Summary of Feature Graphs For Interpretable Unsupervised Tree Ensembles: Centrality, Interaction, and Application in Disease Subtyping, by Christel Sirocchi et al.
Feature graphs for interpretable unsupervised tree ensembles: centrality, interaction, and application in disease subtyping
by Christel Sirocchi, Martin Urschler, Bastian Pfeifer
First submitted to arxiv on: 27 Apr 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 study explores the role of feature selection in enhancing interpretability in unsupervised random forests, particularly in high-stakes domains like healthcare. While random forests excel at predicting outcomes in tabular datasets, their black-box nature makes it difficult to understand the reasoning behind predictions. Feature selection can help identify crucial input features, but most studies focus on supervised settings. This research proposes novel methods for constructing feature graphs from unsupervised random forests and selecting effective feature combinations. The feature graphs are built by capturing parent-child node splits within trees, which enables feature centrality to reflect relevance to clustering tasks. Edge weights represent the discriminating power of feature pairs. The proposed approach is evaluated on synthetic and benchmark datasets, showing its ability to reduce dimensionality while improving clustering performance and enhancing model interpretability. This method has potential applications in real-world biomedical settings, such as disease subtyping. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps make artificial intelligence more understandable by finding the most important features in big datasets. Right now, some AI models are really good at predicting things, but it’s hard to know why they’re making those predictions. This research makes these black-box models more transparent by identifying which input features matter most. The team develops new methods for creating graphs that show how different features relate to each other and how important they are. They test this approach on fake data and real-world datasets, showing it can help reduce the number of features while improving performance and making AI more understandable. |
Keywords
» Artificial intelligence » Clustering » Feature selection » Supervised » Unsupervised
