Summary of Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies, by Qi Liu and Wanjing Ma
Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies
by Qi Liu, Wanjing Ma
First submitted to arxiv on: 24 Dec 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 This paper explores how real-world machine learning models are affected by corrupted data, such as missing or noisy values. The study focuses on two applications: natural language processing for text classification (NLP-SL) and optimizing traffic signals using deep reinforcement learning (Signal-RL). The authors investigate the impact of varying levels of data corruption on model performance, test the effectiveness of imputation methods to fix corrupt data, and evaluate whether increasing dataset size can help alleviate the issue. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper studies how corrupted data affects machine learning models. Researchers looked at two cases: natural language processing for text classification (NLP-SL) and optimizing traffic signals using deep reinforcement learning (Signal-RL). They found out that corrupting data makes models perform worse, and tried to fix this by using special methods to make the data better. They also checked if having more training data helps. |
Keywords
» Artificial intelligence » Machine learning » Natural language processing » Nlp » Reinforcement learning » Text classification
