HardVis: Visual Analytics to Handle Instance Hardness Using Undersampling and Oversampling Techniques. (arXiv:2203.15753v2 [cs.LG] UPDATED)

Despite the tremendous advances in machine learning (ML), training with
imbalanced data still poses challenges in many real-world applications. Among a
series of diverse techniques to solve this problem, sampling algorithms are
regarded as an efficient solution. However, the problem is more fundamental,
with many works emphasizing the importance of instance hardness. This issue
refers to the significance of managing unsafe or potentially noisy instances
that are more likely to be misclassified and serve as the root cause of poor …

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