SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary
The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is
considered" de facto" standard in the framework of learning from imbalanced data. This is …
considered" de facto" standard in the framework of learning from imbalanced data. This is …
Learning from class-imbalanced data: Review of methods and applications
G Haixiang, L Yi**g, SK Lim, Y Loo, NT Tran, NM Cheung… - … conference on data …, 2018 - ieeexplore.ieee.org
Recently, the introduction of the generative adversarial network (GAN) and its variants has
enabled the generation of realistic synthetic samples, which has been used for enlarging …
enabled the generation of realistic synthetic samples, which has been used for enlarging …
A regularized ensemble framework of deep learning for cancer detection from multi-class, imbalanced training data
In medical diagnosis, eg bowel cancer detection, a large number of examples of normal
cases exists with a much smaller number of positive cases. Such data imbalance usually …
cases exists with a much smaller number of positive cases. Such data imbalance usually …