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

• Published in: Scientific programming Vol. 2021; pp. 1 - 18
• Main Authors: Wang, Jia-Bao, Zou, Chun-An, Fu, Guang-Hui
• Format: Journal Article
• Language: English
• Published: New York Hindawi 13.05.2021; John Wiley & Sons, Inc
• Subjects: Accuracy; Algorithms; Classification; Collinearity; Datasets; Interpolation; Learning; Machine learning; Methods; Oversampling; Weight

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...