AWSMOTE: An SVM-Based Adaptive Weighted SMOTE for Class-Imbalance Learning

In class-imbalance learning, Synthetic Minority Oversampling Technique (SMOTE) is a widely used technique to tackle class-imbalance problems from the data level, whereas SMOTE blindly selects neighboring minority class points when performing an interpolation among them and inevitably brings collinea...

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Bibliographic Details
Published inScientific programming Vol. 2021; pp. 1 - 18
Main Authors Wang, Jia-Bao, Zou, Chun-An, Fu, Guang-Hui
Format Journal Article
LanguageEnglish
Published New York Hindawi 13.05.2021
John Wiley & Sons, Inc
Subjects
Accuracy
Algorithms
Classification
Collinearity
Datasets
Interpolation
Learning
Machine learning
Methods
Oversampling
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Summary:In class-imbalance learning, Synthetic Minority Oversampling Technique (SMOTE) is a widely used technique to tackle class-imbalance problems from the data level, whereas SMOTE blindly selects neighboring minority class points when performing an interpolation among them and inevitably brings collinearity between the generated new points and the original ones. To combat these problems, we propose in this study an adaptive-weighting SMOTE method, termed as AWSMOTE. AWSMOTE applies two types of SVM-based weights into SMOTE. A kind of weight is used in variable space to combat the drawbacks of collinearity, while another weight is utilized in sample space to purposefully choose those support vectors from the minority class as the neighboring points in the interpolation. AWSMOTE is compared with SMOTE and its improved versions with six simulated datasets and 22 real-world datasets. The results demonstrate the effectiveness and advantages of the proposed approach.
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ISSN:1058-9244
1875-919X
DOI:10.1155/2021/9947621