A Comprehensive Analysis of Synthetic Minority Oversampling Technique (SMOTE) for handling class imbalance

2019/07/19 by Dina Elreedy, Amir F. Atiya · 765 citations
Computer Science · Decision Sciences · Engineering · Mathematics · #Advanced Statistical Process Monitoring #Artificial intelligence #Bandwidth (computing) #Class (philosophy) #Computer science #Data mining #Economic shortage #Electricity Theft Detection Techniques #Imbalanced Data Classification Techniques #Machine learning #Mathematics #Oversampling #Point (geometry) #Set (abstract data type)

paper · doi:10.1016/j.ins.2019.07.070

published in Information Sciences 505, 32-64 (Elsevier BV)

openalex publication_date 2019/07/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31

Citations

• On Information and Sufficiency
• An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics
• Oversampling for Imbalanced Learning Based on K-Means and SMOTE

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