计算机与数字工程2024，Vol.52Issue(5)：1275-1281,1330,8.DOI:10.3969/j.issn.1672-9722.2024.05.003
基于OCkNN+ENN的过采样算法研究
Research on Oversampling Algorithm Based on OCkNN+ENN
摘要
Abstract
Class imbalance learning(CIL)is one of the hot topics in the field of machine learning(ML).Among the CIL meth-ods,SMOTE is considered as one of the benchmark algorithms.Although the SMOTE algorithm performs well on most of the class imbalance datasets,it has some problems,such as generating noise interference and noise propagation.Based on the study of SMOTE variants,a more robust and general algorithm is proposed,which is ONE-SMOTE.That method can use edited nearest neighbor(ENN)to clean data and filter noise,then use one-class(OCkNN)to detect the relative density distribution information of the sample.And the relative density position and boundary of each sample can be precisely located that will be used for oversam-pling.The experimental results show that the algorithm can effectively improve the accuracy rate of data classification.关键词
类不平衡学习/SMOTE/ENN/OCkNN/相对密度分布信息Key words
class imbalance learning/SMOTE/ENN/OCkNN/relative density distribution information分类
信息技术与安全科学引用本文复制引用
张爱民,于化龙..基于OCkNN+ENN的过采样算法研究[J].计算机与数字工程,2024,52(5):1275-1281,1330,8.基金项目
国家自然科学基金项目(编号:62176107) （编号:62176107）
江苏省自然科学基金项目(编号:BK20191457)资助. （编号:BK20191457）
