Summary of Iife: Interaction Information Based Automated Feature Engineering, by Tom Overman et al.
IIFE: Interaction Information Based Automated Feature Engineering
by Tom Overman, Diego Klabjan, Jean Utke
First submitted to arxiv on: 7 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposes a novel algorithm for automated feature engineering (AutoFE), called IIFE, which leverages an information-theoretic perspective called interaction information to identify synergistic feature pairs. The authors demonstrate that IIFE outperforms existing algorithms and provide insights into how the same technique can improve existing AutoFE methods. Additionally, they highlight critical experimental setup issues in current AutoFE literature and their impact on performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new algorithm for automated feature engineering (AutoFE) called IIFE, which helps improve predictive performance. Traditional feature engineering requires expertise and testing, but this new method makes it easy and accessible to data scientists. The authors show that IIFE is better than other algorithms and explain how the same idea can help existing AutoFE methods. They also discuss problems with current experimental setups in AutoFE research. |
Keywords
» Artificial intelligence » Feature engineering
