文献类型：期刊文献

英文题名：Imbalanced Classification Based on Minority Clustering Synthetic Minority Oversampling Technique With Wind Turbine Fault Detection Application

作者：Yi, Huaikuan[1,2];Jiang, Qingchao[1,2];Yan, Xuefeng[1,2];Wang, Bei[3]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[2]Tongji Univ, Shanghai Inst Intelligent Sci & Technol, Shanghai 200092, Peoples R China;[3]Shanghai Elect Wind Power Grp, Shanghai 200233, Peoples R China

年份：2021

卷号：17

期号：9

起止页码：5867

外文期刊名：IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

收录：;EI(收录号：20210209735936);WOS:【SCI-EXPANDED(收录号:WOS:000663538800002)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 61973119, in part by Shanghai Rising-Star Program under Grant 20QA1402600, and in part by the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017.

语种：英文

外文关键词：Classification; imbalanced data; K-means algorithm; synthetic minority oversampling technique (SMOTE); wind turbine fault detection

摘要：Synthetic minority oversampling technique (SMOTE) has been widely used in dealing with the imbalance classification problem in the machine learning field. However, classical SMOTE implements the oversampling by linear interpolation between adjacent minority class samples, which may fail to consider the uneven distribution of the samples. This article proposes a minority clustering SMOTE (MC-SMOTE) method that involves the clustering of minority class samples to improve the imbalance classification performance. First, samples from the minority class are clustered into several clusters. Second, oversampling is performed by linear interpolation between adjacent clusters to create new samples from different clusters that contain additional information of the entire minority class. Then classical classification techniques can be employed to achieve efficient classification. The superiority of the MC-SMOTE is first verified by experiments on some benchmark datasets from various application domains. The proposed method is then applied to the real industrial SCADA data of wind turbine blade icing. Classification results indicate that the MC-SMOTE exhibits a better performance than that of the classical SMOTE.