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

arXiv: https://arxiv.org/abs/2203.15753 · HTML: https://mlsanity.com/paper/2203.15753/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 122 references that are in the map.
- First posted on arXiv 29 Mar 2022.

## Builds on (most cited first, 17 of 17)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Tabular Data: Deep Learning is Not All You Need](https://mlsanity.com/paper/2106.03253/) (2021)
- [RuleMatrix: Visualizing and Understanding Classifiers with Rules](https://mlsanity.com/paper/1807.06228/) (2018)
- [A Survey of Visual Analytics Techniques for Machine Learning](https://mlsanity.com/paper/2008.09632/) (2020)
- [t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections](https://mlsanity.com/paper/2002.06910/) (2020)
- [Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics](https://mlsanity.com/paper/1907.07296/) (2019)
- [Topological Data Analysis of Decision Boundaries with Application to Model Selection](https://mlsanity.com/paper/1805.09949/) (2018)
- [StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metrics](https://mlsanity.com/paper/2005.01575/) (2020)
- [VisEvol: Visual Analytics to Support Hyperparameter Search through Evolutionary Optimization](https://mlsanity.com/paper/2012.01205/) (2020)
- [Evaluation of Sampling Methods for Scatterplots](https://mlsanity.com/paper/2007.14666/) (2020)
- [VisRuler: Visual Analytics for Extracting Decision Rules from Bagged and Boosted Decision Trees](https://mlsanity.com/paper/2112.00334/) (2021)
- [REsCUE: A framework for REal-time feedback on behavioral CUEs using multimodal anomaly detection](https://mlsanity.com/paper/1903.11485/) (2019)
- [FeatureEnVi: Visual Analytics for Feature Engineering Using Stepwise Selection and Semi-Automatic Extraction Approaches](https://mlsanity.com/paper/2103.14539/) (2021)
- [In Defence of Visual Analytics Systems: Replies to Critics](https://mlsanity.com/paper/2201.09772/) (2022)
- [Extreme Entropy Machines: Robust information theoretic classification](https://mlsanity.com/paper/1501.05279/) (2015)
- [Does the Layout Really Matter? A Study on Visual Model Accuracy Estimation](https://mlsanity.com/paper/2110.07188/) (2021)

## Built on by (most cited first, 5 of 5)

- [Stop Misusing t-SNE and UMAP for Visual Analytics](https://mlsanity.com/paper/2506.08725/) (2025)
- [Are We Closing the Loop Yet? Gaps in the Generalizability of VIS4ML Research](https://mlsanity.com/paper/2308.06290/) (2023)
- [Visual Analytics for Explainable and Trustworthy Artificial Intelligence](https://mlsanity.com/paper/2507.10240/) (2025)
- [DeforestVis: Behavior Analysis of Machine Learning Models with Surrogate Decision Stumps](https://mlsanity.com/paper/2304.00133/) (2023)
- [Standardness Clouds Meaning: A Position Regarding the Informed Usage of Standard Datasets](https://mlsanity.com/paper/2406.13552/) (2024)

## Abstract (arXiv)

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 classification performance. This paper introduces HardVis, a visual analytics system designed to handle instance hardness mainly in imbalanced classification scenarios. Our proposed system assists users in visually comparing different distributions of data types, selecting types of instances based on local characteristics that will later be affected by the active sampling method, and validating which suggestions from undersampling or oversampling techniques are beneficial for the ML model. Additionally, rather than uniformly undersampling/oversampling a specific class, we allow users to find and sample easy and difficult to classify training instances from all classes. Users can explore subsets of data from different perspectives to decide all those parameters, while HardVis keeps track of their steps and evaluates the model's predictive performance in a test set separately. The end result is a well-balanced data set that boosts the predictive power of the ML model. The efficacy and effectiveness of HardVis are demonstrated with a hypothetical usage scenario and a use case. Finally, we also look at how useful our system is based on feedback we received from ML experts.