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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Bibliographic Details
Published in:IEEE transactions on industrial informatics Vol. 17; no. 9; pp. 5867 - 5875
Main Authors: Yi, Huaikuan, Jiang, Qingchao, Yan, Xuefeng, Wang, Bei
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.09.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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 Access:Get full text
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Summary: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.
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ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2020.3046566