Summary of Synthetic Tabular Data Generation For Class Imbalance and Fairness: a Comparative Study, by Emmanouil Panagiotou et al.
Synthetic Tabular Data Generation for Class Imbalance and Fairness: A Comparative Study
by Emmanouil Panagiotou, Arjun Roy, Eirini Ntoutsi
First submitted to arxiv on: 8 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A new study tackles the problem of biased Machine Learning (ML) models by exploring methods to mitigate class and group imbalances in classification tasks. The researchers analyze state-of-the-art synthetic tabular data generation models and sampling strategies to determine their effectiveness in reducing bias. Experimental results on four datasets show that generative models can be a promising approach for bias mitigation, paving the way for further exploration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new study is trying to make sure machine learning models don’t get biased by using different kinds of fake data. This fake data helps balance out real-world data problems where some groups are underrepresented. The researchers tested many different approaches and found that one type of fake data, called generative models, works really well at reducing bias. This could lead to more fair and accurate machine learning models in the future. |
Keywords
» Artificial intelligence » Classification » Machine learning
