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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2021-01-01
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| Series: | Scientific Programming |
| Online Access: | http://dx.doi.org/10.1155/2021/9947621 |