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

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
Main Authors:	Jia-Bao Wang, Chun-An Zou, Guang-Hui Fu
Format:	Article
Language:	English
Published:	Hindawi Limited 2021-01-01
Series:	Scientific Programming
Online Access:	http://dx.doi.org/10.1155/2021/9947621

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