Summary of Tabular Data Augmentation For Machine Learning: Progress and Prospects Of Embracing Generative Ai, by Lingxi Cui and Huan Li and Ke Chen and Lidan Shou and Gang Chen
Tabular Data Augmentation for Machine Learning: Progress and Prospects of Embracing Generative AI
by Lingxi Cui, Huan Li, Ke Chen, Lidan Shou, Gang Chen
First submitted to arxiv on: 31 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Databases (cs.DB)
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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 comprehensive review of tabular data augmentation (TDA) is presented, with a focus on generative AI. The TDA pipeline consists of three main procedures: pre-augmentation, augmentation, and post-augmentation. Pre-augmentation includes preparation tasks such as error handling, table annotation, and schema matching. Augmentation categorizes current methods into retrieval-based and generation-based approaches, analyzing their effects at the row, column, cell, and table levels. Post-augmentation discusses datasets, evaluation metrics, and optimization aspects of TDA. The review also summarizes current trends and future directions for TDA in the era of generative AI. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Tabular data augmentation (TDA) is a way to make more data available for machine learning models. This helps the models learn better from the data they have. Some people use external data, while others generate new synthetic data. The paper looks at all these different methods and how they work. It also talks about what kind of datasets are best suited for TDA and how we can make sure our models are using the data correctly. |
Keywords
» Artificial intelligence » Data augmentation » Machine learning » Optimization » Synthetic data
