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...

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التفاصيل البيبلوغرافية

الحاوية / القاعدة:	International Journal of Computational Intelligence Systems
المؤلفون الرئيسيون:	Ahmed Saad Hussein, Tianrui Li, Chubato Wondaferaw Yohannese, Kamal Bashir
التنسيق:	مقال
اللغة:	الإنجليزية
منشور في:	Springer 2019-11-01
الموضوعات:	Imbalanced datasets SMOTE Machine learning Oversampling Undersampling
الوصول للمادة أونلاين:	https://www.atlantis-press.com/article/125924019/view