Challenges and limitations of synthetic minority oversampling techniques in machine learning  

作　　者：Ibraheem M Alkhawaldeh Ibrahem Albalkhi Abdulqadir Jeprel Naswhan 

机构地区：[1]Faculty of Medicine,Mutah University,Karak 61710,Jordan [2]Department of Neuroradiology,Alfaisal University,Great Ormond Street Hospital NHS Foundation Trust,London WC1N 3JH,United Kingdom [3]Nursing for Education and Practice Development,Hamad Medical Corporation,Doha 3050,Qatar

出　　处：《World Journal of Methodology》2023年第5期373-378,共6页世界方法学杂志

摘　　要：Oversampling is the most utilized approach to deal with class-imbalanced datasets,as seen by the plethora of oversampling methods developed in the last two decades.We argue in the following editorial the issues with oversampling that stem from the possibility of overfitting and the generation of synthetic cases that might not accurately represent the minority class.These limitations should be considered when using oversampling techniques.We also propose several alternate strategies for dealing with imbalanced data,as well as a future work perspective.

关 键 词：Machine learning Class imbalance OVERFITTING MISDIAGNOSIS 

分 类 号：TP1[自动化与计算机技术—控制理论与控制工程]