Imbalanced Classification Based on Minority Clustering Synthetic Minority Oversampling Technique With Wind Turbine Fault Detection Application

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...

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Veröffentlicht in:	IEEE transactions on industrial informatics Jg. 17; H. 9; S. 5867 - 5875
Hauptverfasser:	Yi, Huaikuan, Jiang, Qingchao, Yan, Xuefeng, Wang, Bei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.09.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Blades Classification Classification algorithms Clustering Clustering algorithms Fault detection imbalanced data Informatics Interpolation K-means algorithm Machine learning Oversampling Partitioning algorithms synthetic minority oversampling technique (SMOTE) Turbine blades wind turbine fault detection Wind turbines
ISSN:	1551-3203, 1941-0050
Online-Zugang:	Volltext
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