A-SMOTE: A New Preprocessing Approach for Highly Imbalanced Datasets by Improving SMOTE

Imbalance learning is a challenging task for most standard machine learning algorithms. The Synthetic Minority Oversampling Technique (SMOTE) is a well-known preprocessing approach for handling imbalanced datasets, where the minority class is oversampled by producing synthetic examples in feature ve...

詳細記述

書誌詳細

出版年:	International Journal of Computational Intelligence Systems
主要な著者:	Ahmed Saad Hussein, Tianrui Li, Chubato Wondaferaw Yohannese, Kamal Bashir
フォーマット:	論文
言語:	英語
出版事項:	Springer 2019-11-01
主題:	Imbalanced datasets SMOTE Machine learning Oversampling Undersampling
オンライン･アクセス:	https://www.atlantis-press.com/article/125924019/view