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View all- Zhu MXia JJin XYan MCai GYan JNing G(2018)Class Weights Random Forest Algorithm for Processing Class Imbalanced Medical DataIEEE Access10.1109/ACCESS.2018.27894286(4641-4652)Online publication date: 2018
Random Forests are considered for classification of multisource remote sensing and geographic data. Various ensemble classification methods have been proposed in recent years. These methods have been proven to improve classification accuracy ...
Classification of imbalanced data is an important research problem as most of the data encountered in real world systems is imbalanced. Recently a representation learning technique called Synthetic Minority Over-sampling Technique (SMOTE) has been ...
Class imbalance is a crucial problem in machine learning and occurs in many domains. Specifically, the
two-class problem has received interest from researchers in recent years, leading to solutions for oil spill
detection, tumour discovery and ...
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