grant (CCF-HuaweiFM202209), and the Research and Practice Innovation Project of Jiangsu Normal University (2022XKT1540, 2024XKT2592).
合写作者：Xu Zhang,祝义,洪维强
第一作者：He Ma
论文类型：期刊论文
通讯作者：宋媚
论文编号：129233
学科门类：工学
文献类型：J
卷号：620
ISSN号：0925-2312
是否译文：否
发表时间：2025
收录刊物：SCI
发布期刊链接：https://www.sciencedirect.com/science/article/pii/S0925231224020046?via%3Dihub