Summary of Enhancing Feature Selection and Interpretability in Ai Regression Tasks Through Feature Attribution, by Alexander Hinterleitner et al.
Enhancing Feature Selection and Interpretability in AI Regression Tasks Through Feature Attribution
by Alexander Hinterleitner, Thomas Bartz-Beielstein, Richard Schulz, Sebastian Spengler, Thomas Winter, Christoph Leitenmeier
First submitted to arxiv on: 25 Sep 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 A novel study in Explainable Artificial Intelligence (XAI) aims to improve the performance and robustness of deep learning algorithms by using feature attribution methods for regression problems. Specifically, the researchers introduce a pipeline that combines Integrated Gradients with k-means clustering to select an optimal set of variables from the input data space, improving accuracy and stability. This approach is validated on a real-world industrial problem – blade vibration analysis in turbo machinery development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial Intelligence (AI) wants to be more transparent! Scientists are working on Explainable AI (XAI) to make AI decisions clear. Usually, XAI helps with security issues, but this study looks at using XAI for deep learning problems that predict numbers (regression). They created a new way to pick the most important features from data by combining two techniques: Integrated Gradients and k-means clustering. This helps predictions be more accurate and stable. The researchers tested it on a real-world problem – analyzing vibrations in turbo machinery. |
Keywords
» Artificial intelligence » Clustering » Deep learning » K means » Regression
