Summary of Feature Selection Based on Wasserstein Distance, by Fuwei Li
Feature Selection Based on Wasserstein Distance
by Fuwei Li
First submitted to arxiv on: 11 Nov 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 feature selection method, Wasserstein distance-based feature selection, to improve machine learning performance. Unlike traditional methods relying on correlation or Kullback-Leibler divergence, this approach assesses feature similarity using the Wasserstein distance, which captures class relationships and is robust to noisy labels. The Markov blanket-based algorithm is introduced, demonstrating its effectiveness in reducing the impact of noisy labels without relying on specific noise models. Experimental results across multiple datasets show that this method consistently outperforms traditional methods, particularly in noisy settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research introduces a new way to choose the most important features for machine learning. Instead of using old methods like correlation or something called Kullback-Leibler divergence, they use something called the Wasserstein distance. This helps by capturing how different features are related and being good at handling noisy data. They also developed an algorithm that works well with this new approach. In tests on many datasets, their method did better than old methods, especially when there was a lot of noise. |
Keywords
» Artificial intelligence » Feature selection » Machine learning
