摘要
【目的】解决二分类任务中因类间数据不平衡导致少数类分类准确度低的问题。【方法】提出一种基于模糊C-均值聚类的欠采样集成不平衡数据分类算法(ECFCM),即对多数类样本进行基于FCM聚类的欠采样,将聚类中心样本与全部少数类样本组成平衡数据集;利用基于Bagging的集成学习算法对平衡数据集进行分类。【结果】在4组不平衡数据集上的Matlab仿真实验结果表明,ECFCM算法的Acc、AUC和F_1提升幅度最高为5.75%(Spambase), 13.84%(Glass2)和7.54%(Spambase)。【局限】本文采用标准数据集验证ECFCM算法的有效性,当采用实际应用中的不平衡数据时,需要有针对性地研究不平衡数据分类算法。【结论】ECFCM算法分类性能良好,在一定程度上有利于提高不平衡数据中少数类的分类准确度。
[Objective] This paper tries to solve the problem of the low accuracy of minority classification in the binary classification task due to class imbalance.[Methods] An under-sampling ensemble classification algorithm based on fuzzy c-means(FCM) clustering for imbalanced data is proposed.That is,the majority class samples are under-sampled based on FCM clustering,all these cluster center samples and all the minority samples are made up to a balance data set.We use the integrated learning algorithm based on Bagging to classify the balanced data sets.[Results] The Matlab simulation results of experiments on four imbalanced datasets show that the ECFCM algorithm improves Acc,AUC and F1 by up to 5.75%,13.84% and 7.54%.[Limitations] Some standard data sets are used to verify the effectiveness of ECFCM.When in a specific application,a targeted research on classification algorithm is needed.[Conclusions] The ECFCM algorithm performs good to a certain extent,which is conducive to improve the binary classification accuracy of the minority class on imbalanced datasets.
作者
肖连杰
郜梦蕊
苏新宁
Xiao Lianjie;Gao Mengrui;Su Xinning(School of Information Management,Nanjing University,Nanjing 210023,China;Jiangsu Key Laboratory of Data Engineering and Knowledge Service,Nanjing 210023,China)
出处
《数据分析与知识发现》
CSSCI
CSCD
北大核心
2019年第4期90-96,共7页
Data Analysis and Knowledge Discovery
基金
国家社会科学基金重大项目"情报学学科建设与情报工作未来发展路径研究"(项目编号:17ZDA291)
南京大学研究生跨学科科研创新项目"大数据环境下情报学理论方法知识库构建研究"(项目编号:2018ZDW03)的研究成果之一
关键词
不平衡数据
模糊C-均值聚类
分类
欠采样
集成学习
Imbalanced Data
Fuzzy C-Means Clustering
Classification
Under-sampling
Ensemble Learning
