Summary of The Hidden Influence Of Latent Feature Magnitude When Learning with Imbalanced Data, by Damien A. Dablain and Nitesh V. Chawla
The Hidden Influence of Latent Feature Magnitude When Learning with Imbalanced Data
by Damien A. Dablain, Nitesh V. Chawla
First submitted to arxiv on: 14 Jul 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 Machine learning (ML) models struggle to generalize when the training data is numerically imbalanced. This problem has been attributed to the lack of training data for under-represented classes and feature overlap. To address this issue, practitioners often implement data augmentation, assign higher costs to minority class prediction errors, or undersample the prevalent class. However, our research reveals that one of the primary causes of impaired generalization with imbalanced data is the way ML models perform inference. These models rely heavily on the magnitude of encoded signals and predict classes based on a combination of signal magnitudes that sum to the largest scalar. We demonstrate that even with aggressive data augmentation, parametric ML models still associate class labels with limited feature combinations that affect generalization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models have trouble predicting when some groups are much smaller than others. This is because they rely too much on the size of the signals they learn. Our research shows that this problem isn’t just about having enough data for minority groups, but also how the model works during prediction. We found that even with extra tricks to help the model do better with minority groups, it still has trouble generalizing. |
Keywords
» Artificial intelligence » Data augmentation » Generalization » Inference » Machine learning
