Summary of Partial Information Decomposition For Data Interpretability and Feature Selection, by Charles Westphal et al.
Partial Information Decomposition for Data Interpretability and Feature Selection
by Charles Westphal, Stephen Hailes, Mirco Musolesi
First submitted to arxiv on: 29 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Information Theory (cs.IT)
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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 introduces Partial Information Decomposition of Features (PIDF), a novel approach for simultaneous data interpretability and feature selection. Unlike traditional methods, PIDF assigns three importance metrics per feature: mutual information with the target variable, contribution to synergistic information, and redundant information. This paradigm reveals not only feature correlations but also additional and overlapping information provided by combining features. The authors evaluate PIDF using synthetic and real-world data, demonstrating its potential applications and effectiveness in case studies from genetics and neuroscience. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to understand how different pieces of information are connected. It’s called Partial Information Decomposition of Features (PIDF). Instead of just looking at how each piece of information relates to the whole, PIDF looks at how they work together too. This helps us learn more from our data and make better decisions. |
Keywords
» Artificial intelligence » Feature selection
