Jun Ni

A systematic study of the class imbalance problem in convolutional neural networks

Artificial intelligence, bias and clinical safety

Diversity techniques improve the performance of the best imbalance learning ensembles

Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE

Learning from class-imbalanced data: Review of methods and applications

SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary

Mining and integrating reliable decision rules for imbalanced cancer gene expression data sets