Summary of Iterative Feature Exclusion Ranking For Deep Tabular Learning, by Fathi Said Emhemed Shaninah et al.
Iterative Feature Exclusion Ranking for Deep Tabular Learning
by Fathi Said Emhemed Shaninah, AbdulRahman M. A. Baraka, Mohd Halim Mohd Noor
First submitted to arxiv on: 21 Dec 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 The proposed study tackles the limitation of existing deep learning models for tabular data, which fail to capture contextual feature interactions and overlook high-impact features. To address this, an iterative feature exclusion module is introduced, which iteratively excludes each feature from the input data and computes attention scores representing their impact on predictions. The refined representation of feature importance captures both global and local interactions between features. Evaluation on four public datasets shows that the proposed module outperforms state-of-the-art methods and baseline models in feature ranking and classification tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Tabular data is a common way to store information, but deep neural networks aren’t great at working with it. Some researchers have developed special models for tabular data, but they don’t account for how features interact with each other. This study proposes a new module that helps identify the most important features and captures these interactions. The module iteratively removes features one by one and sees how much impact they have on predictions. By combining this information, it gets a better understanding of which features matter most. The researchers tested their method on four public datasets and found it did better than other methods at ranking and classifying data. |
Keywords
» Artificial intelligence » Attention » Classification » Deep learning
