HardVis: Visual Analytics to Handle Instance Hardness Using Undersampling and Oversampling Techniques

arXiv:2203.15753v4 Announce Type: replace
Abstract: 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 …

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