Summary of Conjunction Subspaces Test For Conformal and Selective Classification, by Zengyou He and Zerun Li and Junjie Dong and Xinying Liu and Mudi Jiang and Lianyu Hu
Conjunction Subspaces Test for Conformal and Selective Classification
by Zengyou He, Zerun Li, Junjie Dong, Xinying Liu, Mudi Jiang, Lianyu Hu
First submitted to arxiv on: 16 Oct 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 new classifier is introduced that leverages significance testing results from various random subspaces to derive consensus p-values, quantifying classification uncertainty. This approach can be applied for conformal prediction and selective classification with reject and refine options by thresholding the consensus p-values. Theoretical generalization error bounds are analyzed, and empirical studies on real datasets demonstrate the proposed classifier’s effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to predict if something belongs to a certain group or not. It combines many small tests from different directions to get a better understanding of how sure we can be about our prediction. This helps us make more accurate decisions and avoid making mistakes. The method is useful for deciding what to do when faced with uncertainty, like which options are safe to choose or which predictions are reliable. |
Keywords
» Artificial intelligence » Classification » Generalization
